Abrasive Grain Diamond Wire Saw Inspection system based on Exposure Fusion and Deep Learning

• Main Authors: LU, PO-HSUAN, 盧柏瑄
• Other Authors: HO, CHAO-CHING
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/ye6frz

碩士 === 國立臺北科技大學 === 製造科技研究所 === 107 === This thesis develops a set of optical inspection system applying on diamond wire saw, which can test and observe with graphical user interface and acquire key parameters such as abrasive distribution, abrasive protrusion, wire diameter and coating thickness by analyzing collected data. During detecting surface abrasives, the most challenging factor is the surface of the plated diamond wire may have different reflection coefficients in different sections. Moreover, the top of the surface of arc is tend to over-exposed, while the exposure intensity at the wire edge is rather low. Hence, the none-constant exposure condition is the main cause of inaccurate surface abrasive detection. Therefore, this system employs the exposure fusion method with combining deep learning method. Therefore, camera response to curve calibration is no longer required. By taking photos with different exposure time, and superimpose the images with different indicators to get a clear and evenly exposed image of diamond wire saw. And then the flaw detection results were compared with the influence of different reflection condition, different source of light and different exposure time based on deep learning model. And further comparison between the area of prediction mark and the manual mark overlaps, the ratio of the overflow, the recall rate, and the accuracy were conducted. The experimental result shows that the proposed optical inspection system of the diamond abrasive grain line has high accuracy and reproducibility. The recall and precision rate of deep learning detection result achieve 93%. Through the exposure fusion, the identified features are similar to human judgment.