Research On Some Key Issues Of Bayesian Learning Framework Based Nonlinear Manufacturing Process Modeling And Multiobjective Optimization

Multi-objective optimization and modeling of nonlinear process are two significant and closely relational research topics in manufacturing. Machine learning and computational intelligence based nonlinear process modeling approach, only using discrete sample data, could generate multiple responses hypersurface, where it may be hardly to establish an exact or even approximate mathematical relationship between the input and output variables. Due to its flexibility and simplicity, extensive nonlinear process optimizations are applied in manufacturing. Compared with other methods, the general Bayesian learning framework approach denotes all forms of uncertainty with probability. The predictive model can be encoded prior knowledge to avoid overfitting, and realizing the inference and learning process through the Bayesian theorem, and supporting the variance based on the model representation, and also providing theories for model selection. Therefore the nonlinear process modeling under the Bayesian learning framework and multi-objective optimization of nonlinear process research has theoretical and economic significance.The main contents of this dissertation are focused on nonlinear manufacturing process with few noisy samples and realtime scenario. Some key issues of nonlinear process modeling at the perspective of probability measure and under Bayesian statistical learning framework, and multi-objective optimization are studied. This dissertation makes some creative researches on the following aspects:1. A general multi-objective optimization framework of nonlinear manufacturing process in systemic perspective firstly is described. The framework is constituted by five components including the prior knowledge or prior model about nonlinear process, the sampling strategy and data preprocessing, data-driven based nonlinear process modeling and model verification and model-based multi-objective optimization and its control, as well as Pareto solutions decision-making method. The systemic view can be used for the systematic analysis of the existing optimization system, and also can be used to generate a tuple of components into a new integrated system for specific nonlinear process optimization.2. According to the framework mentioned above, a hybrid intelligent approach based on relevance vector machine (RVM) and genetic algorithm (GA) has been developed for optimal control of parameters of nonlinear manufacturing processes. Modeling method based on the sparse Bayesian learning could make the predictive model with competitive generalization, as well as it has state of the art sparseness, which could be more esaily to realize the real-time optimization system. Applied above optimization paradigm, as a case study, the optimization of control parameters of seed separator system is used for evaluating the proposed intelligent approach. The experimental results show the effectiveness of the proposed hybrid approach. Compared with other learning algorithm, in the nonlinear system identification, the number of relevance vectors of RVM keeps sparseness and stability after the relevance vectors have ability to describe the distribution of the dataset, which is very favorable to fast search process of genetic algorithm for time consuming of fitness computation and the finding of near-optimal control parameters.3. Through extending conventional Gaussian kernel function in kernel based machine learning, a novel adaptive spherical Gaussian kernel is utilized by RVM for more sparseness predictive model in nonlinear process regression. The new class of kernel function has indepentent kernel width of relevance vector corresponding to problem space. For this new kernel function, a stagewise optimization algorithm for maximizing Bayesian evidence in sparse Bayesian learning framework is proposed for model selection. The attractive ability of this approach is to automatically choose the right kernel widths locally fitting RVs from the training dataset, which could keep right level smoothing at each scale of signal. Extensive empirical study, on artificial and real-world benchmark datasets and EDM process modeling, shows its effectiveness and flexibility of model on representing regression problem with higher levels of sparsity and better performance than classical RVM. Therefore, it is more suitable to real-time application.4. For small and nosiy samples modeling of nonlinear process, reliable multi-objective optimization based on Gaussian process regression (GPR) is developed to optimize the WEDM-HS process, GPR based modeling methods make the model more suitable for modeling the characteristics of manufacturing process, and the model has competitive generalization in small samples, as well as state of the art prediction accuracy. Objective functions of predictive reliability multi-objectives optimization are built by probabilistic variance of predictive response used as empirical reliability measurement and responses of GPR models. Finally, the cluster class centers of Pareto front are the optional solutions to be chosen. Experiments on WEDM-HS are conducted to evaluate the proposed intelligent approach in terms of optimization process accuracy and reliability. The experimental result shows that GPR models have the advantage over other regressive models in terms of model accuracy and feature scaling and probabilistic variance. Given the regulable coefficient parameters, the experimental optimization and optional solutions show the effectiveness of controlling optimization process to acquire more reliable optimum predictive solutions.5. For solving the contradiction in nonlinear process modeling with prior knowledge from field experts and serious shortage of process samples, and focusing on the fusion between rough fuzzy system and very scarce noisy samples, a effective re-sampling algorithm based on piecewise relational transfer interpolation is presented and it is integrated with GPR to improve modeling accuracy. An empirical study on two benchmark and WEDM datasets demonstrates the feasibility and effectiveness of this approach. The experimental result shows that combining very rough fuzzy prior model with training examples still significantly improves the predictive performance of WEDM process modeling, even with very limited training dataset. That is, given the generalized prior model, the samples needed by GPR model could be reduced greatly meanwhile keeping precise. Since this method is independent of the fuzzy model, which also applies to other intelligent model of fusiion.6. Oriented manufacturing process, the architecture of nonlinear optimization system and the function of each module is developed to provide a good research platform for further development of advanced modeling and optimization algorithms. Matlab procedures as a model and algorithms processing the core computing components are integrated to .net framework.