A Novel Data Mining Approach for Defect Detection in the Printed Circuit Board Manufacturing Process

• Autorzy: Blanka Bártová , Vladislav Bína
• Języki publikacji: EN

Abstrakty

EN

This research aims to propose an effective model for the detection of defective Printed Circuit Boards (PCBs) in the output stage of the Surface-Mount Technology (SMT) line. The emphasis is placed on increasing the classification accuracy, reducing the algorithm training time, and a further improvement of the final product quality. This approach combines a feature extraction technique, the Principal Component Analysis (PCA), and a classification algorithm, the Support Vector Machine (SVM), with previously applied Automated Optical Inspection (AOI). Different types of SVM algorithms (linear, kernels and weighted) were tuned to get the best accuracy of the resulting algorithm for separating good-quality and defective products. A novel automated defect detection approach for the PCB manufacturing process is proposed. The data from the real PCB manufacturing process were used for this experimental study. The resulting PCALWSVM model achieved 100 % accuracy in the PCB defect detection task. This article proposes a potentially unique model for accurate defect detection in the PCB industry. A combination of PCA and LWSVM methods with AOI technology is an original and effective solution. The proposed model can be used in various manufacturing companies as a postprocessing step for an SMT line with AOI, either for accurate defect detection or for preventing false calls. (original abstract)

Słowa kluczowe

EN

Quality management; Production quality; Production process

PL

Zarządzanie jakością; Jakość produkcji; Proces produkcji

• Czasopismo: Engineering Management in Production and Services
• Rocznik: 2022
• Numer: nr 2
• Strony: 13--25

Twórcy

autor

Blanka Bártová

• University of Economics in Prague, Czech Republic

autor

Vladislav Bína

• University of Economics in Prague, Czech Republic

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Identyfikatory

DOI

https://doi.org/10.2478/emj-2022-0013