Smart CAD Analysis
Industry
Manufacturing
Client
DIAB Group
Team
BUILD
Year
2024
We were hired to automate the CAD error detection and eliminate the manual file inspection bottleneck that was causing production delays and material waste.
Challenge
DIAB struggled with DXF and CNC file inconsistencies including missing measurements, misaligned parts, and discrepancies between 2D and 3D designs. These issues caused production inaccuracies, costly adjustments, delays, and required extensive manual inspections that reduced material efficiency.
Approach
Conducted systematic analysis of DXF and CNC files using Python-based libraries to map drawing objects to part numbers and identify six key error types (missing measurements, detached dimension annotations, part drawing discrepancies).
Implemented polygon analysis algorithms to calculate overlap with reference polygons, assessing part accuracy and identifying missing/mismatched parts across multiple file layers for CNC machining precision.
Developed AI-enhanced error detection models that automatically flag common issues like missing measurements and misaligned parts, eliminating manual quality checks.
Created cutting and nesting layout optimization algorithms (including graph-based and Monte Carlo methods) to improve part orientations, reduce material waste, and lower production costs.
Built layer-specific analysis capabilities to differentiate between file layers ("UNITS," "CUTTING LINE," "NESTING LAYOUT") and detect subtle discrepancies in part alignment.
Architecture/Backend
Python pipeline to ingest DXF/CNC files; polygon-overlap computations.
AI/ML models
I-enhanced error-detection models that auto-flag issues plus optimization methods.
Infrastructure/Deployment
Implemented in pre-manufacturing QA workflow.
Key results
Achieved 95% accuracy in cut angle detection and 90% accuracy in cut position identification within panels, dramatically improving manufacturing precision.
Automated error detection eliminated manual CAD file reviews, streamlining the review process from hours to minutes.
Proactive identification of file inconsistencies before manufacturing now prevents costly production errors and material waste.
Enhanced material usage efficiency through optimized cutting and nesting layouts, reducing waste and lowering production costs
