TY - JOUR
T1 - Screw Slot Quality Inspection System Based on Tactile Network
AU - Chen, Yan Chun
AU - Hwang, Ren Hung
AU - Chen, Mu Yen
AU - Wen, Chih Chin
AU - Hsu, Chih Ping
N1 - Publisher Copyright:
© 2021 Association for Computing Machinery.
PY - 2021/11
Y1 - 2021/11
N2 - The popularity of 5G networks has made smart manufacturing not limited to high-Tech industries such as semiconductors due to its high speed, ultra-high reliability, and low latency. With the advance of system on chip (SoC) design and manufacturing, 5G is also suitable for data transmission in harsh manufacturing environments such as high temperatures, dust, and extreme vibration. The defect of the screw head is caused by the wear and deformation of the die forming the head after mass production. Therefore, the screw quality inspection system based on the tactile network in this article monitors the production quality of the screw; the system will send a warning signal through the router to remind the technician to solve the production problem when the machine produces a defective product. Sensors are embedded into the traditional screw heading machine, and sensing data are transmitted through a gateway to the voluntary computing node for screw slot quality inspection. The anomaly detection data set collected by the screw heading machine has a ratio of anomaly to normal data of 0.006; thus, we propose a time-series deep AutoEncoder architecture for anomaly detection of screw slots. Our experimental results show that the proposed solution outperforms existing works in terms of efficiency and that the specificity and accuracy can reach 97% through the framework proposed in this article.
AB - The popularity of 5G networks has made smart manufacturing not limited to high-Tech industries such as semiconductors due to its high speed, ultra-high reliability, and low latency. With the advance of system on chip (SoC) design and manufacturing, 5G is also suitable for data transmission in harsh manufacturing environments such as high temperatures, dust, and extreme vibration. The defect of the screw head is caused by the wear and deformation of the die forming the head after mass production. Therefore, the screw quality inspection system based on the tactile network in this article monitors the production quality of the screw; the system will send a warning signal through the router to remind the technician to solve the production problem when the machine produces a defective product. Sensors are embedded into the traditional screw heading machine, and sensing data are transmitted through a gateway to the voluntary computing node for screw slot quality inspection. The anomaly detection data set collected by the screw heading machine has a ratio of anomaly to normal data of 0.006; thus, we propose a time-series deep AutoEncoder architecture for anomaly detection of screw slots. Our experimental results show that the proposed solution outperforms existing works in terms of efficiency and that the specificity and accuracy can reach 97% through the framework proposed in this article.
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U2 - 10.1145/3423556
DO - 10.1145/3423556
M3 - Article
AN - SCOPUS:85116207875
VL - 21
JO - ACM Transactions on Internet Technology
JF - ACM Transactions on Internet Technology
SN - 1533-5399
IS - 4
M1 - 3423556
ER -