Data-driven Soft Computing Modeling And Optimization With Applications To Material Engineering

One important problem in material science and engineering is the computational design of material, that's to predict properties through theoretical calculations, or optimize compositions and process parameters of material with prescribed properties. However, for most industrial materials, it's difficult to design compositions and processes from physical principle. So, a practical alternative is empirical design,that is to develop data-driven models directly from industrial data. For traditional statistical analyses could not deal with the complexity in material data, the soft computing has been used in material modeling and optimization recently, and its applications in material are becoming a new trend in multi-discipline researches. Therefore, the researches on data-driven soft computing modeling and optimization could provide new practical methods, and then enrich methodology in material design theory。The goal of this work is to develop a systematic data-driven modeling and optimization framework for empirical design of material, so it focuses on solving the following problems. First, an attempt is made to improve the generability of neural network and the interpretability of fuzzy system. Then, combining empirical models with improved optimization algorithms, a systematic data-driven soft computing modeling and optimization framework is to be developed. And finally, all of the above would be validated in material processing practices based on industrial data.After systematic researches on the data-driven modeling and optimization, thesis's main theoretical contributions are as following. Firstly, using Bayesian complexity regularization for weight decay in the error back-propagation learning procedure, differential evolution method is used in determination of optimal multilayer perceptrons networks with better approximation and generalization. Secondly, an adaptive neuro-fuzzy system consisted of reduced interpretable Takagi-Sugeno type inference rules is also proposed to improve the predictability and generalization. In fuzzy inference system, the structure identification and parameter optimization are carried out automatically and efficiently by the combined use of fuzzy clustering, adaptive back-propagation learning, and similarity analysis-based model simplification. Thirdly, instead of binary string encoding, more quickly genetic algorithms with improved genetic operators are developed, in which digits vary over the numbers 0, 1, 2, . . . 9 and fuzzy controllers are also used to adapt the crossover and mutation. A hybrid particle swarm optimization (PSO) exhibiting higher success rates is developed. The hybrid PSO utilizes fuzzy control method and differential evolution algorithm to determine the appropriate set of parameters during the optimization. At the same time, bacterial foraging optimization (BFO) is also applied to the material processing. And finally, a systematic modeling and fuzzy multi-objects optimization framework is proposed, in which by the use of fuzzy multi-attributes material design method one could describe uncertain and imprecise information in materials.In practices, integrating adaptive neruo-fuzzy inference models into optimization procedure, a number of industrial problems related to material processing have been solved. With operating rules generated directly from cupola melting experimental data, a set of neuro-fuzzy inference models has been developed to determine the optimal blast and carbon rate for the lowest energy consumption with fuzzy constrained tap temperature and melting rate. After modeling the correlation between mechanical properties and compositions of ZL101A cast aluminum alloy, with the optimized compositions, the ultimate tensile strength and elongation are controlled within the range of 250(10MPa and 3.0(0.5%。Based on solution treatment experiments of A319 cast aluminum alloy, neuro-fuzzy models have been obtained to describe the relationship between mechanical properties and solution parameters. Combined the fuzzy models with optimization pr...