激光与红外
激光與紅外
격광여홍외
LASER & INFRARED
2014年
8期
861-865
,共5页
激光焊接%径向基函数神经网络%多目标优化%模拟退火算法
激光銲接%徑嚮基函數神經網絡%多目標優化%模擬退火算法
격광한접%경향기함수신경망락%다목표우화%모의퇴화산법
laser welding%radial basis function neural network%multi-objective optimization%simulated annealing algo-rithm
激光焊接过程产生的焊斑熔深和热影响区宽度直接影响焊接质量。激光焊接过程复杂,影响因素众多,许多参数难以量化。本文以TC4钛合金薄板为实验样品进行脉冲激光焊接实验。利用两个径向基函数神经网络分别预测焊斑熔深和热影响区宽度。将上述两个径向基函数神经网络作为多目标优化算法的目标函数,以提高焊接熔深并减小热影响区宽度。通过模拟退火算法寻求多目标优化所得的非劣解集中的最优解。实验证明,该方法可有效平衡激光焊接过程的焊斑熔深和热影响区宽度。
激光銲接過程產生的銲斑鎔深和熱影響區寬度直接影響銲接質量。激光銲接過程複雜,影響因素衆多,許多參數難以量化。本文以TC4鈦閤金薄闆為實驗樣品進行脈遲激光銲接實驗。利用兩箇徑嚮基函數神經網絡分彆預測銲斑鎔深和熱影響區寬度。將上述兩箇徑嚮基函數神經網絡作為多目標優化算法的目標函數,以提高銲接鎔深併減小熱影響區寬度。通過模擬退火算法尋求多目標優化所得的非劣解集中的最優解。實驗證明,該方法可有效平衡激光銲接過程的銲斑鎔深和熱影響區寬度。
격광한접과정산생적한반용심화열영향구관도직접영향한접질량。격광한접과정복잡,영향인소음다,허다삼수난이양화。본문이TC4태합금박판위실험양품진행맥충격광한접실험。이용량개경향기함수신경망락분별예측한반용심화열영향구관도。장상술량개경향기함수신경망락작위다목표우화산법적목표함수,이제고한접용심병감소열영향구관도。통과모의퇴화산법심구다목표우화소득적비렬해집중적최우해。실험증명,해방법가유효평형격광한접과정적한반용심화열영향구관도。
Depth of weld penetration and heat affected zone width are very important to laser welding quality.Laser welding is a complicated process,and quantization analysis of this process is quite difficult.In this work,a set of TC4 titanium alloy thin plate specimens were used as laboratory samples.Two radial basis function neural network (RBFNN)models were used to predict weld penetration depth and heat affected zone width.In order to minimize the heat affected zone width and maximize the depth of weld penetration,the above two neural networks were used as ob-jective functions of multi-objective optimization algorithm.A simulated annealing algorithm is used to find the optimal solution within non-inferior solutions of the multi-objective optimization algorithm.The results show that the heat af-fected zone width and the depth of weld penetration are well balanced by this method.