<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

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

Engineering Management in Production and Services

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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...

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

Online veröffentlicht: 11. Juli 2022

Seitenbereich: 13 - 25

Eingereicht: 12. Dez. 2021

Akzeptiert: 05. Mai 2022

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

Schlüsselwörter
quality management, defect detection, AOI, PCA, PCB, SVM

© 2022 Blanka Bártová et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Online veröffentlicht: 11. Juli 2022

Seitenbereich: 13 - 25

Eingereicht: 12. Dez. 2021

Akzeptiert: 05. Mai 2022

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

Schlüsselwörterquality management, defect detection, AOI, PCA, PCB, SVM

© 2022 Blanka Bártová et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
quality management, defect detection, AOI, PCA, PCB, SVM