Why SOLIDWORKS Is Leading the AI Revolution in CAD

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Why SOLIDWORKS Is Leading the AI Revolution in CAD

 SOLIDWORKS and its parent company, Dassault Systems, have been ahead of the competition when it comes to all things AI. SOLIDWORKS started developing AI features, also known as Smart Features, decades ago, giving their software a lead above the competition. While continuing to invest and stay ahead of the pack, all new AI assistants are now directly available within the application, ensuring that integration is seamless.

Follow along in this blog, because I want to show you all the amazing features SOLIDWORKS has already implemented over the year, along with what is in store for the future. By the end, I will have shown how the recent attempts of our competition’s software do not hold a candle to the advances SOLIDWORKS has already made, let alone what is in store for the future.

Past Additions of Machine Learning and Artificial Intelligence

For over a decade, SOLIDWORKS has been continuously adding features that make use of machine learning and artificial intelligence. From features such as Smart Mates or Smart Fasteners to new AI Drawing Creation, SOLIDWORKS has been working to optimize engineer time, and reduce the number of tedious repetitive tasks.

Excelling in time optimization for years, SOLIDWORKS has continued making tools designed with engineering resources in mind. Tools like Fully Defined Sketch and Selection Accelerators have been available for years, helping make the sketching and selection processes faster. Always improving, SOLIDWORKS took the predictive selection accelerator from the Fillet command, and added it into Chamfers in recent years, making seamless group selection even easier than before in both features.

Machine Learning and Artificial Intelligence

Users can go from this underdefined sketch to this fully defined sketch in 3 quick clicks!

Machine Learning and Artificial Intelligence 2

There have even been productivity increasing tools in the assembly environment for just as long! Smart Fasteners and Smart Mates have allowed engineers to snap together parts and fill their holes with fasteners for over a decade. Even before the general public heard about AI and chatbots, SOLIDWORKS has been working to implement AI based features to improve the engineering experience.

Current SOLIDWORKS AI Tool Additions

In 2026, SOLIDWORKS continues this trend of improving the engineering experience through implementing countless new features in the most recent as well as future updates. Some such features include AI Drawing Creation, AI Assembly Creation, Automatic Fastener Recognition, Command Predictor, and Pattern Assistant, to name a few. With these tools, SOLIDWORKS will become even smarter, and can predict an engineer’s next move; whether that move is dropping a nut into place, or needing to add a pattern of bolts in one swift movement. SOLIDWORKS can now even assist engineers in making sure the most efficient patterning methods are being used, as an efficiency check to young engineers.

SOLIDWORKS AI Tool Additions

Tools, like Automatic Fastener Recognition, make use of a database of thousands of fastener files, allowing the SOLIDWORKS AI to determine if a part is a fastener as soon as it is dragged in to your current project. This recognition will allow the system to offer better mate conditions and groupings, for instance pairing a new nut to your existing bolt.

Additionally, features like AI Drawing Creation and AI Assembly Creation take processes out of the engineers hands and begin these processes in the system background before bringing the engineer in for confirmation. From laying out standard views and annotations, to organizing folder structures in assemblies, SOLIDWORKS continues to assist in simplifying and standardizing these initial steps in creation and documentation.

SOLIDWORKS AI Tool Additions

With the use of SOLIDWORKS AI Drawing Creation, a simple conversation with LEO about the desired settings and defaults leads to a drawing created faster than ever before!

SOLIDWORKS AI Tool Additions

Addition of AI assistants in SOLIDWORKS

SOLIDWORKS AI Assistants

The most recent additions of artificial intelligence to SOLIDWORKS include the three all new AI assistants; AURA, LEO, and MARIE. Each serves a unique role throughout the CAD Design process, as described below.

AURA is the starting point of any great project, even before you draw your first sketch. AURA holds the ability to leverage knowledge from both web and enterprise sources, making it your one stop shop for rapid confirmation. For questions regarding basic design rules and suggestions, or even searching your company’s knowledge base, AURA can answer it all.

After the first steps with AURA are completed, LEO takes the reins. LEO can help users effectively solve many complications through the design process, helping validate your design and optimize your processes. Throughout both mechanical design, as well as simulation, LEO can take your prompts to generate assembly structures, as parametric features, run studies, and even help resolve design errors. For both answering questions, and offering solutions, LEO can solve many engineering headaches.

The last assistant in the lineup is MARIE, your scientific research specialist. With expertise in materials science, chemistry and more, your thorough scientific research can be simplified. With this third member of the SOLIDWORKS AI trifecta, you have an assistant in your corner for every part of the engineering design process.

Competitors attempts at replication

Outside of SOLIDWORKS, many competitors have tried their hand in implementing AI for the benefit of users. While many companies have had good feature additions in recent years, it is hard to compare them to the decades of experience and additions seen in SOLIDWORKS. The following sections detail some of these features within the competing software, and shows how SOLIDWORKS has taken the lead in all things AI.

For starters, Autodesk has invested in AI in Fusion 360. However, you will find no such features in Inventor. Looking into these, features like CAM hole recognition have existed in SOLIDWORKS for some time. The drawing AI tool seems to be in the early stages, having very little interaction or flexibility. Fusion can add relationships and dimensions automatically, much like Fully Define Sketch (something that has existed in SOLIDWORKS for nearly 20 years). The main hurdle that Autodesk will have to overcome is that their files don’t talk to each other, unlike the fully associative files found in SOLIDWORKS, making their AI feature development harder.

Other competitors like Siemens have three main enhancements, Magnetic Snap, Automated Drawings, and a design copilot, all things that have existed or do now exist in SOLIDWORKS. Lastly, Onshape has a lot of potential due to their cloud-based nature, however the content released as of now is just in the infancy stage.

The Bottom Line: SOLIDWORKS AI is Changing the Game

After looking at the history of feature development, as well as a brief look at the competition, you can see that SOLIDWORKS continues to be designed with the engineer in mind. From features that increase productivity by decreasing repetition, to tools that give you a head start in the design process, SOLIDWORKS is a lifesaver. Many competitors’ Artificial Intelligence ambitions are just beginning, so SOLIDWORKS is working hard to maintain the lead they already have, while pushing engineering design technology to the next level. Our SOLIDWORKS Technical Team has been ahead of the pack when it comes to learning and using AI, so please contact us with any questions, and find out what makes us the Solidxperts.


Alain

Alain Provost

Senior Technical Sales Executive

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    Resolving issues with part name display in eDrawings compared to SOLIDWORKS

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    Resolving issues with part name display in eDrawings compared to SOLIDWORKS

    It is quite common for information shared with the workshop through eDrawings not to appear exactly as expected. In fact, part names may seem incorrect, incomplete, or simply different from what the engineering team sees in SOLIDWORKS.

    As a result, this is a question we are regularly asked: “Why are part names not the same in eDrawings as in the SOLIDWORKS assembly?”

    So, if you use eDrawings as a viewer for shop floor personnel, this article is for you. Let’s take a few minutes to understand why this happens and more importantly, how to fix it in a sustainable way.

    The Typical Context: eDrawings as a Workshop Support Tool

    In many manufacturing companies, eDrawings is used to:

    • View assemblies without a SOLIDWORKS license;

    • Visualize complete machines on the shop floor;

    • Quickly identify parts to manufacture or assemble;

    • Reduce paper drawings.

    It is an excellent tool as long as the displayed information is clear and consistent.

    However, in some cases, workshop users are faced with:

    • Cryptic file names;

    • Internal references that are not meaningful;

    • Part names different from those used by engineering.

    A Key Point to Understand: eDrawings Does Not Interpret, It Displays

    First of all, it is important thing to clarify: eDrawings does not “guess” anything. It simply displays the information coming from SOLIDWORKS, based on the assembly structure, the properties defined on each part, and the export options used. Therefore, if the display does not meet your expectations, it is almost never an eDrawings bug, but rather a source data or configuration issue.

    The Three Most Common Causes

    In practice, three main causes explain this behavior:

    1. The displayed name is the file name, not the business designation

    By default, eDrawings often displays the part file name (.SLDPRT) instead of:

    • The business designation;

    • The part number;

    • The workshop-oriented description.

    Example: PLT_4587_V3.SLDPRT instead of Conveyor support plate – 10 mm steel

    For the shop floor, the added value is… very limited.

    2. Custom properties are not being leveraged

    Additionally, in SOLIDWORKS, you most likely already have:

    • Description

    • Part Number

    • Internal reference

    • Customer name

    But if these properties are not filled in consistently or eDrawings is not configured to display them, they become useless for the workshop.

     

    3. The eDrawings export process is not standardized

    Finally, an export performed quickly, by different users and without a clear procedure often results in:

    • inconsistent displays;

    • different habits from one project to another.

    As a result, the workshop gradually loses confidence in the tool.

    Recommended Best Practice: Think “Workshop” Directly in SOLIDWORKS

    In reality, the solution is not in eDrawings…it starts in SOLIDWORKS.

    Here is a simple and effective approach:

    Use a workshop-oriented property

    For example:

    • Description

    • or Workshop_Description

    This property should be clear, readable and free of unnecessary CAD jargon.

    Standardize how properties are filled in

    Apply the same logic to all parts:

    • same property name

    • same text convention

    • same language

    Ultimately, this is a small effort on the engineering side…but delivers significant gains on the production side.

     

    Structuring the eDrawings Export for the Workshop

    To ensure consistency, the eDrawings export should:

    • always come from an up-to-date assembly;

    • follow a simple, documented procedure;

    • display useful information, not technical noise.

    This is exactly why a short internal procedure is often an excellent idea.

    eDrawings: An Excellent Tool, When Properly Prepared

    eDrawings is neither a design tool nor a PDM system. It is a technical communication tool.

    In other words, like any communication, quality depends on what is sent, not only on the tool itself.

    As a result, when best practices are in place the workshop gains autonomy, the unnecessary questions decrease, and the interpretation errors are reduced.

    From Confusion to Clarity: Making eDrawings Work for the Workshop

    If part names displayed in eDrawings do not match what you expect, know that you are not alone, it is not inevitable, and it is almost never a bug. More often than not, it is an opportunity to review how information is prepared and transferred to the workshop.

    Very often…a few simple adjustments are enough to turn eDrawings into a true production support tool.

    FAQ

    Why do part names in eDrawings differ from those in SOLIDWORKS?

    eDrawings displays information coming from SOLIDWORKS files, typically the file name or custom properties. If these data are not standardized or workshop-oriented, the display may appear inconsistent.

    Is this an eDrawings bug or limitation?

    No. In most cases, the issue lies in how data is structured upstream in SOLIDWORKS, not in eDrawings itself.

    What is the best practice to display clear part names on the shop floor?

    Use a dedicated, readable SOLIDWORKS property such as Description or Workshop_Description, filled consistently across all parts.

    Is a SOLIDWORKS license required on the shop floor?

    No. eDrawings allows assembly viewing without a SOLIDWORKS license, making it a cost-effective solution for workshop use.

    What is the tangible benefit for the company?

    A clear and standardized eDrawings display helps to:

    • reduce interruptions between engineering and production

    • limit interpretation errors

    • improve overall operational efficiency


    Alain

    Alain Provost

    Senior Technical Sales Executive

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    Whether you’re ready to get started or just have a few more questions, you can contact us toll-free:

      Guide: Getting Started with AI in SOLIDWORKS

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      Guide: Getting Started with AI in SOLIDWORKS

      Artificial Intelligence is quickly becoming part of everyday engineering workflows, but if you’re a SOLIDWORKS user, the big question is usually:

      “Where do I even start?”

      The good news is that AI in SOLIDWORKS isn’t something separate you need to learn from scratch. It’s already being integrated into the tools you use every day through the 3DEXPERIENCE platform.

      In this guide, we’ll walk through everything you need to get started, step by step:

      • Required software and prerequisites

      • Activating the 3DEXPERIENCE platform

      • Installing the Design with SOLIDWORKS connector

      • Accessing AI tools like the new AI Labs tab

      No fluff, just what you need to get up and running.

      Step 1: Understand What “AI in SOLIDWORKS” Actually Means

      Before jumping into setup, it’s important to clarify something:

      AI in SOLIDWORKS isn’t a single feature. It’s a set of capabilities delivered through the 3DEXPERIENCE platform.

      Today, that includes things like:

      • Design assistance and recommendations

      • Automation of repetitive tasks

      • Data-driven insights

      • Early access tools in AI Labs

      In other words, AI is layered into your workflow, not replacing it.

      Step 2: Confirm Your Prerequisites

      Before you can access any AI-driven tools, you’ll need a few key components in place.

      Required Software

      • SOLIDWORKS 2026 (or newer)

      • Active subscription (required for cloud services integration)

      Platform Access

      • A 3DEXPERIENCE platform account

      • Assigned roles (including Collaborative Designer for SOLIDWORKS)

      System Requirements

      • Stable internet connection

      • Admin rights for installation

      • Browser access to the platform

      If you’re missing any of these, that’s your starting point.

      Step 3: Activate the 3DEXPERIENCE Platform

      AI functionality depends on your connection to the 3DEXPERIENCE platform.

      How to Activate:

      • Check your welcome email from Dassault Systèmes
      • Click the activation link
      • Set your password and log in
      • Access your platform dashboard

      Once inside, you should see your roles and available apps.

      Still confused? Follow our Getting Started guide:
      Getting Started with the 3DEXPERIENCE Platform

      Step 4: Install the 3DEXPERIENCE Launcher

      Before installing any apps, you’ll need the 3DEXPERIENCE Launcher.

      Steps:

      • Log into your 3DEXPERIENCE platform
      • Navigate to the Compass (top-left menu)
      • Scroll down to My Apps and locate Design with SOLIDWORKS.
      • Select the app to begin the installation.
      • Click Install Launcher when prompted
      • Run the installer

      This tool acts as the bridge between your browser and desktop applications.

      Step 5: Install “Design with SOLIDWORKS”

      This is the most important step.

      The Design with SOLIDWORKS connector is what links your desktop SOLIDWORKS environment to the platform, and enables AI-driven features.

      Installation Steps:

      • In the platform, search for Design with SOLIDWORKS
      • Click Install
      • Accept default settings (recommended)
      • Complete installation
      • Restart your machine if prompted

      Once installed, your environment is officially “connected.”

      Having trouble? Check out our installation guide:
      Connect SOLIDWORKS Desktop to the 3DEXPERIENCE Platform

      Step 6: Launch SOLIDWORKS from the Platform

      This step is often missed, however, it is absolutely critical.

      First Launch:

      • Go to the platform
      • Click Open on Design with SOLIDWORKS
      • Launch SOLIDWORKS from the browser

      Why this matters:

      This ensures:

      • Your session is authenticated
      • The connector is active
      • Cloud services are initialized

      If you launch SOLIDWORKS directly from your desktop first, you may not be connected properly.

      Step 7: Verify the 3DEXPERIENCE Add-in

      Once SOLIDWORKS opens, confirm everything is working.

      Check:

      • A 3DEXPERIENCE tab appears in the task pane
      • Add-in is enabled under:
        Tools > Add-ins

      If it’s not active:

      • Enable it manually
      • Restart SOLIDWORKS if needed

      This confirms your system is fully connected.

      Step 8: Access the AI Labs Tab

      Now we get to the interesting part.

      With everything configured, you should have access to AI Labs, where new AI-driven tools are introduced.

      Where to Find It:

      • Inside SOLIDWORKS (Task Pane)
      • Look for AI Labs tab

      What You’ll Find:

      • Experimental AI features
      • Early access tools
      • Workflow enhancements powered by AI

      These features evolve quickly, so expect changes over time.

      Step 9: Start Using AI Features (Practical Examples)

      Once inside AI Labs or connected tools, start small.

      Good First Use Cases:

      • Automating repetitive design steps
      • Getting design suggestions
      • Exploring data-driven insights

      What Not to Expect:

      • Fully automated design generation
      • “One-click engineering”

      AI is there to assist, not replace your expertise.

      Step 10: Best Practices for Getting Started

      This is where most teams succeed or struggle.

      ✔ Start Small

      Don’t try to overhaul your entire workflow.

      ✔ Focus on Real Problems

      Look for:

      • Repetitive tasks
      • Bottlenecks
      • Manual processes

      ✔ Validate Everything

      AI suggestions still require engineering judgment.

      ✔ Train Your Team Gradually

      Adoption works best when it’s incremental.

      Final Thoughts: Where AI in SOLIDWORKS Is Headed

      AI in SOLIDWORKS is evolving, but the direction is clear:

      • More automation of low-value tasks
      • Better decision support
      • Deeper integration with simulation and data

      And importantly:

      SOLIDWORKS isn’t being replaced, it’s being enhanced.

      For most teams, the real opportunity isn’t jumping ahead, it’s simply getting started.

      For more information on AI in SOLIDWORKS, reach out to us through our website:
      SOLIDWORKS AI: Transform Your Design with Artificial Intelligence


      Michael Habrich

      3DEXPERIENCE Specialist

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        Connecting SOLIDWORKS Desktop to the 3DEXPERIENCE Platform

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        Connecting SOLIDWORKS Desktop to the 3DEXPERIENCE Platform

        The 3DEXPERIENCE platform includes a wide range of powerful, web-based apps, but many teams prefer to continue designing in the familiar SOLIDWORKS desktop environment. The good news? You don’t have to choose one or the other.

        By combining SOLIDWORKS desktop with the Design with SOLIDWORKS connector, you can keep your existing workflows and interface while taking full advantage of cloud-based file storage, sharing, and collaboration.

        In this article, we’ll walk through:

        • Installing the Design with SOLIDWORKS connector

        • Launching SOLIDWORKS with the 3DEXPERIENCE connection enabled

        • Saving files directly to the platform

        • Managing your local cache for best performance

        Installing Design with SOLIDWORKS

        First, once your 3DEXPERIENCE tenant is activated, or you’ve been invited to an existing one , linking SOLIDWORKS desktop to the platform is quick and straightforward.

        • In the 3DEXPERIENCE interface, click the Compass icon in the upper-left corner.

        • Scroll down to My Apps and locate Design with SOLIDWORKS.

        • Select the app to begin the installation.

        Installing Design with SOLIDWORKS

        During installation, you’ll be prompted to:

        • Install all granted roles, or

        • Install only the roles required for the Design with SOLIDWORKS connector

        Installing Design with SOLIDWORKS

        The installer will then allow you to choose:

        • The installation directory

        • The location of your 3DEXPERIENCE cache

        By default, the cache is stored in C:\3DEXPERIENCE. Since the cache is managed directly from within SOLIDWORKS, you typically won’t need to access this folder manually.

        The cache is stored in C:\3DEXPERIENCE

        Once installation is complete, the connector is added to your system.

        Enabling the 3DEXPERIENCE Add-In in SOLIDWORKS

        Before using the connector, take a moment to confirm the 3DEXPERIENCE add-in is enabled in SOLIDWORKS.

        • Launch SOLIDWORKS.

        • Go to Settings > Add-Ins.

        • Verify that the 3DEXPERIENCE add-in is installed and checked.

        Enabling the 3DEXPERIENCE Add-In in SOLIDWORKS

        This ensures SOLIDWORKS can communicate properly with the platform.

        Launching SOLIDWORKS with the Connector

        One important workflow change to be aware of is how you launch SOLIDWORKS.

        • Launching SOLIDWORKS from a desktop shortcut or system search opens the standard desktop version without the 3DEXPERIENCE connection.

        • To use the connector, launch Design with SOLIDWORKS instead.

        This starts SOLIDWORKS with full 3DEXPERIENCE functionality enabled.

        You can also:

        • Use the dropdown next to Design with SOLIDWORKS to check for updates or uninstall

        • Create a dedicated desktop shortcut for Design with SOLIDWORKS, allowing you to access cloud functionality without opening a web browser

        Launching SOLIDWORKS with the Connector

        Saving Files to the 3DEXPERIENCE Platform

        Once connected, saving files to the cloud is seamless.

        You can:

        • Use Save to 3DEXPERIENCE from the File menu (alongside Save and Save As), or

        • Use the 3DEXPERIENCE Task Pane, added by the add-in

        The task pane lets you:

        • Browse your tenant

        • Search for existing data

        • Right-click and save files directly to the platform

        And if needed, you can still save files locally, the connector doesn’t force you into a cloud-only workflow.

        Saving Files to the 3DEXPERIENCE Platform

        Managing the 3DEXPERIENCE Cache

        When you open or edit files stored on the platform, they’re downloaded locally to your 3DEXPERIENCE cache. Keeping this cache clean can significantly improve performance.

        The 3DEXPERIENCE add-in makes cache management easy:

        • Delete individual cached files

        • Use the cleanup tool to remove files older than a specified date

        The cleanup utility is smart. It automatically skips:

        • Files referenced by assemblies

        • Files not yet saved to the platform

        • Files that are currently locked

        This helps you clear space without risking your data.

        Saving Files to the 3DEXPERIENCE Platform

        Final Thoughts

        The Design with SOLIDWORKS connector bridges the gap between SOLIDWORKS desktop and the 3DEXPERIENCE platform, giving you the best of both worlds. You get cloud-based collaboration and data management without changing how you design.

        If you need help installing the connector, optimizing your workflow, or rolling this out to your team, your Solidxperts team is here to help.

        Looking to learn more?

        • Explore additional articles and tutorials

        • Connect with other users and experts

        • Or reach out to us! We’re always happy to help you get the most out of your tools


        Michael Habrich

        3DEXPERIENCE Specialist

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          7 Myths About AI: Demystifying Bias and Technological Limits

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          7 Myths About AI: Demystifying Bias and Technological Limits

          Every wave of innovation in artificial intelligence (AI) brings real technological progress, along with a dramatic rise in hype. With every breakthrough, new narratives emerge: AI is portrayed as “magical,” endowed with its own will, on the verge of becoming superhuman, or conversely as something completely uncontrollable by law.

          As a result, this fog of myths makes AI opaque to the public, complicates decision-making for organizations, and distracts attention from the real technical and societal challenges.

          In this article, we aim to clarify two key questions:

          • What are the main myths currently surrounding AI?

          • And what technical, physical, and social realities help dismantle them?

          The Major Myths Shaping Our View of AI

          Several myths structure today’s collective imagination about artificial intelligence.

          “AI has agency.”
          The idea that AI systems act on their own initiative, with intentions, goals, or desires.

          “Superintelligence is imminent.”
          The belief that we are only a few years, or even months, away from a general intelligence far surpassing human capabilities.

          “AI can be objective or impartial.”
          The assumption that algorithms are inherently neutral because they rely on computation.

          “AI has a clear definition.”
          As if AI referred to a single, clearly defined technology, when in reality no universal definition exists.

          “Ethical guidelines are enough to protect us.”
          The perception that voluntary ethical charters are sufficient safeguards against harmful AI uses.

          “AI cannot be regulated.”
          The claim that technological innovation moves too fast for legal systems to keep up.

          “AI can solve any problem.”
          The idea that AI is a universal solution applicable to any technical, economic, or social challenge.

          In reality, these myths stem from a mixture of marketing, science fiction, and technical misunderstanding. To move beyond them, we need to return to what AI actually is today.

          1. Agency and Consciousness: AI as a “Stochastic Parrot”

          One of the most common misconceptions is attributing intention to AI. We often talk about what AI “wants,” “decides,” or “thinks.” Yet modern systems, especially large language models (LLMs), function much more simply.

          Models That Predict, Not Understand

          An LLM does not interpret your sentences in the human sense. Technically, it:

          • receives a sequence of tokens (pieces of words) as input

          • computes a probability distribution over the next token using a trained neural network

          • selects or samples the next token according to this distribution

          • repeats the process until a complete response is produced

          This mechanism relies on massive statistical correlations learned during training. At no point does the system possess:

          • semantic understanding of concepts

          • an internal model of the world comparable to a human’s

          • independent intentions or goals

          In other words, what researchers sometimes call a “stochastic parrot”: a machine that reproduces learned language structures in sophisticated probabilistic combinations.

          Anthropomorphism as a Persistent Bias

          If these systems appear to “think,” it is largely because humans naturally anthropomorphize systems that display seemingly intelligent behavior. This cognitive bias is central to many misunderstandings about AI today.

          2. Superintelligence and the Resource Wall

          Another dominant narrative suggests that we are on the verge of general superintelligence, held back only by corporate caution. However, the actual infrastructure behind AI tells a different story.

          The Data Wall: A Finite Resource

          Today’s large models rely on enormous volumes of high-quality human-generated data: text, conversations, code, and multimedia content. But this resource is not infinite.

          Estimates suggest that high-quality training data suitable for ever-larger models could be largely exhausted between 2026 and 2032. Beyond that point:

          • existing datasets would be reused repeatedly, yielding limited improvements

          • or synthetic data would be used, introducing new risks and feedback loops

          Physical Constraints and Diminishing Returns

          The idea of unlimited growth in model power faces several practical limits.

          Energy and cooling constraints
          The computing density required for training and deploying the largest models pushes data centers toward limits in:

          • electrical grid capacity

          • cooling infrastructure needed to dissipate heat

          Hardware limits
          GPUs and other accelerators are approaching physical limits in terms of performance per watt and cost efficiency.

          Diminishing returns
          Scaling models by increasing parameters, data, or compute still improves performance, but each additional gain becomes smaller relative to the resources invested.

          These “resource walls” do not prevent progress, but they challenge the idea of a straightforward path toward limitless superintelligence.

          3. Objectivity and Impartiality: AI as a Mirror of Human Bias

          AI is often presented as a way to eliminate human bias. In reality, AI systems frequently inherit and sometimes amplify existing inequalities.

          Data Bias: Who Is Represented?

          Models can only generalize effectively if training data represent a sufficiently diverse set of situations and populations.

          When datasets are imbalanced, performance degrades unevenly. Studies have shown, for instance, that some facial recognition systems exhibit error rates up to 35% higher for darker-skinned women than for white men.

          This is not an isolated bug. It reflects underlying representation biases in the data.

          Design Bias: Optimization Choices Matter

          Even with balanced datasets, models reflect the priorities of their designers:

          • How is overall accuracy balanced against fairness between groups?

          • Which metrics are optimized during training and deployment?

          • What trade-offs are accepted between false positives and false negatives?

          These decisions directly shape who benefits from an AI system and who may be harmed. Claims of algorithmic objectivity often overlook these design choices.

          4. The Plural Architecture of AI

          Contrary to popular belief, “artificial intelligence” does not describe a single unified technology. Instead, it is an umbrella term covering a broad and heterogeneous set of methods, theories, and applications.

          A Hierarchy of Often-Confused Concepts

          Many people use AI, Machine Learning, and Deep Learning interchangeably, although they represent different levels of abstraction.

          Artificial Intelligence (AI)
          The broader field of computer science focused on creating systems capable of performing tasks that require human-like cognitive abilities.

          Machine Learning (ML)
          A subset of AI in which systems learn patterns from data rather than relying solely on explicit programming.

          Deep Learning (DL)
          A specialized ML approach using multi-layer neural networks to process complex data such as images, speech, or language.

          Divergent Definitions

          The meaning of AI changes depending on perspective.

          • Scientific definition: a research discipline exploring computational models of cognition.

          • Technological definition: systems capable of perceiving their environment and taking actions accordingly.

          • Popular definition: a largely anthropomorphic vision attributing awareness or autonomy to machines.

          A Fragmented Ecosystem

          AI is not monolithic. It includes multiple research traditions and technical approaches.

          Two historical families illustrate this diversity:

          Symbolic AI
          Systems based on logical rules and expert knowledge.

          Connectionist AI
          Statistical approaches based on large datasets and neural networks, including modern language models.

          Narrow AI vs General AI

          Today’s systems belong entirely to narrow AI, designed to perform specific tasks such as:

          • playing chess

          • recognizing objects in images

          • detecting fraud

          • generating text

          Artificial General Intelligence (AGI), capable of learning any intellectual task a human can perform, remains a speculative concept.

          5. Ethics, Marketing, and the Need for Regulation

          In response to AI risks, many organizations have adopted ethical charters and voluntary guidelines. While useful, these tools have clear limitations.

          Ethical Marketing

          Without enforcement mechanisms, many ethical charters function more as reputation tools:

          • they reassure stakeholders

          • they improve brand image

          • but they rarely prevent high-risk systems from being deployed

          Toward Enforceable Regulation: The EU AI Act

          Contrary to the myth that AI cannot be governed, regulatory frameworks are emerging.

          The European Union’s AI Act proposes a risk-based approach:

          • Unacceptable risk systems are banned

          • High-risk systems must comply with strict requirements including transparency, traceability, documentation, conformity assessments, and human oversight

          • Minimal risk systems face limited regulation

          The goal is not to slow innovation, but to ensure that AI systems remain accountable within existing legal frameworks.

          6. AI Is Not a Magic Wand

          Perhaps the most persistent myth is that AI can solve any problem.

          In reality, successful AI systems are:

          • specialized, designed for specific tasks such as image recognition, text summarization, fraud detection, or code generation

          • limited in common sense, often failing when faced with situations outside their training distribution

          • highly context-dependent, relying on data quality, system integration, and human oversight

          The same model may perform extremely well in a well-defined environment yet fail dramatically when conditions change or when real-world usage diverges from intended scenarios.

          AI as a Component, Not a Strategy

          For organizations, AI should be viewed as:

          • a technical component within a larger system architecture

          • integrated into a broader strategy involving governance, metrics, risk management, and human supervision

          The wrong question is:

          “How can we add AI everywhere?”

          The better question is:

          “On which well-defined problems does AI provide a real advantage compared to existing solutions?”

          Moving Beyond the Myths

          Today’s AI is neither a conscious entity, nor an imminent superintelligence, nor a universal solution.

          It is a set of powerful techniques deeply grounded in real-world constraints. These systems are limited by physical infrastructure such as energy, cooling, and hardware, as well as by the availability of data and computational resources. They are also shaped by the social structures and human biases embedded in the data and objectives guiding their development.

          By dismantling the myths surrounding AI, autonomous agency, imminent superintelligence, perfect objectivity, legal ungovernability, or universal applicability, we can ask better technical questions, design safer systems, and build more effective regulatory frameworks.

          Ultimately, understanding these realities allows us to treat AI for what it truly is: a powerful but specialized tool that must be used with rigor, transparency, and human oversight.

          If you have questions about AI and its practical applications, our experts are here to help. Contact us to start the conversation.


          Benoit Bilodeau

          Senior Solutions Architect

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            SWOOD and Material Management: From Design to Wood Manufacturing

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            SWOOD and Material Management: From Design to Wood Manufacturing

            Material is More Than Just a Visual Appearance

            In the furniture, cabinetry, and commercial millwork industries, material selection plays a critical role. It impacts not only product aesthetics, but also manufacturability, cost control, quality, and production repeatability. Yet, in many organizations, material management is still treated as a secondary concern, often limited to a visual texture or a late-stage production note.

            As a result, this approach frequently leads to well-known issues. Designers and production teams may face inconsistencies between design and the shop floor, incorrect panel selection, edge banding errors, material waste, and costly rework. In addition, standardizing internal processes becomes much more difficult.

            At a time when companies are striving to improve operational efficiency and production reliability, these issues can quickly turn into costly bottlenecks.

            This is where the combination of SOLIDWORKS and SWOOD makes a real difference. By integrating intelligent material management directly into the design phase, SWOOD transforms materials into structured, manufacturing-ready data. As a result, this information remains consistent throughout the entire digital workflow.

            The Limitations of Material Management in SOLIDWORKS

            SOLIDWORKS is a powerful and flexible CAD platform, widely recognized for its robustness and parametric capabilities. In addition, it offers advanced material handling for mechanical design, including physical properties, mass calculations, and rendering. However, when applied to wood-based design, certain limitations quickly emerge.

            In fact, native SOLIDWORKS materials are primarily intended for mechanical applications. As a result, they do not fully address the realities of wood manufacturing, such as:

            • engineered wood panels,

            • commercial panel thicknesses,

            • wood grain direction,

            • supplier-specific decors,

            • edge banding compatibility,

            • or CNC manufacturing constraints.

            As a result, designers often rely on generic materials and manual adjustments. This information remains disconnected from manufacturing processes, forcing production teams to reinterpret design intent. The lack of continuity increases error risks and severely limits automation.

            Why Material Management Is Critical in Wood Design?

            In wood design, materials are never neutral. A panel is not simply a thickness and a color. Instead, it represents a supplier, a finish, compatible edge banding, machining rules, and cost implications.

            Without proper material definition, several issues can arise. For example, poor material management can lead to:

            • incorrect panel usage in production,

            • edge banding mismatches,

            • nesting inefficiencies,

            • inaccurate material cost estimates,

            • and inconsistencies across similar projects.

            On the other hand, structured material management allows companies to:

            • ensure design-to-production consistency,

            • reduce manual data entry,

            • improve communication between departments,

            • and secure manufacturing outcomes early in the design process.

            In this context, materials become a strategic data asset, just as critical as dimensions or tolerances.

            How SWOOD Structures Material Management?

            Material Libraries Designed for the Wood Industry

            SWOOD introduces material libraries specifically developed for cabinetry, furniture, and millwork professionals. Unlike generic CAD materials, these libraries are designed to reflect real manufacturing requirements. As a result, SWOOD materials include production-relevant parameters such as:

            • actual panel thickness,

            • material type (MDF, melamine, plywood, solid wood, etc.),

            • grain direction,

            • tolerances,

            • and attributes required for bills of materials and cut lists.

            These libraries can be standardized company-wide, ensuring consistent practices across all projects and designers.

            Direct Link Between Materials and CNC Manufacturing

            One of SWOOD’s key strengths is the direct connection between materials and manufacturing processes. Because of this, materials are no longer used only for visualization. Instead, they actively drive CNC machining behavior.

            Based on the selected material, SWOOD can:

            • adapt machining strategies,

            • select appropriate tools,

            • control cutting depths,

            • and automatically prepare data for production.

            This significantly reduces manual adjustments on the shop floor and improves manufacturing reliability, even for highly customized projects.

                      

            Edge Banding and Decor Management

            Edge banding is a critical aspect of wood manufacturing. SWOOD enables intelligent associations between panels and compatible edge banding materials.

            Decors are not used solely for visualization. They are also embedded into:

            • bills of materials,

            • cut lists,

            • nesting data,

            • and shop floor documentation.

            By automating these relationships, SWOOD minimizes human error and ensures consistent data from design through production.

            From Design to Manufacturing: A Controlled Digital Continuity

            SWOOD is built around the concept of digital continuity. Data defined during design is the same data used for manufacturing, without re-entry or reinterpretation.

            A typical workflow includes:

            1. Designing furniture or millwork in SOLIDWORKS with SWOOD Design.

            2. Applying structured, manufacturing-ready materials.

            3. Transferring data directly to SWOOD CAM and SWOOD Nesting.

            4. CNC production driven by consistent and reliable information.

            This approach improves traceability, reduces lead times, and increases overall production confidence.

            The Impact on Costs and Industrial Performance

            Effective material management directly impacts business performance. By integrating materials early in the design phase, companies can:

            • improve material cost estimation accuracy,

            • reduce waste and scrap,

            • optimize panel nesting,

            • standardize internal workflows,

            • and accelerate onboarding of new employees.

            These benefits are especially valuable for growing organizations that need scalable and repeatable processes.

            Which Companies Benefit Most from SWOOD Material Management?

            SWOOD material management is particularly valuable for:

            • furniture manufacturers,

            • commercial millwork companies,

            • industrial cabinet makers,

            • CNC woodworking shops,

            • and organizations seeking to structure or automate their design-to-production workflows.

            Regardless of company size, this approach increases reliability, productivity, and competitiveness.

            Why SWOOD Is the Best Solution for Wood Design in SOLIDWORKS

            SWOOD does not replace SOLIDWORKS, it enhances it. It adds a critical industry-specific layer tailored to wood manufacturing requirements. By combining SOLIDWORKS’ parametric power with SWOOD’s manufacturing intelligence, companies gain a coherent, scalable, and production-oriented environment.

            This integration unlocks the full potential of the digital manufacturing chain, from design through CNC production.

            Material as a Core Element of the Digital Wood Workflow

            In modern wood manufacturing, materials can no longer be treated as simple visual properties. Instead, they must be managed as essential design and manufacturing data that supports the entire production process.

            When material management is structured properly, companies gain much better control over their operations. With SWOOD, wood manufacturers can reduce errors, better control material costs, and improve overall production reliability.

            Ultimately, integrating materials early in the design phase helps create a more consistent and efficient workflow from design to manufacturing.

            Looking to improve your material management and secure your digital workflow from design to production? Solidxperts helps wood manufacturing companies implement SWOOD, train their teams, and optimize their design-to-production processes.

            FAQ

            What are the financial benefits of materials management with SWOOD?

            Materials management with SWOOD reduces manufacturing errors, rework, and material waste. By standardizing materials from the design stage, companies improve the accuracy of cost estimates, optimize nesting, and reduce scrap, generating a measurable return on investment from the very first projects.

            How does SWOOD contribute to reducing production errors?

            SWOOD eliminates information gaps between the design office and the shop floor. Materials defined during the design phase are used directly in CNC manufacturing, without re-entry. This digital continuity significantly reduces errors related to incorrect panels, incompatible edges, or incorrect machining parameters.

            Does SWOOD improve the productivity of the design office?

            Yes. By using standardized material libraries, designers spend less time checking or correcting material information. Projects are faster to design, more consistent, and easier to reuse, improving overall engineering productivity.

            What is the impact of SWOOD on time to market?

            By reducing manual approvals and last-minute adjustments, SWOOD accelerates the transition from design to manufacturing. With reliable data from the design stage, time to market is shortened and bottlenecks between departments are reduced.

            Does managing materials with SWOOD facilitate company growth?

            Yes. SWOOD helps structure internal processes, which is essential for supporting growth. Standardized practices, reduced reliance on key experts, and faster onboarding of new employees allow the company to grow without a proportional increase in operational risks.

            How can the ROI be concretely measured after implementing SWOOD?

            ROI can be measured through several indicators: reduced scrap, shorter design time, fewer production errors, improved panel utilization, and shorter delivery times. These indicators are easily observable before and after implementation.

            Is SWOOD profitable for a wood industry SME?

            SWOOD is particularly well-suited to SMEs. The gains from reduced errors, optimized material usage, and improved productivity quickly offset the initial investment. Many SMEs see a return on investment within a few months, especially when producing diverse or custom projects.

            Does SWOOD help secure internal knowledge and standards?

            Yes. SWOOD’s material libraries and design rules allow for the formalization of company standards. This reduces reliance on individual knowledge and safeguards expertise, even in the event of staff turnover.


            Alain

            Alain Provost

            Senior Technical Sales Executive

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              Artificial Intelligence in Engineering: Automation Without Losing the Human Touch

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              Artificial Intelligence in Engineering: Automation Without Losing the Human Touch

              Artificial intelligence (AI) is playing an increasingly important role in engineering processes, particularly when it comes to automating repetitive tasks and accelerating the production of technical documentation. However, its role remains fundamentally complementary to that of engineers. Creativity, domain expertise, and decision-making responsibility remain human.

              In this article, we explore:

              • what AI concretely brings to engineering

              • which tasks remain (and will remain) human

              • how to organize an effective human–machine collaboration

              • and what this means for the engineering profession

              1. What AI concretely brings to engineering

              1.1 Automating repetitive, low-value tasks

              The daily work of engineering teams is filled with essential but repetitive tasks that consume a great deal of time without fully leveraging engineers’ expertise. This is precisely where AI excels.

              A typical example is generating technical drawings from 3D models.

              Traditionally, producing technical drawings involves:

              • manually creating the different views (front, section, detail views)

              • applying dimensioning and tolerancing standards

              • reusing elements from previous projects, often manually

              • performing successive checks for consistency and compliance

              With AI, a large portion of this work can be:

              • automated: generating technical drawings directly from 3D designs

              • contextualized: taking into account company history, internal standards, and previously validated models

              The result: fewer repetitive clicks and more time for analysis and improvement.

              1.2 Measurable efficiency gains

              The operational impact is far from marginal.

              Where dozens of people were previously needed to produce, adjust, and verify detailed drawings, organizations can now concentrate human work within a smaller team of reviewers responsible for:

              • correcting the remaining inconsistencies

              • validating compliance

              • managing special cases not covered by the models

              AI handles the repetitive heavy lifting. Humans focus on quality, reliability, and exception management.

              2. Tasks that remain (and will remain) human

              Despite these gains, certain activities remain difficult to automate and may remain so in the short and medium term.

              2.1 Creative design and early project phases

              The early stages of a project, when the architecture of a product and the major technical choices are defined, rely on:

              • creativity

              • accumulated domain expertise

              • the ability to integrate sometimes ambiguous constraints (real-world usage, environment, maintenance, ergonomics)

              • complex decision-making that affects overall product performance

              These activities require systemic understanding, multi-criteria trade-offs, and a form of intuition that current AI models cannot replicate.

              2.2 Safety, compliance, and responsibility

              A clear example is the design of powerful machinery.

              Engineers must:

              • integrate safety factors to protect users

              • sometimes introduce additional margins based on experience or real-world conditions that are difficult to simulate

              These decisions directly affect safety, regulatory compliance, and legal responsibility.

              Today, these types of decisions cannot be delegated to AI.
              Decision-making responsibility remains with humans, not algorithms.

              3. Toward intelligent human–machine collaboration

              The key question is therefore not whether AI will replace engineers, but how to organize an effective collaboration between the two.

              3.1 AI as a copilot during design

              During the design process, AI can act as a copilot or technical assistant. For example, it can:

              • propose lighter materials that still meet strength requirements

              • suggest geometric variations to reduce weight or improve rigidity

              • quickly analyze the impact of small design changes on overall performance

              In practice, engineers can ask AI questions such as:

              • “Which materials meet these strength and weight constraints?”

              • “What geometric alternatives could reduce the mass by 10 percent?”

              However, final validation, trade-off decisions, and system integration remain the responsibility of the engineer.

              3.2 AI as an analyst for standardized tasks

              For more standardized analytical tasks, AI becomes a particularly useful engineering assistant. It can support:

              • the processing and structuring of large volumes of data

              • the automatic generation of variants for comparative studies

              • consistency checks across large sets of technical documentation

              This allows teams to explore more possibilities in less time, without removing the engineer from the decision-making process.

              4. Should engineers fear being replaced by AI?

              The fear of being replaced by machines is real and understandable, especially in technical professions.

              4.1 Vulnerable jobs vs resilient jobs

              A job is more exposed to automation when its tasks are:

              • repetitive

              • highly standardized

              • not very creative

              • associated with limited decision-making

              In contrast, a job is more resilient when it involves:

              • significant creativity

              • a global understanding of complex systems

              • multi-criteria trade-offs (cost, performance, risk, environmental impact)

              • strong responsibility for safety, compliance, or performance

              In engineering, activities such as:

              • defining a product’s overall architecture

              • breakthrough innovation

              • high-impact technical decisions

              • field responsibility

              remain firmly within the human domain.

              4.2 A change in role rather than disappearance

              Consider the example of technical documentation.

              Yes, AI can generate documents based on validated models or historical data.

              No, it does not replace engineers when it comes to:

              • critical decision-making

              • technical trade-offs

              • creative innovation

              What changes most is how time is allocated:

              • less manual and repetitive production work

              • more design, analysis, validation, and innovation

              Toward augmented engineering, not automated engineering

              Artificial intelligence brings real value to engineering by:

              • automating repetitive, low-value tasks

              • accelerating the generation of drawings and technical documentation

              • assisting engineers in exploring design alternatives and performing analysis

              However, creativity, domain expertise, and responsibility remain central to the engineer’s role.

              The goal is not to replace humans, but to build intelligent collaboration:

              • letting AI handle what it does best (speed, repetition, scale)

              • preserving what defines engineering expertise: inventing, evaluating trade-offs, and taking responsibility for decisions

              The future of engineering will not be “human or AI,” but clearly human + AI: augmented engineering that is more efficient, safer, and more focused on innovation.


              Benoit Bilodeau

              Senior Solutions Architect

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                How to Define Bonded Interactions in SOLIDWORKS Simulation: A Practical Case Study

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                How to Define Bonded Interactions in SOLIDWORKS Simulation: A Practical Case Study

                Are you wondering which interaction type should be used in SOLIDWORKS Simulation to represent a weld or to attach two bodies so they do not separate during the analysis?

                Think about a bracket supporting critical components of a product. What must be done to ensure that the simulation accurately represents the real behavior before running the analysis?

                After reading this blog, you will be familiar with the key steps required to properly define bonded interactions in SOLIDWORKS Simulation. 

                Representation of the bonded interaction

                In SOLIDWORKS Simulation, a bonded interaction is used to connect two or more bodies so that no relative motion is allowed at their interface. A typical example is welding a bracket to another component to reinforce a structure and reduce stress in critical areas.

                A bonded interaction is equivalent to merging bodies while still allowing each part to retain its own material properties. Once defined, the connected bodies are assumed to never separate during the analysis. This represents an idealized, perfectly rigid weld. While such a condition does not exist in reality, it is often a reasonable and efficient assumption when a near-perfect weld behavior is expected.

                A bonded interaction should not be used to represent a contact condition (formerly called No Penetration) or any situation where sliding between components is expected.

                In some cases, however, it may be acceptable to use a bonded interaction instead of defining multiple contact conditions in order to simplify the analysis. An example is a threaded rod, where detailed local behavior is not required and where the objective is to capture the global structural response rather than local stresses.

                Mesh refinement plays a key role in obtaining accurate results near bonded interaction regions. Adjusting the global mesh parameters or applying local mesh controls can significantly improve mesh consistency at the interface and help ensure reliable and meaningful results.

                Modeling Assumptions: Using Bonded Interactions in a Welded Structure

                We are going to consider the following case study to illustrate the bonded interaction application. See the image below:

                Jib crane case study highlighting potential bonded interaction locations
                Jib crane case study highlighting potential bonded interaction locations

                In this jib crane case study, the gusset parts are welded to the column and base plate to increase the overall resistance of the local area. Because of the nature of the problem, we make the assumption that the parts are tied together and that there is no relative motion between them. Therefore, we can apply a bonded interaction at this location to represent multiple welding interactions.

                Please note that it is not necessary to model the weld as a separate part or body in SOLIDWORKS. This simplifies both the model and the analyst’s work while requiring only minimal additional information.

                As with any modeling assumption, the use of bonded interactions should always be aligned with the objectives of the analysis and the level of accuracy required.

                Global vs Local Bonded Interactions: Setup and Best Practices

                Here we are, the most sought after section of this blog on how to define the bonded interaction. There are several ways to define bonded interactions in SOLIDWORKS Simulation. The good news is that the default option when creating a stress analysis with SOLIDWORKS Simulation is set to apply a bonded interaction at a global level. This means that for coincident solid bodies, no additional interaction definition is required as long as the global interaction type is set to Bonded. The global bonded interaction can be found in the Simulation Tree in the Connections folder, under Component Interactions. Additional options can be set to take into account a gap between the bodies. The following image shows a case where a bonded interaction already defined by default could already be sufficient, meaning that there is no additional required step:

                 Alternative jib crane design where the gussets fit into slotted holes
                Alternative jib crane design where the gussets fit into slotted holes

                In some specific cases, a bonded interaction must be defined at a local level which requires a definition in the software. It could be the case of parts with different mesh types or geometry inconsistencies. Let’s consider the case study where there is a small gap between the gusset and the column where a bonded interaction is needed to represent a welding.

                To define a local bonded interaction:

                1. In the Simulation Tree, right-click Connections and select Local Interaction.

                1. In Type, choose Bonded.

                1. In the blue selection box, select the first entity (ideally the smaller one).

                1. In the pink selection box, select the second entity (ideally the larger one).

                1. Multiple entities can be selected if required.

                1. If necessary, define additional options such as the gap tolerance.

                Local bonded interaction definition
                Local bonded interaction definition

                Interpreting Results When Using Bonded Interactions

                When the calculations complete and that we are at the step of validating the results, it is very important to understand how they should be interpreted. Adding unnecessary bonded interactions tends to artificially increase the stiffness of the structure. This can make the model appear stronger than it actually is, resulting in a non-conservative analysis. Therefore, it is important to keep that in mind and make sure that the analysis represents the real case study appropriately. An animation of the results is an excellent way to determine whether or not the structure behaves as it should be. Expect stress concentrations near edges with bonded interactions and pay attention to stress singularities. If necessary, plot the reaction forces and compare them with the applied loads. If the results don’t make sense, it is important to consider reviewing the analysis setup and rerunning the analysis.

                Key Takeaways on Bonded Interactions in SOLIDWORKS Simulation

                In this blog, we explored the application of bonded interactions to better understand their meaning and areas of use.

                Through the lifting jib crane case study, we illustrated the creation of both global and local bonded interactions. In Finite Element Analysis (FEA), the quality of results depends primarily on the relevance of your modeling assumptions and choices.

                Beyond interactions, there are other features that must be used properly to produce reliable simulations tailored to your objectives. If you wish to deepen your knowledge of SOLIDWORKS Simulation, several resources are available to support your progress.

                You can visit our website to read our other technical blogs and learn more: https://www.solidxperts.com/en/blog/


                Chung Ping Lu, eng.

                Chung Ping Lu, eng.

                Senior Technical Representative

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                  Managing Your 3DEXPERIENCE Cache: Keep Your Files Clean and Up to Date

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                  Managing Your 3DEXPERIENCE Cache: Keep Your Files Clean and Up to Date

                  One of the biggest advantages of the 3DEXPERIENCE platform is having your files stored securely in the cloud. You can access your designs anytime, anywhere, and collaborate with teammates without worrying about version control.

                  Behind the scenes, SOLIDWORKS uses a local cache, a folder on your computer where files are temporarily stored while you work. These cached files are then synced with the 3DEXPERIENCE servers when you save or refresh.

                  Managing this cache is key to keeping your designs current, preventing confusion, and saving disk space. Let’s take a closer look.

                  Where to Find the 3DEXPERIENCE Cache

                  Think of 6W Tags as smart labels that make it easy to filter, sort, and find your files in 3DSpace or 3DDrive.

                  Your cache shows up both in SOLIDWORKS (via the 3DEXPERIENCE add-in) and in Windows Explorer. While you can browse the cache folders directly, we don’t recommend managing them that way. Instead, stick to the tools built into SOLIDWORKS.

                  Here are the default folder locations:

                  • SOLIDWORKS Desktop with the 3DEXPERIENCE add-in:
                    C:\3DEXPERIENCE

                  • SOLIDWORKS Connected:
                    C:\Users\<username>\AppData\Local\DassaultSystemes\3DEXPERIENCE

                  Managing the Cache Inside SOLIDWORKS

                  When you enable the “3DEXPERIENCE Files on This PC” add-in, you’ll see a dedicated tab in the Task Pane. This view shows you all cached files with helpful details like:

                  • Status

                  • Lock Status

                  • Maturity State

                  ub / Managing Your 3DEXPERIENCE Cache

                  From here, you can quickly refresh your cache to make sure you’re always working with the latest version.

                  • Refresh View updates the local cache for selected files.

                  • Refresh from Server checks for changes made by other users and downloads the latest copy if needed.

                  • Starting a new SOLIDWORKS session automatically refreshes files in the background.

                  ub / Managing Your 3DEXPERIENCE Cache - 2

                  Understanding Cache Status Icons

                  The status icons make it easy to tell if your local files are current, out-of-date, or waiting to be uploaded. They also warn you if refreshing would overwrite changes you’ve made locally.

                  ub / Managing Your 3DEXPERIENCE Cache - Icons

                  Pro tip: Always double-check before reloading from the server. Unsaved local edits will be lost.

                  Cleaning Up the 3DEXPERIENCE Cache

                  Over time, cached files can pile up and take up space. To keep things tidy (and ensure you’re pulling the latest versions from the cloud), it’s a good idea to clean your cache periodically.

                  Here’s how:

                  1. In the Task Pane, select individual files, or use the top-left checkbox to select all.

                  2. Right-click and choose Delete from this PC.”

                  ub / Managing Your 3DEXPERIENCE Cache - Delete

                  This only removes files from your local cache. Your data stays safe in the 3DEXPERIENCE platform.

                  You can also use:

                  • Filters to find specific file types.

                  • The search box to locate files quickly.

                  And before you delete, always confirm your files are saved and synced to the platform.

                  Automating Cache Clean-Up

                  Don’t want to do it manually? The Clean Up command takes care of it for you.

                  • By default, it removes unchanged files older than one week.

                  • Locked or modified files won’t be touched.

                  • If you open an assembly later, any missing references are automatically redownloaded from the server.

                  ub / Managing Your 3DEXPERIENCE Cache - Automation

                  If disk space isn’t a concern, you can extend the timeframe to reduce how often files get cleared. It is especially useful if your internet connection is slow.

                  A Simple Habit for Staying Up to Date

                  The local 3DEXPERIENCE cache is like a bridge between your desktop and the cloud. Keep it clean, refresh it often, and you’ll always know you’re working with the latest designs.

                  Want to get even more out of your 3DEXPERIENCE platform? Our training sessions are designed to help you and your team take full advantage of its powerful tools.


                  Michael Habrich

                  3DEXPERIENCE Specialist

                  LinkedIn

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                    Make Your Data Work for You with 6W Tags on the 3DEXPERIENCE Platform

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                    Make Your Data Work for You with 6W Tags on the 3DEXPERIENCE Platform

                    The 3DEXPERIENCE platform isn’t just about CAD in the cloud. It’s your all-in-one workspace where design, data management, and collaboration come together. Whether you’re sketching with xShape, modeling in xDesign, or connecting to SOLIDWORKS, the platform helps keep everything, and everyone, in sync.
                    But let’s be honest: every engineering project generates mountains of data. 3D models, drawings, BOMs, simulations, even invoices and Word docs. It all piles up. The good news? The platform makes it easy to organize and navigate this information with a powerful tool called 6W Tags.

                    What Are 6W Tags?

                    Think of 6W Tags as smart labels that make it easy to filter, sort, and find your files in 3DSpace or 3DDrive.

                    6W Tags in SOLIDWORKS

                    And it’s not just CAD data. Office documents, simulation results, and more can all benefit from tagging.

                    Here’s how the 6Ws break down:

                    • What: Type of content (CAD models, documents, simulations, tasks, etc.)
                    • Who: The person who uploaded, edited, revised, or owns the data
                    • When: Date or time range
                    • Where: Geolocation or data source
                    • How: Manufacturing method (made in-house or purchased)
                    • Why: Links to project or task management

                    Out of the box, the system automatically fills in basics like owner, location, and save date. But the real power comes when your team adds custom tags. For example, you can include project numbers, material types, or vendor names so searches are tailored to your company’s workflow.

                    How to Use 6W Tags

                    Let’s say you search for “bolt” in 3DEXPERIENCE. Without filters, you might get hundreds (if not thousands) of results. That’s where 6W Tags shine.

                    Search bar for 6W Tags in SOLIDWORKS

                    Click the tag icon next to the search bar, then start narrowing your results. For example:

                    • Under What, choose Physical Product (to exclude tasks or documents).
                    • Add a Material filter for Stainless Steel.

                    By stacking filters, your results go from overwhelming to precise in just a few clicks.

                    Real-World Examples

                    In one test, a simple search brought back over 1,000 results. But after filtering with 6W Tags for “Physical Product” and “Plain Carbon Steel,” the number of results dropped down to two digits. That’s the power of smart filtering.

                    Beyond search, 6W Tags can be used visually inside apps. For example, parts can be color-coded by material in the graphics area, giving you an instant overview of your design.

                    From Data Overload to Data Control

                    Data shouldn’t slow you down and with 6W Tags, it won’t. Whether you’re hunting down a single file or organizing entire projects, the 3DEXPERIENCE platform helps you stay in control.

                    Want to learn more tips like this? Our experts at Solidxperts can help you get the most out of your 3DEXPERIENCE environment. Reach out anytime or join one of our training sessions!


                    Michael Habrich

                    3DEXPERIENCE Specialist

                    LinkedIn

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