Our team is debating the best approach for CAD data validation in our test data management process on Aras 14.0. We’re split between implementing fully automated validation tools versus maintaining manual review processes for critical CAD data.
The automated validation tools can check geometry integrity, property completeness, and relationship consistency much faster than manual review. However, they sometimes miss context-specific issues that experienced engineers catch during manual inspection. On the other hand, manual review is time-consuming and doesn’t scale well as our CAD data volume grows.
What are your experiences with CAD data validation? Have you found hybrid validation strategies that balance automation benefits with human expertise? How do you decide what gets automated versus what requires manual review?
I’m skeptical of pure automation for CAD validation. Engineering judgment involves understanding design intent, manufacturability, and assembly feasibility - things that are hard to codify in validation rules. We use automation for basic checks like file integrity, required properties, and standard compliance. But critical design reviews still need experienced eyes. A validation script can’t tell you if a tolerance stack-up will cause assembly problems or if a design choice will create maintenance issues downstream.
We went full automation two years ago and haven’t looked back. The key is building comprehensive validation rules that capture engineering knowledge. Yes, initial setup took six months to encode all the validation logic, but now we process 500+ CAD files daily with 98% accuracy. The 2% that fail automated checks get flagged for manual review. This hybrid approach gives you speed plus safety net.
Don’t forget the maintenance burden of automated validation. Rules need constant updates as design standards evolve, new part types are introduced, or validation requirements change. We started with 50 validation rules, now have over 200, and spend significant time maintaining them. Manual review processes are more adaptable - you can brief reviewers on new requirements without reprogramming. There’s a trade-off between automation efficiency and flexibility that depends on how stable your validation criteria are.
Having implemented CAD validation strategies across multiple organizations, here’s a comprehensive framework that addresses this debate:
Automated Validation Tools:
Automation excels at objective, repeatable checks. Implement automated validation for:
- File integrity and format compliance (can the file be opened, is it corrupt)
- Required property population (part number, description, material, mass)
- Naming convention compliance (file names match standards)
- Geometry basic checks (closed volumes, surface continuity, model completeness)
- Relationship validation (required BOM structures exist, correct parent-child links)
- Standard compliance (company templates used, approved materials selected)
- Revision history completeness (prior versions exist, proper succession)
These checks are binary pass/fail and don’t require engineering judgment. Automated validation should run on every CAD file import or update, providing immediate feedback.
Manual Review Process:
Human review is essential for subjective assessments:
- Design intent verification (does the design meet functional requirements)
- Manufacturability assessment (can this be produced with available processes)
- Assembly feasibility (will components fit together as intended)
- Tolerance analysis (will accumulated tolerances cause problems)
- Alternative design evaluation (are there better approaches)
- Risk identification (what could go wrong in production or field use)
- Cross-functional impact (how does this affect other systems or teams)
These require experience, context, and judgment that automation cannot replicate.
Hybrid Validation Strategies:
The optimal approach combines both:
-
Tiered Validation: All CAD data goes through automated validation first. Only data that passes automation proceeds to manual review. This eliminates wasting engineer time on files with basic errors.
-
Risk-Based Routing: Automated system assigns risk scores based on:
- Component criticality (safety-critical, customer-facing, high-cost)
- Design novelty (new design vs. revision of existing)
- Complexity metrics (part count, feature count, assembly levels)
- Change magnitude (minor tweak vs. major redesign)
High-risk items get mandatory manual review, low-risk items get sampled review.
-
Intelligent Sampling: For high-volume standard parts, automate validation fully but implement statistical sampling for manual review. Review 10% of automated-passed items to verify validation rules are working correctly and catch edge cases.
-
Continuous Learning: Track validation failures and root causes. When manual reviewers find issues that automation missed, create new validation rules to catch similar issues automatically in future. This progressively shifts more validation from manual to automated over time.
-
Context-Aware Automation: Modern validation tools can incorporate engineering knowledge through configurable rule engines. Work with experienced engineers to codify their expertise into validation rules. For example, a rule might check that fastener spacing meets assembly tool clearance requirements - this is engineering judgment made repeatable through automation.
Implementation recommendation: Start with automated validation covering basic quality gates (file integrity, required properties, standards compliance). This typically catches 60-70% of CAD data issues with minimal setup. Simultaneously, maintain manual review for all new designs and critical components. Over 6-12 months, analyze manual review findings and progressively build automation rules for recurring issues. Target state is 80% validation automated, 20% requiring human judgment, with the automated portion handling routine checks and the manual portion focused on high-value design assessment.
The data quality and efficiency balance comes from using each approach for what it does best: automation for consistency and speed, human review for judgment and insight.