CAJ | 학술논문

为了有效地控制激光铣削层质量，建立了激光铣削层质量(铣削层宽度、铣削层深度)与铣削层参数(激光功率、扫描速度和离焦量)的BP神经网络预测模型。采用粒子群算法优化了BP神经网络的权值和阈值，构建了基于粒子群神经网络的质量预测模型。所提出的PSO-BP算法解决了一般BP算法迭代速度慢，且易出现局部最优的问题，并以Al2O3陶瓷激光铣削质量预测为例，进行算法实现。仿真结果表明：提出的PSO-BP算法迭代次数大大减少，且预测误差明显减少。所构建的质量预测模型具有较高的预测精度和实用价值。
위료유효지공제격광선삭층질량，건립료격광선삭층질량(선삭층관도、선삭층심도)여선삭층삼수(격광공솔、소묘속도화리초량)적BP신경망락예측모형。채용입자군산법우화료BP신경망락적권치화역치，구건료기우입자군신경망락적질량예측모형。소제출적PSO-BP산법해결료일반BP산법질대속도만，차역출현국부최우적문제，병이Al2O3도자격광선삭질량예측위례，진행산법실현。방진결과표명：제출적PSO-BP산법질대차수대대감소，차예측오차명현감소。소구건적질량예측모형구유교고적예측정도화실용개치。
In order to effectively control the quality of laser milling layer, BP neural network model was established between the laser milling quality of layer (the milling layer width, milling depth) and the milling layer parameters (laser power, scanning speed and defocus amount). Using particle swarm optimization (PSO)BP neural network weights and thresholds, quality prediction model based on particle swarm neural network was built. The proposed PSO-BP algorithm solve the problem that the general BP algorithm iteration speed was slow, and prone to local optimum . Al2O3 ceramics laser milling quality prediction model was taben to realize the algorithm.The simulation results show that the number of iterations of proposed PSO-BP algorithm, and the prediction error are greatly reduced. The built quality prediction model has high prediction accuracy and practical value.