CAE-Based Robust Optimization for Deep-Drawing Process of Sheet Metal Forming Using Grey Relational Analysis Method

The theory of grey systems is a new technique for performing prediction, relational analysis and decision making in many areas. The grey relational analysis was used to optimize the deep-drawing process parameters with considerations of the multiple response (the wrinkle, crack and thinning variation). The deep-drawing parameters, such as the blank holding force (F_ bh ), the radii of punch and die (R_1,R_2), the coefficients of friction (μ_1,μ_2,μ_3) are considered. An orthogonal array is used for the experimental design. The multiple response values are obtained making use of finite element analysis (FEA). Optimal process parameters are determined by the grey relational grade obtained from the grey relational analysis for multi-performance characteristics (the wrinkle, crack and the thinning). The analysis of variance (ANOVA) for the grey relational grade is implemented. The results show that the quality of stamped parts can be improved effectively through the new approach. The grey relational analysis can be applied in sheet metal forming.

Finite Element Optimization of Vee-ring Factor in Fine-blanking Process

The vee-ring is one of the most characteristic features of the fine-blanking process which is derived from the conventional blanking. With the vee-ring, a counter plate and some other working parameters, a component with a precise geometry and smoothly blanked surface can be produced without any major secondary operations. However, these working parameters are always empirically determined or determined by trial-and-error method, which is time-consuming and expensive. In this study, a numerical simulation method was used to analyze the effect of vee-ring factors on product quality, such as distance between vee-ring and punch, vee-ring height and blank holder force, so as to obtain as high clean cut ratio as possible. During the simulation, the model was assumed as axisymmetric one and the workpiece was considered as rigid plastic material, meanwhile the tools were defined as rigid bodies so as to shorten the computational time. A damage model taking into account the influence of hydrostatic stress was used to simulate material fracture in fine-blanking. The result obtained from the simulation indicated that with the help of vee-ring, the metal material near the shear band does not flow with the punch penetration and high compressive stress in the shear band is generated. Consequently, higher clean cut ratio can be obtained with a vee-ring model than the one obtained with a plane blank holder model. Furthermore, the longer the distance between vee-ring and punch is, the higher the height of roll-over becomes.

Finite Element Analysis for Multi-pass Equal Channel Angular Pressing

Equal channel angular pressing (ECAP) is an attractive process method to produce bulk ultra-fine grained materials. There are many experiment evidences showing that the nature of the microstructural evolution in multi-pass ECAP depends on process routes. Isothermal three dimensional FEM simulations for multi-pass ECAP were performed using DEFORM3D finite element code. The material model of 6061Al-T6 was employed. Flow nets, effective strain distribution in the workpiece and loads during multi-pass ECAP using different routes were analysed respectively. The simulations show process routes influence material flow and effective strain distribution in the workpiece obviously but have few influence on loads.

KEY TECHNOLOGY IN INTELLIGENT CONTROL FOR SHEET METAL DEEP DRAWING

Intellectualization of sheet metal in deep drawing is a new combined technology, which is concerned with control science and computer science and sheet metal forming theory. The intelligent control system for sheet metal deep drawing consists of four fundamental factors: real time measurement, identification, prediction and control. Real time identification of material properties and friction coefficient is the most important factor in the whole system. An artificial neural network model for identification of the material properties and friction coefficient was established according to deep drawing characteristics and more automation. The identification of the material properties and friction coefficient was realized.

Structural Multi-objective Probabilistic Design for Six Sigma

Uncertainties in engineering design may lead to low reliable solutions that also exhibit high sensitivity to uncontrollable variations. In addition, there often exist several conflicting objectives and constraints in various design environments. In order to obtain solutions that are not only "multi-objectively" optimal, but also reliable and robust, a probabilistic optimization method was presented by integrating six sigma philosophy and multi-objective genetic algorithm. With this method, multi-objective genetic algorithm was adopted to obtain the global Pareto solutions, and six sigma method was used to improve the reliability and robustness of those optimal solutions. Two engineering design problems were provided as examples to illustrate the proposed method.