Iot-Enabled Retrofitting Of A Conventional Lathe Machine For Real-Time Cutting Force And Acoustic Emission Monitoring Under Industry 4.0 Framework

Sheetal S.  Soni; Akash B.  Pandey; Suraj Thulaseedharan  Nair; Urvish Patel

doi:10.29284/907ta402

Authors

Sheetal S. Soni Assistant Professor, Department of Mechanical Engineering, Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India
Akash B. Pandey Assistant Professor, Department of Mechanical Engineering, Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India
Suraj Thulaseedharan Nair Research Scholar, Department of Mechanical Engineering, Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India
Urvish Patel Assistant Professor, Department of Mechanical Engineering, Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India

DOI:

https://doi.org/10.29284/907ta402

Keywords:

Industry 4.0 · IoT retrofitting, Cutting force monitoring, Acoustic emission, MPU6050, InfluxDB, Grafana, Smart manufacturing.

Abstract

The advent of Industry 4.0 technologies presents significant opportunities for upgrading obsolete manufacturing equipment without incurring the high capital costs typically associated with complete machine replacement. This study elaborates on the design, execution, and experimental validation of a budget-friendly Internet of Things (IoT) monitoring system retrofitted onto a conventional general-purpose lathe machine. The system includes an MPU6050 MEMS accelerometer and a sound-level sensor attached directly to the cutting tool shank, with data collection facilitated by an ATmega2560/ESP8266 hybrid microcontroller board. The sensor data—cutting force (derived from the measured acceleration) and acoustic emission (sound pressure level in dB)—is transmitted in real-time via Wi-Fi to a local InfluxDB time-series database and visualized on a Grafana dashboard with a refresh rate of 5 seconds. A detailed factorial experiment comprising 64 trials was performed on a mild steel (MS) workpiece utilizing a High Speed Steel (HSS) tool across four spindle speeds (185, 280, 420, 640 RPM), four feed rates (0.46, 0.71, 1.05, 1.62 mm/s), and four depths of cut (0.25, 0.50, 0.75, 1.00 mm). The findings reveal that both cutting force and sound level increase consistently with all three machining parameters. Spindle speed has the most substantial impact, with the average cutting force rising from 525.93 N at 185 RPM to 1118.22 N at 640 RPM under the same feed and depth conditions. A multiple linear regression model for Cutting Force successfully predicts experimental results with a mean absolute percentage error of 3.13%, confirming the model's reliability. The proposed framework demonstrates a cost-effective and scalable strategy for small and medium-sized enterprises (SMEs) to achieve continuous process monitoring and predictive maintenance capabilities within the paradigm of Industry 4.0.

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