A Study of Implementing a Predictive Maintenance Software System for Conventional Machine Based on Internet of Things (IOT)

Abstract

A predictive maintenance system is a form of maintenance system which can predict the need to maintain assets at a certain future period. The Internet of Things system may benefit manufactures from the data collection to advance the current manufacturing process which allowed devices to monitor physical processes. This paper is aiming to develop low-cost predictive maintenance system with an Internet of Things feature sensor to predict the condition of cutting tool and time decision on change the cutting tool to ensure the quality of products that allowing the tool life of cutting tool to be optimized. It is also studying the potential opportunity for install features to upgrade the old conventional machine. The operating cutting temperature of the cutting tool in metal cutting is influenced by cutting factors particularly during the machining operation. As the cutting temperature greatly affects the tool life, it is important to monitor an increase in cutting temperature using reliable techniques. In this study, two conditions of cutting tool whereas new tool with zero flank wear and a worn tool with 0.51mm flank wear was used. Average cutting temperature and maximum cutting temperature were investigated by placing a non-contact infrared MLX90614 temperature sensor. The data acquisition has been done with PLX-DAQ software and live monitoring system with Blynk application through ESP8266 NodeMCU controller board with Arduino IDE software. Experiments were performed and cutting temperature was recorded as well as results have been analysed. The correlation between cutting parameters and cutting temperature is clearly noticed. The predictive maintenance software system based on IoT technology with cutting temperature had a successful build-up to collect the data information of cutting temperature with a data acquisition system.