Manufacturing Process Modeling and Optimization Based on Multi-Layer Perceptron Network

It has been shown that a manufacturing process can be modeled (learned) using Multi-Layer Perceptron (MLP) neural network and then optimized directly using the learned network. This paper extends the previous work by examining several different MLP training algorithms for manufacturing process modeling and three methods for process optimization. The transformation method is used to convert a constrained objective function into an unconstrained one, which is then used as the error function in the process optimization stage. The simulation results indicate that: (i) the conjugate gradient algorithms with backtracking line search outperform the standard BP algorithm in convergence speed; (ii) the neural network approaches could yield more accurate process models than the regression method; (iii) the BP with simulated annealing method is the most reliable optimization method to generate the best optimal solution, and (iv) process optimization directly performed on the neural network is possible but cannot be especially automated totally, especially when the process concerned is a mixed integer problem.

[1]  Yoshio Hirose,et al.  Backpropagation algorithm which varies the number of hidden units , 1989, International 1989 Joint Conference on Neural Networks.

[2]  László Monostori,et al.  Artificial neural networks in intelligent manufacturing , 1992 .

[3]  Wendelin Feiten,et al.  Improving neural net training by mathematical optimization , 1992 .

[4]  Charles Stark,et al.  Enhanced Training Algorithms, and Integrated Training/Architecture Selection for Multilayer Perceptron Networks , 1992 .

[5]  T. Warren Liao.  MLP neural network models of CMM measuring processes , 1996, J. Intell. Manuf..

[6]  George Chryssolouris,et al.  A Comparison of Statistical and AI Approaches to the Selection of Process Parameters in Intelligent Machining , 1990 .

[7]  Robert H. Storer,et al.  Off-line multiresponse optimization of electrochemical surface grinding by a multi-objective programming method , 1992 .

[8]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[9]  Roberto Battiti,et al.  First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.

[10]  T. Warren Liao,et al.  A neural network approach for grinding processes: Modelling and optimization , 1994 .

[11]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[12]  Etienne Barnard,et al.  Optimization for training neural nets , 1992, IEEE Trans. Neural Networks.

[13]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[14]  Chuck Zhang,et al.  Implementation and Comparison of Three Neural Network Learning Algorithms , 1993 .

[15]  S.S. Rangwala,et al.  Learning and optimization of machining operations using computing abilities of neural networks , 1989, IEEE Trans. Syst. Man Cybern..

[16]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[17]  Martin G. Bello,et al.  Enhanced training algorithms, and integrated training/architecture selection for multilayer perceptron networks , 1992, IEEE Trans. Neural Networks.

[18]  Ming-Kuen Chen,et al.  Neural network modelling and multiobjective optimization of creep feed grinding of superalloys , 1992 .

[19]  Tsu-Shuan Chang,et al.  A universal neural net with guaranteed convergence to zero system error , 1992, IEEE Trans. Signal Process..