Abstract
Most electronic and automotive parts are affixed by bolts. To prevent such bolts from loosening through shock and vibration, anti-loosening coating is applied to their threads. However, during the coating process, various defects can occur. Consequently, as the quality of the anti-loosening coating is critical for the fastening force, bolts are inspected optically and manually. It is difficult, however, to accurately screen coating defects owing to their various shapes and sizes. In this study, we applied deep learning to assess the coating quality of bolts with anti-loosening coating. From the various convolutional neural network (CNN) methods, the VGG16 structure was employed. Furthermore, the gradient-weighted class activation mapping visualization method was used to evaluate the training model; this is because a CNN cannot determine the classification criteria or the defect location, owing to its structure. The results confirmed that external factors influence the classification. We, therefore, applied the region of interest method to classify the bolt thread only, and subsequently, retrained the algorithm. Moreover, to reduce the learning time and improve the model performance, transfer learning and fine tuning were employed. The proposed method for screening coating defects was applied to a screening device equipped with an actual conveyor belt, and the Modbus TCP protocol was used to transmit signals between a programmable logic controller and a personal computer. Using the proposed method, we were able to automatically detect coating defects that were missed by optical sorters.
Highlights
Automobiles, trains, and large electronic products are made by combining various parts
May become unfastened owing to vibration or shock, which can be hazardous where safetycritical parts are combined using the bolt fastening technique
In products such as Nylock, a liquid nylon coating is applied between the bolt threads, thereby increasing the frictional graft force and the fastening power
Summary
Automobiles, trains, and large electronic products are made by combining various parts. These combinations, in turn, require the use of techniques such as welding, assembly, and bolt fastening. May become unfastened owing to vibration or shock, which can be hazardous where safetycritical parts are combined using the bolt fastening technique. To resolve this problem, in products such as Nylock, a liquid nylon coating is applied between the bolt threads, thereby increasing the frictional graft force and the fastening power. Fine turning was conducted to achieve high performance and reduce the learning time, with weighted values previously obtained from large data volumes beilneagranpinpglietidmteo, lwowitherwdeaitgahvteodluvmaelusetsopinrecvreioasuesltyheobqtuaainlietyd. from large data volumes being applied to lower data volumes to increase the quality
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