Abstract
Vibration is essential for achieving an optimal surface quality and enhancing tool longevity. Minimizing this vibration is essential to mitigate its detrimental effects on both the tool and the workpiece, hence enhancing the overall finish of the workpiece. The aim of the present study is to mitigate the vibrations experienced by the EN-8 workpiece during the milling process. The machine has installed the MPU-6050 sensor to conduct the experiments. The utilized sensor is the MPU-6050, featuring a six-axis configuration comprising a three-axis gyroscope and a three-axis accelerometer. It possesses motion tracking capabilities and is coupled with a Raspberry Pi unit and a monitor for displaying the data on a computer screen. The cutting speed, depth of cut, and feed rate were selected as input parameters, and an L9 orthogonal array was built using the design of experiments methodology. MATLAB is utilized to obtain a non-linear model and to fit vibration readings through the curve-fitting extension of the application, which is employed in examining various regression models of quadratic depth along with their corresponding “R-square” values and error. This process ultimately aids in assessing the model's appropriateness and facilitates a comparison between regression models derived through different methodologies. The goal function that had the best “R-square” value was then added to the particle swarm optimization Python code to study the milling results and find the best cutting settings that produced the least vibrations and surface roughness. The algorithm analyzed the vibration and surface roughness data using curve fitting to find the best cutting limits for both measurements. The optimal cutting conditions of 99.932 speed, 0.9253 depth, and 50.163 feed rate resulted in 0.96152 Hz vibration, while 30.00 speed, 0.50 depth, and 10.00 feed rate gave 2.27 µm surface roughness.
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