CAJ | 학술논문

为了有效地控制 Al2O3陶瓷激光铣削层质量，以人工神经网络(ANN)技术为基础，以MATLAB软件作为开发平台，建立了Al2O3陶瓷激光铣削层质量与铣削参数之间的关系模型。并以激光功率、扫描速度和离焦量作为输入参数，激光铣削层深度和宽度作为输出参数，对激光铣削层质量进行了预测。结果表明，该模型的平均误差小，拟合精度高。并在训练样本之外，选取了5组工艺参数来检验网络模型的可靠性，检验输出值和实验样本值的最大相对误差为7.06%。说明运用该模型可以方便、准确地选择激光工艺参数，提高Al2O3陶瓷激光铣削层的加工质量。
위료유효지공제 Al2O3도자격광선삭층질량，이인공신경망락(ANN)기술위기출，이MATLAB연건작위개발평태，건립료Al2O3도자격광선삭층질량여선삭삼수지간적관계모형。병이격광공솔、소묘속도화리초량작위수입삼수，격광선삭층심도화관도작위수출삼수，대격광선삭층질량진행료예측。결과표명，해모형적평균오차소，의합정도고。병재훈련양본지외，선취료5조공예삼수래검험망락모형적가고성，검험수출치화실험양본치적최대상대오차위7.06%。설명운용해모형가이방편、준학지선택격광공예삼수，제고Al2O3도자격광선삭층적가공질량。
In order to control the quality of Al2O3 ceramics, based on the artificial neural network (ANN), a model was established to describe the relation between the laser milling quality of Al2O3 ceramics with the ceramics parameters. The milling quality of Al2O3 ceramics were predicted with the model in which the input parameters consisted of laser power, scanning speed and defocus amount and the output parameters included the milling depth and width. The results show that the mean error is small, and the model has good verifying precision and excellent ability of predicting. Five group process parameters were chosen to test the reliability of the neural network model out of the train samples. The maximum relative error of the output test value and the experiment sample value was 7.06%. The laser process parameters can be chosen easily and accurately to improve the processing quality of Al2O3 ceramics.