Small Modulus Injection Gear Size Inspection Method Based on Super Resolution

被引：3

作者：

Jiang, Kuosheng ^{[1
]}

Liu, Hao ^{[1
]}

Chang, Yasheng ^{[2
,3
,4
]}

机构：

[1] Anhui Univ Sci & Technol, State Key Lab Min Response & Disaster Prevent & Co, Huainan 232000, Anhui, Peoples R China

[2] Suzhou City Univ, Sch Opt & Elect Informat, Suzhou 215104, Peoples R China

[3] Suzhou City Univ, Suzhou Key Lab Biophoton, Suzhou 215104, Peoples R China

[4] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian, Peoples R China

来源：

IEEE SENSORS JOURNAL | 2024年 / 24卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Gears; Inspection; Superresolution; Generative adversarial networks; Image enhancement; Injection molding; Visualization; Deep learning; generating adversarial network (GAN); size inspection; small modulus injection gear; super resolution (SR); NETWORKS;

D O I：

10.1109/JSEN.2024.3390033

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Accurate gear parameter inspection technology can effectively improve the quality of gear production. In order to obtain more accurate detection results, the quality of the detection image must be controlled. However, there is a lack of optimization of image accuracy in the existing gear size detection methods. Consequently, this article proposes a super-resolution (SR)-based dimensional detection method for small modulus injection molding gears. The method introduces an improved SR algorithm for small modulus injection molding gears, constructs a size detection system, effectively enhances the quality of the image of the measured gear through image processing, and then calculates the value of the measured gear's precision parameters according to the definition of gear precision parameters. The experimental results show that the standard deviation of the bore diameter and the toothed circle diameter are 0.756 and 1.531 mu m respectively, and the repeat errors of the repeated measurements are less than 2 mu m. Therefore, the research results of this article provide a new detection method for related industries, which is of positive significance for improving production efficiency and quality.

引用

页码：18646 / 18658

页数：13

共 30 条

[1] Tiny Machine Learning for High Accuracy Product Quality Inspection [J].