Scaling DFM Best Practices With AI: A Case Study in EV Component Manufacturing

AI can turn existing design guidelines into automated design for manufacturing (DFM) checks by combining deterministic analysis of CAD geometry with large language models that interpret company-specific requirements. CoLab recently applied this approach with an automotive supplier, achieving greater than 80% accuracy and relevance in an initial implementation. Here's how we approached the implementation, what the initial results showed, and why I think DFM is one of the most practical applications of AI for engineering teams.

This article is adapted from a talk I gave at SimScale's Agentic Unlocks event. You can watch the full session here.

Why is it so difficult to apply DFM expertise across every design?

One of CoLab's customers, an automotive supplier, wanted to compete for more electric vehicle programs. The company already had detailed DFM guidelines for its plastic parts, but needed a way to apply that expertise consistently across more engineering projects. The goal was to turn those guidelines into automated checks that design engineers could run themselves, earlier in development.

Traditionally, DFM reviews follow a fairly linear process. Engineers develop 3D models, create drawings, and send them to manufacturing engineers or suppliers for feedback. When manufacturability issues aren't identified until late in development, resolving them can mean revising drawings, modifying tooling, and delaying production schedules. Much of that feedback could have been provided earlier, when changes were easier and less expensive to make.

I've experienced this sort of thing firsthand. At Tesla, I exchanged DFM feedback with suppliers in Asia through emails and spreadsheets. It was a slow, sometimes painful process.

At CoLab, our design for manufacturability review platform helps teams run design reviews earlier and more collaboratively, while capturing the engineering knowledge that goes into those decisions. That's particularly valuable for DFM, where much of the expertise still lives in company guidelines and the experience of senior engineers.

DFM is a practical starting point for AI because these reviews are already a core business activity. The opportunity before us is how to apply more of a company's existing manufacturing expertise while engineers are still developing their designs, catching issues before they lead to costly rework, tooling changes, or production delays. For large manufacturers, the potential savings can run into the millions of dollars.

How we turned an automotive supplier's DFM guidelines into automated checks

CoLab's approach to DFM review combines a few different capabilities. First, we start with deterministic DFM checks. A lot of DFM analysis is essentially mathematics applied to geometry. You identify features such as holes, bosses, and ribs, then evaluate them against manufacturing requirements.

There are already some DFM tools that perform these types of checks. Our first step was to build those capabilities into CoLab.

The second part involves large language models (LLMs). We use those models to interpret the company's design guidelines and help translate them into DFM checks. AI can also help interpret the results in the context of those guidelines, explaining which findings matter most, why they may be important, and how engineers might address them while following the company's manufacturing best practices.

So there's two things happening here. Geometric analysis establishes what's happening on the part, while AI helps connect that analysis to the company's manufacturing knowledge.

And because we're doing this within CoLab's design review platform, engineers can collaborate with their peers and subject matter experts on the findings.

In our first implementation with the automotive supplier, we achieved greater than 80% accuracy and relevancy against its design guidelines.

That means users generally agreed that the feedback was accurate, relevant, and worth considering or addressing in the design. It doesn't necessarily mean the system detected every possible manufacturing issue. But for an initial implementation, it was a really encouraging result. We are already looking at expanding the approach to more users, business units, and manufacturing methods.

What the AI DFM checks found and how engineers validate the results

In the accompanying video, I demonstrate how this works on a 3D model of an injection-molded component.

A DFM check in CoLab identified several potential manufacturing issues, including insufficient draft angle, sharp edges that could complicate tooling, and wall-thickness concerns. These are among the common issues engineers look for during an injection molding DFM check.

An engineer can select any of those findings and see the corresponding area highlighted on the 3D model. If they agree with a finding, they can turn it into an open feedback item under their own name. If they aren't sure, they can bring it into a design review for input from a manufacturing engineer or another subject matter expert.

We're very deliberate about keeping engineers involved in that process. We want them to validate the feedback and take responsibility for the issues they believe need attention.

The same principle applies to AI drawing reviews. In a separate drawing review example from the presentation, CoLab identified a potential issue and provided a direct reference to the relevant passage in a design guideline. The engineer can open that reference and determine whether the guidance has been applied correctly.

That brings up an interesting question. How do we communicate an AI agent's confidence in its findings?

I think providing the context behind a recommendation is more useful than assigning it a confidence score. Ask an AI agent how confident it is, and it'll often tell you it's very confident. But that doesn't necessarily help you determine whether the finding is valid. If it shows you the applicable design guideline, you can inspect the evidence yourself.

Looking further ahead, we also see opportunities to provide context from industry standards and historical manufacturing quality issues. Imagine identifying a potential DFM problem and being able to reference similar parts that encountered manufacturing issues in the past. That's the kind of information that could make AI-generated feedback considerably more useful to an engineer.

Watch the demonstration below to see how engineers can inspect 3D DFM findings and turn them into actionable design feedback in CoLab.

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How can manufacturers scale AI-powered DFM reviews?

For the automotive supplier, the initial implementation was just the beginning. We're already looking at expanding the checks to more users and business units, as well as other manufacturing processes, including stamping, plating, CNC machining, and sheet metal fabrication.

We're also developing ways to bring AI-based DFM checks directly into CAD tools, starting with Creo, so engineers can identify potential issues while designing and bring those findings into CoLab for review.

Another important piece is keeping those checks up to date. Many manufacturers maintain detailed design guidelines in PLM systems or SharePoint. We're extending our integrations with the goal of automatically updating DFM checks as the underlying guidelines change. Eventually, feedback from design reviews could help improve the guidelines themselves.

But the opportunity goes beyond simply automating more checks.

Think about how much manufacturing expertise exists inside a large engineering organization. Experienced engineers know which design decisions have caused problems in the past. Companies have spent decades documenting those lessons in guidelines and best practices. Yet applying that knowledge consistently still depends heavily on manual reviews, often after a design is already fairly mature.

That's where I see the real potential of AI for DFM: making the manufacturing expertise companies already have available to more engineers, while they're still making design decisions.

Put your company's DFM knowledge to work earlier

Your engineering guidelines contain years of manufacturing expertise. See how CoLab can help your team apply that knowledge earlier in development and bring automated findings into collaborative design reviews.

Book a demo to explore how CoLab could support your DFM review process, or learn more about AutoReview.