The prediction of the building precision in the Laser Engineered Net Shaping process using advanced networks

• Published in: Optics and lasers in engineering Vol. 48; no. 5; pp. 519 - 525
• Main Authors: Lu, Z.L., Li, D.C., Lu, B.H., Zhang, A.F., Zhu, G.X., Pi, G.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.05.2010; Elsevier
• Subjects: Algorithms; Back propagation; Biological and medical applications; Coefficients; Depositing height; Exact sciences and technology; Fundamental areas of phenomenology (including applications); Industrial applications; Laser Engineered Net Shaping; Lasers; Least square support vector machine; Lenses; Networks; Nonlinearity; Optics; Physics; Support vector machines; Technology parameter; Laser Engineered Net Shaping; Technology parameter; Least square support vector machine; Depositing height; Learning; Real time systems; Powders; Algorithms; Laser assisted processing; Manufacturing; Laser beam applications
• ISSN: 0143-8166; 1873-0302
• DOI: 10.1016/j.optlaseng.2010.01.002

Laser Engineered Net Shaping (LENS) is an advanced manufacturing technology, but it is difficult to control the depositing height (DH) of the prototype because there are many technology parameters influencing the forming process. The effect of main parameters (laser power, scanning speed and powder feeding rate) on the DH of single track is firstly analyzed, and then it shows that there is the complex nonlinear intrinsic relationship between them. In order to predict the DH, the back propagation (BP) based network improved with Adaptive learning rate and Momentum coefficient (AM) algorithm, and the least square support vector machine (LS-SVM) network are both adopted. The mapping relationship between above parameters and the DH is constructed according to training samples collected by LENS experiments, and then their generalization ability, function-approximating ability and real-time are contrastively investigated. The results show that although the predicted result by the BP-AM approximates the experimental result, above performance index of the LS-SVM are better than those of the BP-AM. Finally, high-definition thin-walled parts of AISI316L are successfully fabricated. Hence, the LS-SVM network is more suitable for the prediction of the DH.