Teleoperating and monitoring of CNC turning machine under web framework

Abbas, Tahseen Fadhil; Shounia, Yousif K.; Shwaish, Read R.

doi:10.1063/5.0000152

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…(6,7) Hence, considerable research has been conducted to develop monitoring devices with IoT-based systems in machine tools. (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) Kuntoğlu et al reviewed an indirect tool condition monitoring system. They noted that installing multiple sensors during the development of the tool condition monitoring system can make the system prediction more accurate and sensitive.…”

Section: Introductionmentioning

confidence: 99%

Development of Low-cost Wireless Monitoring System and Its Implementation on Computer Numerical Control Machine Tool

Royandi,

Effendi,

Komara

et al. 2024

Sensors and Materials

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We propose a low-cost wireless monitoring system and describe its initial deployment on a computer numerical control (CNC) machine tool as a data acquisition (DAQ) and collection device. It employs a piezoelectric sensor that can detect and analyze signals corresponding to the deflection of the tool holder and reaction force of the cutting process. The sensor readings are acquired wirelessly to prevent interference with the cutting chip. The wireless system was constructed with commonly accessible hardware, such as the Arduino Due and nRF24L01 wireless module. A Windows Forms application can then use a serial communication interface to process and collect data. In addition, the program includes a data storage system, data processing that displays signal properties (maximum and minimum amplitudes, root mean square, and frequency), data visualization that displays data in the time and frequency domains, and an alert function through the mobile application WhatsApp. The low-cost monitoring system, with an estimated hardware cost of approximately USD 225, can achieve a sample rate of approximately 350 samples/s. The feasibility was evaluated by a comparative study. Final validation tests in the machining process demonstrated that the monitoring system exhibited good agreement with an existing DAQ device and sensor, with a maximum root-mean-square error (RMSE) of approximately 2.16% of the measured vibration features.Iman Apriana Effendi currently is a lecturer at the Manufacturing Design Engineering Department of Politeknik Manufaktur Bandung, West Java, Indonesia. His research interests include machine tool design, intelligent machining and manufacturing, and robotics design.

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