Internet-of-Things Based Turning Machine Tool Temperature Monitoring System


  • Li Shen Andy Liew Universiti Tun Hussein Onn Malaysia
  • Prof. Dr. Yusri bin Yusof Universiti Tun Hussein Onn Malaysia


Turning, Cutting tool tip temperature, Cutting speed, Depth of cut, Flank wear, MLX90614-BAA, NodeMCU ESP8266, IoT-based sensor


During metal turning (lathing) operations, it is important to study the factors which will affect the tool life. One of the factors is cutting tool temperature as the process is directly related to high cutting tool temperature. The heat energy generated in the primary shear zone and at the tool-workpiece interface will reduce the tool life of the cutting tool, by wearing down the tool tip or causing flank wear. A study was set up to analyse how the temperature at cutting tool tip can be affected by the cutting parameters such as cutting speed, depth of cut and feed rate. Then, the temperature between new tool and a worn tool will be compared to find the influence of flank wear on cutting tool temperature. These measurements are relevant because they can be used to validate the relationship between cutting parameters, tool condition, and cutting temperature. An infrared temperature sensor, MLX90614-BAA series, is connected to the NodeMCU ESP8266 development board with Wi-Fi module, to make an Internet-of-Things based sensor system to measure the temperature. The system is first programmed using Arduino IDE software and then be connected to BLYNK application on mobile devices to monitor the live temperature data during turning process. Results show that the cutting tool tip temperature increases with cutting speed, which is the same as with the depth of cut. Analysis of the average and peak temperature comparison between new tool and worn tool reveals an increment of 10% and above, indicating that worn tool will have higher temperature overall. With an effective IoT based temperature monitoring system, the condition of cutting tool can be easily observed by looking at the cutting temperature to ensure fast detection of worn tool condition.




How to Cite

Andy Liew, L. S., & Yusof, Y. (2022). Internet-of-Things Based Turning Machine Tool Temperature Monitoring System. Research Progress in Mechanical and Manufacturing Engineering, 3(1), 124–137. Retrieved from