Decision support system for tool condition monitoring in milling process using artificial neural network

.Mohanraj T, Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore. Amrita Vishwa Vidyapeetham, India, Tamilvanan A., Department of Mechanical Engineering, Kongu Engineering College, Erode
2021 Maǧallaẗ al-abḥāṯ al-handasiyyaẗ  
This work discusses the development of tool condition monitoring system (TCMs) during milling of AISI stainless steel 304 using sound pressure and vibration signals. Response Surface Methodology (RSM) was used to design the experiments. The various milling parameters and vegetable-based cutting fluids (VBCFs) were optimized to reduce the surface roughness and flank wear. The experimental results reveal the direct relationship between the flank wear and sound and vibration signals. The various
more » ... atistical parameters were extracted from the measured signals and given as input data to train the artificial neural network (ANN). From the developed ANN model, the flank wear was predicted with the mean squared error (MSE) of 0.0656 mm.
doi:10.36909/jer.9621 fatcat:rg7qnxmwnndstnrxzqhugr57aq