| Summary: | 碩士 === 國立高雄第一科技大學 === 系統資訊與控制研究所 === 101 === During machining of a machining tool, the wearing degrees of tools dominate machining precision. A tool needs to be replaced when precision of machined workpiece indicating out of tolerances of acceptable quality level; however, it has wasted the rejected workpieces and the machining resource before the broken tool being replaced. Hence, if the wearing degree of a tool can be effectively estimated and the broken tool can be predicted and replaced in time, the overall machining efficiency of machine tools can be improved.
This work integrates machining features and intelligent algorithms to propose a tool wearing estimation scheme for predicting tool residual life. In feature analysis, the candidate features covering time, frequency, and time-frequency domains are derived and transformed from the signals of the tool vibration and spindle current during various operations. For feature selection, we use the non-dominated sorting Genetic Algorithm II (NSGA-II) to select the key features of tool wearing while minimizing the three objectives key feature numbers, mean absolute errors, and the maximum error. For wearing estimation, the back propagation neural network (BPNN) is utilized to estimate the wearing degree of tools. Finally, the estimated wearing degree is applied to a linear regression for estimating the residual life of the tool.
In the results, the mean average error of tool wearing estimation is 0.0106 mm under six different machining conditions (including workpiece material, ratio of spindle speed, ratio of feed rate, and cutting depth) with 72 workpieces in a three-axis CNC machine. In addition, the feasible range of the tool residual life can be derived based on the specific conditions.
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