Automated Anthropometric Measurement System for CNC Machine Workspace Design

Abstract

Prior research conducted in India emphasized a significant occurrence of musculoskeletal pain, leading to investigations into the joint position sense and working posture of CNC machine operators. However, there has been insufficient focus on the control panel in these studies. This research endeavours to automatically record anthropometric parameters and assess them in conjunction with ergonomic data charts. Ergonomic details of the control panel were obtained using Ultrasonic sensors, MPU6050 gyro sensor, and load cell sensor, with data processing conducted through a Raspberry Pi microcontroller using Python code. A Taguchi L9 array design of the experiment, with three factors and three levels, was employed for ergonomic analysis, keeping operator height constant. ANOVA identified the significant weighted factor of load on the control panel, and regression analysis produced a mathematical model to assess individual parameter contributions to the required responses. Optimal values for control panel height, operator working distance, and control panel angle (120 cm, 30 cm, and 90°, respectively) were determined through Main effect Plots for Means and S-N ratios. ANOVA confirmed the statistical significance of these parameters with P values below 0.05, indicating their significance in response variation. The accuracy of the developed regression model was 86.11% (R2), demonstrating a high confidence level. RULA Analysis in Catia-v5 software revealed an acceptable score of 2 when the angles of the left hand's arm and forearm were at 52.47° and 172.43°, respectively, and the right-hand arm and forearm angles were at 52.47° and 153.82°, with a viewing angle of 167.42°. The study focused on adjusting CNC control panel ergonomic parameters to mitigate the risk for operators, suggesting potential future work involving artificial intelligence for additional ergonomic adjustments in CNC machine operations.