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Abstract: This paper proposes new approach for modeling of various processes in metal-forming industry. As an example, we demonstrate the use of genetic programming (GP) for modeling of forming efficiency. The forming efficiency is a basis for determination of yield stress which is the fundamental characteristic of metallic materials. Several different genetically evolved models for forming efficiency on the basis of experimental data for learning were discovered. The obtained models (equations) differ in size, shape, complexity and precision of solutions. In one run out of many runs of our GP system the well-known equation of Siebel was obtained. This fact leads us to opinion that GP is a very powerful evolutionary optimization method appropriate not only for modeling of forming efficiency but also for modeling of many other processes in metal-forming industry.
Keywords: metal forming, yield stress, forming efficiency, mathematical modeling, adaptation, genetic methods, genetic algorithm, genetic programming, artificial intelligence, process optimisation
Published in DKUM: 01.06.2012; Views: 2210; Downloads: 121
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Abstract: The demands for complicated products have increased dramatically over the last few years taking into consideration the utilisation of sheet metal, product quality and process conditions. For reliable product development and stable production process, the use of FEM is necessary. One of the most significant parameters in the sheet metal forming process is the blank holding force. In the research work, the optimisation of the blank holding force was performed with the help of FEM analysis. For the optimisation the geometry and the structure of the blank holder was optimised. The best results were obtained with flexible, segmented blank holders, which enables wider technological window for good parts.
Keywords: sheet metal forming, deep drawing, segmented holding system, finite elements method, optimization
Published in DKUM: 01.06.2012; Views: 2139; Downloads: 87
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Abstract: The purpose of this research is the determination of the optimal cold forward extrusion parameters with the minimization of tool load as objective. This paper deals with different optimization approaches in order to determine optimal values of logarithmic strain, die angle and friction factor with the purpose to find minimal tool loading obtained by cold forward extrusion process. Two experimental plans based on factorial design of experiment and orthogonal array have been carried out. Classical optimization, according to the response model of extrusion forming force, and the Taguchi approach are presented. The obtained extrusion force model as the fitness function was used to carry out genetic algorithm optimization. Experimental verification of optimal forming parameters with their influences on the forming forces was also performed. The experimental results show an improvement in the minimization of tool loading. The results of optimal forming parameters obtained with different optimization approaches have been compared and based on that the characteristics analysis (features and limitations) of presented techniques.
Keywords: metal forming, forward extrusion force optimization, design of experiments, Taguchi approach, genetic algortihm
Published in DKUM: 31.05.2012; Views: 2028; Downloads: 99
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Abstract: In the paper, an approach completely different from the conventional methods for determination of accurate models for the change of properties of cold formed material, is presented. This approach is genetic programming (GP) method which is based on imitation of natural evolution of living organisms. The main characteristic of GP is its non-deterministic way of computing. No assumptions about the form and size of expressions were made in advance, but they were left to the self organization and intelligence of evolutionary process. First, copper alloy rods were cold drawn under different conditions and then impact toughness of cold drawn specimens was determined by Charpy tests. The values of independent variables (effective strain, coefficient of friction) influence the value of the dependent variable, impact toughness. On the basis of training data, different prediction models for impact toughness were developed by GP. Only the best models, gained by genetic programming were presented in the paper. Accuracy of the best models was proved with the testing data set. The comparison between deviation of genetic model results and regression model results concerning the experimental results has showed that genetic models are more precise and more varied then regression models.
Keywords: metal forming, genetic programming, evolutionary computing, impact toughness, copper alloy, modelling
Published in DKUM: 30.05.2012; Views: 2547; Downloads: 109
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