Abstract

With the advancements in IIoT tools and applications, real-time monitoring systems with remote access capabilities are gaining great interest. The use of these instruments in monitoring manufacturing processes can lead to enhanced performance and increased efficiency. In this paper, a real-time monitoring system using Raspberry Pi is developed for monitoring the maximum temperature and axial force during friction stir spot welding (FSSW). There are two sensors used in this experiment: a thermocouple and a load cell connected to a microcontroller via Node-RED. Through the use of the system and response surface methodology (RSM), three factors (tool rotation speed, plunge rate, and dwell time) were examined in relation to maximum temperatures and axial forces during FSSW. Models of maximum welding temperature and force were constructed based on multivariate regression and adaptive network fuzzy inference system (ANFIS). The monitoring system proved effective at measuring and tracking temperature and axial force in real time across multiple platforms.

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