What question did this study set out to answer?

The research aims to create a predictive framework for identifying surface defects in particleboard production.

February 2, 2026Open Access

Predictive Surface Defect Detection in Particleboard Manufacturing using Defect Tracking Matrix–Principal Component Analysis Framework toward Zero Defect Manufacturing

Key Points

The research aims to create a predictive framework for identifying surface defects in particleboard production.
Integrated Defect Tracking Matrix (DTM) with Principal Component Analysis (PCA)
Evaluation of twenty technical attributes
Analysis of sixteen quality defects
Data-driven approach in manufacturing context
Key contributors to defects include duct cleaning and blower settings
Press calibration and blade sharpening significantly affect defect patterns
Improved traceability aids in achieving Zero Defects Manufacturing

Abstract

Zero Defects Manufacturing (ZDM) is a proactive quality strategy aimed at preventing defects during production. This study proposes a novel integrated method using the Defect Tracking Matrix (DTM) and Principal Component Analysis (PCA) to predict the sources of surface defects in particleboard manufacturing. The authors evaluated twenty technical attributes and sixteen quality defects. Results showed that duct cleaning, setting the blower, screen cleaning, press calibration, and blade sharpening were key contributors to detecting patterns. The DTM- PCA framework improves traceability and helps implement ZDM through structured, data-driven analysis in a previously unexplored context.

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