Twardowski Paweł, Wiciak-Pikuła Martyna
Faculty of Mechanical Engineering and Management, Poznan University of Technology, 3 Piotrowo St., 60-965 Poznan, Poland.
Materials (Basel). 2019 Sep 22;12(19):3091. doi: 10.3390/ma12193091.
The ability to effectively predict tool wear during machining is an extremely important part of diagnostics that results in changing the tool at the relevant time. Effective assessment of the rate of tool wear increases the efficiency of the process and makes it possible to replace the tool before catastrophic wear occurs. In this context, the value of the effectiveness of predicting tool wear during turning of hardened steel using artificial neural networks, multilayer perceptron (MLP), was checked. Cutting forces and acceleration of mechanical vibrations were used to monitor the tool wear process. As a result of the analysis using artificial neural networks, the suitability of individual physical phenomena to the monitoring process was assessed.
在加工过程中有效预测刀具磨损的能力是诊断的一个极其重要的部分，这会导致在相关时间更换刀具。对刀具磨损速率的有效评估提高了加工过程的效率，并使得在灾难性磨损发生之前更换刀具成为可能。在此背景下，检验了使用人工神经网络、多层感知器（MLP）预测淬硬钢车削过程中刀具磨损有效性的价值。切削力和机械振动加速度被用于监测刀具磨损过程。通过使用人工神经网络进行分析，评估了各个物理现象对监测过程的适用性。
