Combined analysis of acoustic emission and vibration signals in monitoring tool wear, surface quality and chip formation when turning SCM440 steel using MQL

With modern production, Minimum Quantity Lubricant (MQL) technology has emerged as an alternative to conventional liquid cooling. The MQLs is environmentally friendly lubricant method with low cost while meeting the requirements of machining conditions. In this study, experimental and analytical results show that obtained acoustic emission (AE) vibration signal components can effectively monitor various circumstances in SCM440 steel turning process MQL, such surface quality chip formation cutting tool AE signals showed a significant response wear processes. contrast, excellent ability reflect roughness during MQL. through mode parameters (cutting speed, feed depth cut) was detected analysis amplitude Ax, Ay Az signal. Finally, Gaussian regression adaptive neuro-fuzzy inference systems (GPR-ANFIS) algorithms were combined predict MQL process. Tool condition monitoring devices assist operator limits, stopping machine case imminent breakage or lower quality. unique combination model training testing samples established by data, corresponding average prediction accuracy 97.57 %. highest error not more than 3.8 %, confidence percentage 98 proposed be used industry tools directly