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Decision support system for tool condition monitoring in milling process using artificial neural network
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
doi:10.36909/jer.9621
fatcat:rg7qnxmwnndstnrxzqhugr57aq