Work

Hybrid Rule-Based Classification and Defect Detection System Using Insert Steel Multi-3D Matching

Year: 2025

Type: article

Abstract: This paper presents an integrated three-dimensional (3D) quality inspection system for mold manufacturing that addresses critical industrial constraints, including zero-shot generalization without retraining, complete decision traceability for regulatory compliance, and robustness under severe data shortages (<2% defect rate). Dual optical sensors (Photoneo MotionCam 3D and SICK Ruler) are integrated via affine transformation-based registration, followed by computer-aided design (CAD)-based classification using geometric feature matching to CAD specifications. Unsupervised defect detection combines density-based spatial clustering of applications with noise (DBSCAN) clustering, curvature analysis, and alpha shape boundary estimation to identify surface anomalies without labeled training data. Industrial validation on 38 product classes (3000 samples) yielded 99.00% classification accuracy and 99.12% macroscopic precision, outperforming Point-MAE (93.24%) trained under the same limited-data conditions. The CAD-based architecture enables immediate deployment via CAD reference registration, eliminating the five-day retraining cycle required for deep learning, essential for agile manufacturing. Processing time stability (0.47 s compared to 43.68 s for Point-MAE) ensures predictable production throughput. Defect detection achieved 98.00% accuracy on a synthetic validation dataset (scratches: 97.25% F1; dents: 98.15% F1).

Source: Electronics

DOI: 10.3390/electronics14234701

Authors: Soon Woo Kwon, Hae Gwang Park, Seung Ki Baek, Min Young Kim

Institution: Kyungpook National University

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896