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.

中文翻译：

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基于触觉网络的螺丝槽质量检测系统

5G网络的普及，使得智能制造以其高速、超高可靠性、低时延等优势，不再局限于半导体等高科技行业。随着片上系统（SoC）设计和制造的进步，5G也适用于高温、灰尘和极端振动等恶劣制造环境中的数据传输。螺钉头的缺陷是由于批量生产后形成头部的模具磨损变形造成的。因此本文采用基于触觉网络的螺丝质量检测系统对螺丝的生产质量进行监控；当机器产生不良品时，系统会通过路由器发出警告信号，提醒技术人员解决生产问题。传感器嵌入到传统的螺丝打头机中，传感数据通过网关传输到自愿计算节点进行螺丝槽质量检测。螺旋打头机采集的异常检测数据集异常与正常数据之比为0.006；因此，我们提出了一种用于螺钉槽异常检测的时间序列深度自动编码器架构。我们的实验结果表明，所提出的解决方案在效率方面优于现有工作，并且通过本文提出的框架，特异性和准确性可以达到 97%。我们提出了一种时间序列深度自动编码器架构，用于螺丝槽的异常检测。我们的实验结果表明，所提出的解决方案在效率方面优于现有工作，并且通过本文提出的框架，特异性和准确性可以达到 97%。我们提出了一种时间序列深度自动编码器架构，用于螺丝槽的异常检测。我们的实验结果表明，所提出的解决方案在效率方面优于现有工作，并且通过本文提出的框架，特异性和准确性可以达到 97%。