Chun-Yin Wu

Shape design for heat conduction problems using curvilinear grid generation, conjugate gradient, and redistribution methods

This study is concerned with the application of an approach combining the curvilinear grid generation and conjugate gradient (CG) methods for shape design of heat conduction problems. Shape design for a conductive medium that contains a heating object and features an isothermal outer surface is investigated. Several practical cases with multiply-connected domains associated with various imbedded heating objects and thermal boundary conditions are studied to demonstrate the performance of the approach. Optimal shape profiles that meet different heat transfer requirements are obtained. Meanwhile, a redistribution method is proposed to numerically regularize the ill-ordered grids, which are commonly found during the iterative optimization process. By using the redistribution method, the locations of the nodal points at the outer surface may be adjusted automatically once the ill-ordered grid patterns are detected, so that the convergence of the optimization process can be facilitated.

The influence of die shape on the flow deformation of extrusion forging

Abstract The flow deformation of extrusion forging is similar to that of the initial pre-forging of closed-die forging and is frequently found in the forming process of high strength parts. Therefore, understanding the influence of the die shapes on metal flow deformation will be beneficial in designing a forging die or in determining the parameters of the forging process. In this paper, the finite element method was used to analyze the influence of die shapes with different draft angles and fillet radii on the extrusion forging deformation. Experiments using two sets of dies with different shapes were performed, and the results were compared with the predictions of the finite element method for the same deformation mode. Thus, the results of the deformation analysis in this study were extended to the design of a more complicated pre-forging die and in the planning of a forming process. This would be beneficial for the forging industry.

Topology optimization of structures using ant colony optimization

A modified ACO algorithm that derives from specific definition of pheromone and cooperation mechanism between ants was applied for solving topology optimization problem of structure. Mesh topology of finite element model for structure was treated as possible paths for ants movement. A tour on mesh topology map for seeking food finished by ant is transformed into a structure and the finite element method was applied to analyze the structure for calculating pheromone deposited on the path. The amount of accumulated pheromone deposited on every element by different ants was used to find optimum structural design. From the results studied in this paper, the purposed ACO algorithm provides as alternate optimization method that has high potential in finding the best design for topology optimization of structure successfully and efficiently.

Artificial Immune Regulation (AIR) for Model-Based Fault Diagnosis

In this paper, a novel approach to immune model-based fault diagnosis methodology for nonlinear systems is presented. An immune-model based fault diagnosis architecture including forward/inverse immune model identification, the residual generation, fault alarm concentration (FAC), and artificial immune regulation (AIR). In this work, the artificial immune regulation was developed to diagnose the failures. A two-link manipulator simulation was employed to validate the effectiveness and robustness of the diagnosis approach. The results show that it can detect and isolate actuator faults, sensor faults and system component faults simultaneously.

Art-enhanced modified binary differential evolution algorithm for optimization

Differential evolution (DE) is a heuristic optimization method with a relatively simple and efficient form of mutation and crossover and it has been applied to solve many real world optimization problems in real-valued search space. Modified binary differential evolution (MBDE) with a simple binary mutation mechanism based on a logical operation is suitable for dealing with binary and continuous optimization problems. In this study, the modified binary differential evolution is enhanced by using adaptive resonance theory (ART) to classify binary image pattern of population into groups for balancing exploration and exploitation in optimization search. The diversity and convergence of search are both enhanced by applying ART clustering strategy. Different types of optimization problems consisting of test function optimization and topology optimization of structure are used to illustrate the high viability of the proposed algorithm in optimization.

Engineering Optimization Using Modified Binary Differential Evolution Algorithm

Differential evolution (DE) is a heuristic optimization method used to solve many optimization problems in real-valued search space. It has the advantage of incorporating a relatively simple and efficient form of mutation and crossover. However, the operator of DE is primarily based on floating-point representation only and is difficult to apply to binary-based optimization problems. In this paper, a modified binary differential evolution with a simple and new binary mutation mechanism based on a logical operation is proposed. The developed binary mutation strategy is suitable for dealing with discrete and parametric engineering optimization problems. Two different types of engineering problems consisting of optimal shape design of heat bonder and topology optimization of structure are used to illustrate the high viability of the proposed algorithm in engineering optimization.

Prediction and Design of the Optimal Punch Shape for Recess Forging

In this study, the finite element method was used to analyze comprehensively the effects of punch shape on forming the forging recess. Then, the polynomial network and genetic algorithm were combined to construct the predicted and designed system. Through this approach, we can predict the forging results formed from arbitrary shaped punches, and design the optimal punch shape for forging recess. Through the interactive verifies of modeling repeatedly, the errors resulted from modeling analysis, network prediction and genetic algorithm optimal design are extremely limited. Consequently, the predicted and designed approach of optimal punched shape for forming recess in this study could be extended to the design of more complicated and difficult formed forging die.

Optimizaton using clonal selection algorithm and immune memory based on self-organizing map

In this work an approach of integration of clonal selection algorithm and immune memory based on self-organizing map(SOM) is presented to solve optimization problem. Immune memory lays the foundation for a rapid and massive secondary response of immune system. Management of immune memory is important for improving performance and quality of optimum search using immune algorithm. The adaptive functionality of SOM is applied for emulation of the dynamic behavior of immune memory. From results obtained using proposed approach SOM-based management of immune memory can keep balance between exploration and exploitation for good solution quality and search performance. Besides SOM can improve the clonal selection algorithm in performance for multi-modal optimization search.