AI automation of 3D rule checking
Application to an aeration vent design
In today’s fast-paced product development environment, design validation must be fast, automated, and reliable. Manual 3D checks are time-consuming, error-prone, and difficult to scale.
This case study explores how Dessia’s AI-driven platform automates 3D rule verification for CAD models — reducing manual effort, increasing accuracy, and accelerating decision-making. While the geometry is relatively simple, the process represents a powerful shift toward automated compliance checking in CAD design.
The challenge: Manual design rule checking in 3D CAD
In mechanical design and engineering, even the simplest parts often require validation against functional and manufacturing rules — such as airflow constraints or minimal feature dimensions.
For the aeration vent, two critical rules had to be checked:
- Functional: The total flow area must exceed a defined threshold (X mm²)
- Manufacturing: Each hole must meet minimum width and length requirements
Traditionally, checking these kind of rules meant manual inspections and custom scripting — all prone to delay and inconsistency.
The challenge: How can we make this process faster, scalable, and repeatable across projects and teams?
What Dessia can do: AI-based 3D design rule checker
Using Dessia’s low-code AI platform, custom AI-Apps were developed to automate all verification steps — allowing designers to run geometry checks without coding or scripting. The process involved:
1. Architecture definition
A 4-block architecture was designed to cover:
- Input reading (3D model & rule parameters)
- Hole detection
- Geometry computation (area, width, height)
- Rule validation logic
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These blocks were coded in Python using Dessia libraries and then visually linked in the Dessia App Builder — enabling fast re-use across projects.
2. Live execution from the Dessia’s AI platform
Within Dessia’s platform, users simply:
- Select and load the 3D CAD model
- Define functional airflow requirements
- Input manufacturing constraints
- Launch the checker and review results
No scripting. No custom interfaces. Fully integrated within a collaborative design environment.
3. Visual and actionable results
After execution, the App generates:
- 3D visualizations with clear pass/fail color coding
- Summary reports of compliance status
- Immediate insights for corrective action
The execution of the checkers can be carried out within Dessia software, or it can be initiated from a third-party tool, such as CAD or PLM software.
Results: Faster, smarter, and scalable verification/ check
- 90% time reduction in rule-checking
- Consistent rule application across all models and users
- Immediate visual feedback for faster design loops
By automating 3D checks with Dessia, design teams can now:
- Perform validation at any time from concept phase
- Catch non-compliant features before detailed design
- Standardize rule enforcement across teams and suppliers
- Save valuable engineering hours
Conclusion
This case study offers a clear, educational example of how Dessia AI's automated design rules checking functions. While simple, the implications are far-reaching. Dessia’s approach shows how AI, automation, and visual workflows are transforming traditional design validation.
From functional compliance to manufacturing readiness, Dessia enables teams to embed rule-based intelligence directly into the design process — making it faster, smarter, and future-ready.
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