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

Follow me on LinkedIn

Any questions? Need help? Ask one of our experts.

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

AI won’t replace you. Someone using AI will.

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AI won’t replace you. Someone using AI will.

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AI may not be perfect yet, but it’s precisely why you should start using it today.

We’ve grown used to talking about artificial intelligence as if the story began in 2022. ChatGPT arrives, the public adopts it, and suddenly AI becomes a topic of casual conversation. But if we want to properly understand what’s happening, we must not confuse media frenzy with historical reality. OpenAI did release ChatGPT publicly (“research preview”) on November 30, 2022, and yes, it was a real social inflection point.

But AI as a field is much older. Turing formalized the intellectual framework of the “imitation game” as early as 1950, and the Dartmouth Proposal (1955) explicitly announced a summer 1956 project dedicated to “artificial intelligence.” Some early demonstrations also appeared quickly: the Ferranti Mark I ran a limited chess program in 1951 (mate-in-two).

This reminder is not meant to give you a history lesson. It serves one purpose: AI is not a feature. It is a trajectory.

And it resembles another well-known human trajectory: that of fire.

The Fire Analogy: Understanding a Technology We Don’t Yet Understand

At this point, you’re probably thinking: “What is he talking about?” Stay with me.

One day, in a cave, one of our ancestors discovered fire. At first, this discovery served very specific purposes: heating, lighting, protection. These were not “industrial innovations”; they were immediate uses. And yet, the full chain – metallurgy, machines, steel industries – that followed from this same discovery reshaped modern history. The human on day one could not imagine the human of today. Not because they were less intelligent, but because they lacked perspective.

We are at the same stage. Except instead of holding a torch, we are writing prompts. And the typical mistake in 2026 is judging AI based on what it is today, as if it were representative of tomorrow’s trajectory.

The Real Signal: Speed of Evolution

What matters is not only what AI does today. What matters is how fast it improves. To make that speed tangible, a cultural artifact has emerged: the “Will Smith Eating Spaghetti test,” now documented as an informal benchmark.

Case Study: The “Spaghetti Test”

In its 2023 version, human motion is unstable: faces and hands deform, physics is not believable. In the 2026 version, the result becomes coherent enough that the difference is obvious: we are no longer looking at a “grotesque meme,” but at a rendering that requires a critical eye to detect AI involvement.

What matters here is the underlying learning dynamic. The progression observed between 2023 and 2026 cannot be attributed solely to model improvements. It is also the result of user adoption.

Early uses produced low-quality, unstable, and difficult-to-use outputs. However, these experiments helped gradually identify model limitations, refine interaction methods (prompts, iterations, post-processing), and structure more robust practices.

In other words, the improvement in outputs in 2026 is inseparable from the learning accumulated by users over time. Current performance is not only technological; it is also cognitive and methodological.

This is how the concept of cumulative advantage should be understood: it does not rely solely on access to technology, but on the experience gained by using it under imperfect conditions.

From Internet Culture to the Engineering Office: Why SOLIDWORKS Is Concerned

The transition from “spaghetti → SOLIDWORKS” is not arbitrary. It is the same mechanism applied in a different context. A general-purpose technology crosses a threshold, then infiltrates products, becomes invisible, and ultimately reshapes practices.

We’ve already seen this in the 2010s: AI did not “look like ChatGPT,” but it was already embedded in everyday life. Google Maps, for example, deployed models (including graph neural networks) at scale for ETA (Estimate Time of Arrival) and traffic prediction. The result: you use AI without thinking about it. The advantage rarely comes from an “AI button,” but from the routines that evolve around your activity.

SOLIDWORKS 2026: The AI Shift Is Underway

This is exactly the same dynamic in SOLIDWORKS.

SOLIDWORKS 2026 already integrates AI into areas where real time is lost: drawings, assemblies, and access to knowledge. Dassault Systèmes presents SOLIDWORKS 2026 as an “AI-powered” portfolio (design, collaboration, data management).

A clear example: Auto-Generate Drawings (BETA). The “What’s New in SOLIDWORKS 2026” documentation explicitly describes automatic drawing generation, including section views and hole callouts.

The same logic applies to assemblies: SOLIDWORKS documents AI-based fastener recognition to automatically create SmartMates, with explicitly listed limitations. This level of detail is precisely what makes the promise credible (and reminds us that this is not “magic,” but engineering with constraints).

Rather than listing every available feature, it is more relevant to focus on the direction: Dassault introduces “Virtual Companions” (AURA, LEO, MARIE), with AURA and LEO already available and MARIE announced soon. SOLIDWORKS also highlights “AI-guided” features in FD01 (guided analysis, guided creation).

What matters here is not proving that everything is ready. It is recognizing that AI has entered the tool, meaning the learning process has begun, whether you like it or not. And it is moving fast.

Waiting for Maturity: A Strategic Mistake

Let’s be clear: in 2026, all of this is still imperfect. And that is normal. We are at the “spaghetti 2023” stage of AI-assisted CAD: promising, functional in certain areas, but not yet obvious everywhere.

The instinctive reaction for many teams is: “we’ll wait until it’s mature.”

This reaction is human. But strategically, it is a serious mistake.

In 2025, we clearly entered a phase of mass adoption. Nearly 88% of organizations report using AI in at least one function, compared to 78% the previous year. This adoption is accelerating and follows an exponential curve.

From an economic perspective, the signals are just as clear. The generative AI market reached nearly $60 billion in 2025 and could exceed $400 billion by 2031.

In industry, the shift is already visible: nearly 76% of manufacturing companies are using AI in 2026.

But the most interesting point is not adoption. It is the gap between adoption and impact. Despite massive investments, only about 5% of companies currently manage to generate significant value from AI. In most cases, projects remain stuck at the experimental stage, and the majority of initiatives never reach production.

In other words: everyone has access to AI, but very few truly know how to use it. So “waiting” does not mean being cautious. It means allowing a capability gap to form. Because knowing how to use AI is a skill. And it must be learned.

What Research Says About Gains (and Their Limits)

To address the assumption “we’ll wait until AI is ready,” it is important to understand a key nuance: AI does not deliver uniform gains, and that is precisely why early learning matters.

The operational conclusion is simple: early adoption is not a blind bet; it is a mapping phase. It helps you understand when AI works, when it fails, and most importantly how to control it.

What It Really Changes: Redefining Engineering Performance

This is where the thesis becomes concrete: AI will not replace you. A competitor who masters it will.

And I mean mastery in the strict sense. Asking ChatGPT for a carbonara recipe does not count. We are talking about work practices, standards, quality control, understanding when AI accelerates a task and when it introduces risk, knowing where to integrate AI in a project without breaking traceability, and knowing how to train teams without creating blind dependency.

In other words, mastery is not built when the tool becomes “perfect.” It is built while it is imperfect, because that is when you establish your standards, your checklists, your controls, and your best practices.

Ultimately, the value of an engineer will not only be their technical skill. It will be their ability to amplify that skill with properly framed AI.

Conclusion: From Intention to Action

The question is no longer whether you are using AI. It is already present in your tools, your processes, and your competitive environment.

The real question is whether you are learning to use it properly.

Like all major technological transformations, the advantage does not go to those who wait for everything to stabilize. It goes to those who start while it is still imperfect, who experiment, who structure, and who gradually build solid methods.

AI does not replace engineering. It redefines its standards.

And this transition does not happen alone.

At solidxperts, our teams are already working with these tools on a daily basis. We support companies in implementing practical AI use cases in SOLIDWORKS: identifying relevant use cases, integrating them into existing processes, training teams, and establishing reliable standards.

If you want to understand concretely what AI can bring to your environment, we offer demos and working sessions tailored to your reality.

The simplest next step is to start the conversation.

Max Laramée

Marketing Director

Follow me on LinkedIn

Any questions? Need help? Ask one of our experts.

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

Connecting SOLIDWORKS Desktop to the 3DEXPERIENCE Platform

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Connecting SOLIDWORKS Desktop to the 3DEXPERIENCE Platform

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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.

During installation, you’ll be prompted to:

Install all granted roles, or
Install only the roles required for the Design with SOLIDWORKS connector

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.

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

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.

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.

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

Follow me on LinkedIn

Any questions? Need help? Ask one of our experts.

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

7 Myths About AI: Demystifying Bias and Technological Limits

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7 Myths About AI: Demystifying Bias and Technological Limits

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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

Follow me on LinkedIn

Any questions? Need help? Ask one of our experts.

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

Artificial Intelligence in Engineering: Automation Without Losing the Human Touch

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Artificial Intelligence in Engineering: Automation Without Losing the Human Touch

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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

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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

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

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:

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

In Type, choose Bonded.

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

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

Multiple entities can be selected if required.

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

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.

Senior Technical Representative

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3DEXPERIENCE World 2026: Big Ideas, Real Innovation, and a Community That Inspires

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3DEXPERIENCE World 2026: Big Ideas, Real Innovation, and a Community That Inspires

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This year’s 3DEXPERIENCE World 2026 in Houston brought together the global SOLIDWORKS community. Designers, engineers, students, educators, makers, and executives from all over the world gathered for three days of inspiration, learning, and connection. From powerful keynotes to cutting-edge tech announcements, here’s a highlight reel you’ll want to read.

Day One: Vision, AI, and the Future of Engineering

Day One kicked off with a high-energy General Session that set the tone for the entire event: innovation powered by people and amplified by the right tools. Thousands of attendees gathered to hear leadership from the industry share bold perspectives on where product development is heading.

A major theme throughout the day was artificial intelligence (AI). SOLIDWORKS leadership made it clear that AI isn’t a gimmick, it’s already reshaping workflows and helping teams accelerate insight, design, and validation. We saw a live demo of new AI assistants: Aura, Leo, and Marie. Three virtual companions that work together to streamline everything from knowledge context and engineering reasoning to scientific rigor.

Aura orchestrates requirements, projects, and changes.
Leo brings engineering reasoning to life (mechanics, motion, simulation), think Leonardo da Vinci.
Marie applies scientific and regulatory insight, think Marie Curie.

These assistants are designed as companions, not autopilots. They’re tools that let engineers stay in control and design with confidence rather than guesswork.

Later, attendees heard from Pascal Daloz and Gian Paolo Bassi on how a combination of human creativity and connected technology fuels progress faster than ever. One standout message? “Success shouldn’t be judged by speed alone, but by time to value”, meaning design outcomes that are reliable, meaningful, and innovative.

Day One wrapped with a keynote from futurist Pablos Holman, reminding us that the real power of technology lies in solving real world problems, from healthcare to space exploration. His message was forward-looking, inspiring, and grounded in practical impact.

Day Two: Engineering Partnerships and AI in Action

Day Two turned the spotlight to how industry partnerships and technology collaboration are reshaping the way we work. The session opened with Dassault Systèmes CEO Pascal Daloz discussing how a strong engineering community combined with strategic technology partnerships creates innovation that scales.

We heard from Jensen Huang, Founder and CEO of NVIDIA, about the long-standing collaboration between NVIDIA and Dassault Systèmes. Their work emphasizes science-driven AI and virtual twins, paired with high-performance computing to give engineers tools that can handle truly complex systems in simulation and design at scale.

SOLIDWORKS CEO Manish Kumar also joined the stage to outline how AI is being embedded into real design workflows, not as a theory, but as a practical productivity boost. This includes contextual AI tools that help reduce repetitive tasks, free up time for innovation, and bring deeper insight into design decisions.

One of the most exciting themes of Day Two was this: “AI isn’t here to replace engineers; it exists to empower them.” By putting smart tools into your hands, you get faster iteration cycles, fewer errors, and a stronger connection between your design intent and your final product.

Day Three: Community Celebration and the Next Generation

The final day of 3DEXPERIENCE World 2026 was all about community, the people who make this ecosystem special. Whether you’re a seasoned pro, an educator, a student, or a maker, this day celebrated the connections that make innovation possible.

Suchit Jain, VP of Strategy and Business Development, kicked things off by highlighting how collaboration across industry, education, and local innovation hubs builds the workforce of tomorrow. There were strong messages about supporting emerging talent, integrating real-world problem solving into education, and making sure SOLIDWORKS continues to be accessible to innovators of all backgrounds.

Day Three also spotlighted how regional and global communities are using SOLIDWORKS tools to solve real problems, whether that’s in manufacturing, healthcare, education, or startup growth. It was a reminder that technology only reaches its potential when it’s put to work by passionate people.

The final sessions included inspiring competition recaps, community-driven breakout sessions, and previews of what’s coming next, including early looks at SOLIDWORKS 2027 features that continue the theme of smarter workflows and tighter collaboration between design, simulation, and data.

What This Means for You

Across all three days of 3DEXPERIENCE World 2026, a few themes stood out loud and clear:

Innovation grows when humans and tools work together.
AI companions are here, but they’re companions. They help you work smarter, not replace your expertise. AND the best part, they come with SOLIDWORKS with Cloud Services.
Connected ecosystems : partnerships + community to accelerate progress.
Whether it’s NVIDIA, startups, educators, or global manufacturers, connection drives insight at scale.
The next generation of designers is in focus.
Programs, hubs, and community efforts are investing in future creators, ensuring SOLIDWORKS tools remain integral to how engineering gets done in the years ahead.

3DEXPERIENCE World 2026 wasn’t just a conference, it was a reminder of why we design, why we connect, and why we build communities around shared purpose, problem solving, and progress.

We’re already looking forward to 3DEXPERIENCE World 2027!

If you want to know more about how SOLIDWORKS users can begin to leverage AI in 2026, call us, or visit at 2650 Avenue Marie-Curie, QC.

Michael Habrich

3DEXPERIENCE Specialist

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Updated Instructions for Adding the Thumbnail Column in the 3DEXPERIENCE Platform

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Updated Instructions for Adding the Thumbnail Column in the 3DEXPERIENCE Platform

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If you use the Bookmark Editor inside the 3DEXPERIENCE platform, the Thumbnail column can be a great way to quickly recognize your content without digging through details. With the July 2025 update, the way you add this column (and a few others) has changed a little. No worries, we will walk you through it.

Creating a Custom View

To add the Thumbnail column, you’ll now need to work with a custom column view instead of the default one.

Right-click any column header in Bookmark Editor.
Select Table Preferences.

You’ll see a dropdown at the top. Most users will currently be using Default.

Click the + icon to create your own view.
- The platform will call it Copy of Default view by default.

Rename it to something meaningful, maybe “My Thumbnail View”,and click the checkmark to confirm.

Adding the Thumbnail Column

You’ll now see two panels: Available Columns and Selected Columns.

Use the search bar or browse the list to find Thumbnail.
The Thumbnail column is located under Common Attributes.
Select it, then click the right-arrow to add it to your selected list.

Once it’s added:

Drag and drop the column to adjust its position, or
Use the up/down arrows to move it into place.

If you’d like it to stay visible while scrolling, use Pin Left.

Click Save when you’re done.

Switching Between Views

If you create more than one custom layout (or want to go back to the original):

Right-click any column header
Go to Table Views
Choose the view you’d like to use

Done! Your Bookmark Editor now includes visual thumbnails, making it easier to browse, recognize, and manage your data at a glance.

Want More 3DEXPERIENCE Platform Tips?

We love helping teams get the most out of their tools.
If you’d like to go a bit further:

Explore more tutorials on our YouTube channel;
Join a training;
Or reach out! We are always happy to share best practices and workflows.

Your platform should feel like it’s working for you. And we’re here to make sure it does!

Michael Habrich

3DEXPERIENCE Specialist

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

Creating Custom Attributes in 3DEXPERIENCE

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Creating Custom Attributes in 3DEXPERIENCE

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Sometimes the standard attributes in 3DEXPERIENCE just aren’t enough. Maybe you need a custom field to track a project code, client name, or any other detail that’s specific to your workflow. The good news? You can create your own attributes in just a few steps — and we’ll show you how.

Before You Start

To build custom attributes, you’ll need:

Administrative privileges on your platform
The Platform Manager role assigned to your profile

Once that’s in place, you’re ready to go.

Step 1: Open Attributes Management

Select the Platform Management role.
Head into the Collaborative Spaces Control Center.
From there, choose Attributes Management.

C’est ici que toute la personnalisation prend forme.

This is where all the customization magic happens.

Step 2: Find Physical Product

In the search bar (click the little magnifying glass), type Physical Product.
This is the object type where most custom attributes live, alongside built-ins like Material or Weight.

Step 3: Create Your Attribute

Click the plus sign in the top-right corner.

Choose a unique name (no duplicates, no special characters).

Hit OK — and your new attribute will appear at the bottom of the Physical Product page.

👉 Pro tip: if you don’t see the green check mark, it means the attribute hasn’t been activated yet.

Step 4: Activate and Deploy

To make your new attribute usable:

Click Apply.
Go to the Collaborative Spaces Configuration Center.
Run Update Index Model and Reload Cache.

⚠️ Heads-up: this part may take a few minutes. Be patient while the platform updates.

Step 5: Test It Out

Open any saved 3D part, click the down arrow > Information, and scroll down. Your new custom attribute should now be visible and ready to use. Add a value to confirm everything’s working as expected.

Why Custom Attributes Matter

By creating custom attributes, you’re tailoring 3DEXPERIENCE to fit your business. That means:

Better search results
Smarter organization
Easier categorization of your data

At Solidxperts, we’re all about helping you get the most out of your tools. Custom attributes are just one way to make your 3DEXPERIENCE platform work harder for you.

Need a hand setting them up? Our team can walk you through it and make sure your environment is optimized for your exact workflow.

Michael Habrich

3DEXPERIENCE Specialist

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

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

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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

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.

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.

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:

In the Task Pane, select individual files, or use the top-left checkbox to select all.
Right-click and choose “Delete from this PC.”

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.

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

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Any questions? Need help? Ask one of our experts.

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

Request a Quote

Guide: Getting Started with AI in SOLIDWORKS

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

Step 2: Confirm Your Prerequisites

Required Software

Platform Access

System Requirements

Step 3: Activate the 3DEXPERIENCE Platform

How to Activate:

Step 4: Install the 3DEXPERIENCE Launcher

Steps:

Step 5: Install “Design with SOLIDWORKS”

Installation Steps:

Step 6: Launch SOLIDWORKS from the Platform

First Launch:

Why this matters:

Step 7: Verify the 3DEXPERIENCE Add-in

Check:

Step 8: Access the AI Labs Tab

Where to Find It:

What You’ll Find:

Step 9: Start Using AI Features (Practical Examples)

Good First Use Cases:

What Not to Expect:

Step 10: Best Practices for Getting Started

✔ Start Small

✔ Focus on Real Problems

✔ Validate Everything

✔ Train Your Team Gradually

Final Thoughts: Where AI in SOLIDWORKS Is Headed

AI won’t replace you. Someone using AI will.

AI may not be perfect yet, but it’s precisely why you should start using it today.

The Fire Analogy: Understanding a Technology We Don’t Yet Understand

The Real Signal: Speed of Evolution

Case Study: The “Spaghetti Test”

From Internet Culture to the Engineering Office: Why SOLIDWORKS Is Concerned

SOLIDWORKS 2026: The AI Shift Is Underway

Waiting for Maturity: A Strategic Mistake

What Research Says About Gains (and Their Limits)

What It Really Changes: Redefining Engineering Performance

Conclusion: From Intention to Action

Connecting SOLIDWORKS Desktop to the 3DEXPERIENCE Platform

Installing Design with SOLIDWORKS

Enabling the 3DEXPERIENCE Add-In in SOLIDWORKS

Launching SOLIDWORKS with the Connector

Saving Files to the 3DEXPERIENCE Platform

Managing the 3DEXPERIENCE Cache

Final Thoughts

7 Myths About AI: Demystifying Bias and Technological Limits

The Major Myths Shaping Our View of AI

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

Models That Predict, Not Understand

Anthropomorphism as a Persistent Bias

2. Superintelligence and the Resource Wall

The Data Wall: A Finite Resource

Physical Constraints and Diminishing Returns

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

Data Bias: Who Is Represented?

Design Bias: Optimization Choices Matter

4. The Plural Architecture of AI

A Hierarchy of Often-Confused Concepts

Divergent Definitions

A Fragmented Ecosystem

Narrow AI vs General AI

5. Ethics, Marketing, and the Need for Regulation

Ethical Marketing

Toward Enforceable Regulation: The EU AI Act

6. AI Is Not a Magic Wand

AI as a Component, Not a Strategy

Moving Beyond the Myths

Artificial Intelligence in Engineering: Automation Without Losing the Human Touch

1. What AI concretely brings to engineering

1.1 Automating repetitive, low-value tasks

1.2 Measurable efficiency gains

2. Tasks that remain (and will remain) human

2.1 Creative design and early project phases

2.2 Safety, compliance, and responsibility

3. Toward intelligent human–machine collaboration

3.1 AI as a copilot during design

3.2 AI as an analyst for standardized tasks