Details

Autor(en) / Beteiligte

• Li, Yu-Ting
• Kuo, Paul
• Guo, Jiun-In

Titel

Automatic Industry PCB Board DIP Process Defect Detection System Based on Deep Ensemble Self-Adaption Method

Ist Teil von

• IEEE transactions on components, packaging, and manufacturing technology (2011), 2021-02, Vol.11 (2), p.312-323

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2021

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• A deep ensemble convolutional neural network (CNN) model to inspect printed circuit board (PCB) board dual in-line package (DIP) soldering defects with Hybrid-YOLOv2 (YOLOv2 as a foreground detector and ResNet-101 as a classifier) and Faster RCNN with ResNet-101 and Feature Pyramid Network (FPN) (FRRF) achieved a detection rate of 97.45% and a false alarm rate (FAR) of 20%-30% in the previous study <xref ref-type="bibr" rid="ref34">[34] . However, applying the method to other production lines, environmental variations, such as lighting, orientations of the sample feeds, and mechanical deviations, led to the degradation in detection performance. This article proposes an effective self-adaption method that collects "exception data" like the samples with which the Artificial Intelligent (AI) model made mistakes from the automated optical inspection inference edge to the training server, retraining with exceptions on the server and deploying back to the edge. The proposed defect detection system has been verified with real tests that achieved a detection rate of 99.99% with an FAR 20%-30% and less than 15 s of inspection time on a resolution <inline-formula> <tex-math notation="LaTeX">7296 \times 6000 </tex-math></inline-formula> PCB image. The proposed system has proven capable of shortening inspection and repair time for online operators, where a 33% efficiency boost from the three production lines of the collaborated factory has been reported <xref ref-type="bibr" rid="ref6">[6] . The contribution of the proposed retraining mechanism is threefold: 1) because the retraining process directly learns from the exceptions, the model can quickly adapt to the characteristic of each production line, leading to a fast and reliable mass deployment; 2) the proposed retraining mechanism is a necessary self-service for conventional users as it incrementally improves the detection performance without professional guidance or fine-tuning; and 3) the semiautomatic exception data collection method helps to reduce the time-consuming manual labeling during the retraining process.