The Experts below are selected from a list of 347949 Experts worldwide ranked by ideXlab platform
Weijaw Deng - One of the best experts on this subject based on the ideXlab platform.
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Process Parameter optimization for mimo plastic injection molding via soft computing
Expert Systems With Applications, 2009Co-Authors: Wenchi Che, Peihao Tai, Weijaw DengAbstract:Determining optimal Process Parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding (PIM) industry. Previously, production engineers used either trial-and-error method or Taguchi's Parameter design method to determine optimal Process Parameter settings for PIM. However, these methods are unsuitable in present PIM because the increasing complexity of product design and the requirement of multi-response quality characteristics. This research presents an approach in a soft computing paradigm for the Process Parameter optimization of multiple-input multiple-output (MIMO) plastic injection molding Process. The proposed approach integrates Taguchi's Parameter design method, back-propagation neural networks, genetic algorithms and engineering optimization concepts to optimize the Process Parameters. The research results indicate that the proposed approach can effectively help engineers determine optimal Process Parameter settings and achieve competitive advantages of product quality and costs.
Y S Tarng - One of the best experts on this subject based on the ideXlab platform.
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Process Parameter selection for optimizing the weld pool geometry in the tungsten inert gas welding of stainless steel
Journal of Materials Processing Technology, 2002Co-Authors: S C Juang, Y S TarngAbstract:Abstract In this paper, the selection of Process Parameters for obtaining an optimal weld pool geometry in the tungsten inert gas (TIG) welding of stainless steel is presented. Basically, the geometry of the weld pool has several quality characteristics, for example, the front height, front width, back height and back width of the weld pool. To consider these quality characteristics together in the selection of Process Parameters, the modified Taguchi method is adopted to analyze the effect of each welding Process Parameter on the weld pool geometry, and then to determine the Process Parameters with the optimal weld pool geometry. Experimental results are provided to illustrate the proposed approach.
Qi Zhou - One of the best experts on this subject based on the ideXlab platform.
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robust optimization for reducing welding induced angular distortion in fiber laser keyhole welding under Process Parameter uncertainty
Applied Thermal Engineering, 2018Co-Authors: Qi Zhou, Seungkyum ChoiAbstract:Abstract Welding-induced angular distortion is a typical out-of-plane distortion, which brings negative effects on the joints’ quality. Therefore, the selection of appropriate Process Parameters to minimize or control welding-induced distortion under uncertainty has become of critical importance. In this paper, a robust Process Parameter optimization framework is proposed to reduce welding-induced distortion in fiber laser keyhole welding under Parameter uncertainty. Firstly, a three-dimensional thermal-mechanical finite element model (FEM) for simulating the welding-induced distortion is developed and validated by laser welding experiment. Secondly, a Gaussian Process (GP) model is constructed to build the relationship between the input Process Parameters and output responses. Finally, uncertainty quantification of both Process Parameter uncertainty and GP model uncertainty is derived. The obtained uncertainty quantification formulas are used in the robust optimization problem to minimize welding-induced distortion. The effectiveness and reliability of the obtained robust optimum are verified by the Monte Carlo method.
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a multi fidelity information fusion metamodeling assisted laser beam welding Process Parameter optimization approach
Advances in Engineering Software, 2017Co-Authors: Qi Zhou, Longchao Cao, Yang Yang, Ping Jiang, Xinyu Shao, Zhongmei Gao, Chaochao WangAbstract:A multi-fidelity (MF) metamodel assisted welding Parameter optimization is proposed.A 3D thermal finite element model is developed as a low-fidelity model.A laser welding physical experiment is taken as the high-fidelity model.The MF metamodel is built based on two different levels fidelity information fusion.Verification experiments illustrated the reliability of the final optima. Selecting reasonable laser beam welding (LBW) Process Parameters is very helpful for obtaining a good welding bead profile and hence a high quality of the welding joint. Existing Process Parameter optimization approaches for LBW either based on cost-expensive physical experiments or low-fidelity (LF) computer simulations. This paper proposes a multi-fidelity (MF) metamodel based LBW Process Parameter optimization approach, in which different levels fidelity information, both from LF computer simulations and high-fidelity (HF) physical experiments can be integrated and fully exploited. In the proposed approach, a three-dimensional thermal finite element model is developed as the LF model, which is fitted with a LF metamodel firstly. Then, by taking the LF metamodel as a base model and scaling it using the HF physical experiments, a MF metamodel is constructed to approximate the relationships between the LBW Process Parameters and the bead profile. Two metrics are adopted to compare the prediction accuracy of the MF metamodel with the single-fidelity metamodels solely constructed with physical experiments or computer simulations. Results illustrate that the MF metamodel outperforms the single-fidelity metamodels both in global and local accuracy. Finally, the fast elitist non-dominated sorting genetic algorithm (NSGA-II) is used to facilitate LBW Process Parameter space exploration and multi-objective Pareto optima search. LBW verification experiments verify the effectiveness and reliability of the obtained optimal Process Parameters.
Wenchi Che - One of the best experts on this subject based on the ideXlab platform.
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Process Parameter optimization for mimo plastic injection molding via soft computing
Expert Systems With Applications, 2009Co-Authors: Wenchi Che, Peihao Tai, Weijaw DengAbstract:Determining optimal Process Parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding (PIM) industry. Previously, production engineers used either trial-and-error method or Taguchi's Parameter design method to determine optimal Process Parameter settings for PIM. However, these methods are unsuitable in present PIM because the increasing complexity of product design and the requirement of multi-response quality characteristics. This research presents an approach in a soft computing paradigm for the Process Parameter optimization of multiple-input multiple-output (MIMO) plastic injection molding Process. The proposed approach integrates Taguchi's Parameter design method, back-propagation neural networks, genetic algorithms and engineering optimization concepts to optimize the Process Parameters. The research results indicate that the proposed approach can effectively help engineers determine optimal Process Parameter settings and achieve competitive advantages of product quality and costs.
S C Juang - One of the best experts on this subject based on the ideXlab platform.
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Process Parameter selection for optimizing the weld pool geometry in the tungsten inert gas welding of stainless steel
Journal of Materials Processing Technology, 2002Co-Authors: S C Juang, Y S TarngAbstract:Abstract In this paper, the selection of Process Parameters for obtaining an optimal weld pool geometry in the tungsten inert gas (TIG) welding of stainless steel is presented. Basically, the geometry of the weld pool has several quality characteristics, for example, the front height, front width, back height and back width of the weld pool. To consider these quality characteristics together in the selection of Process Parameters, the modified Taguchi method is adopted to analyze the effect of each welding Process Parameter on the weld pool geometry, and then to determine the Process Parameters with the optimal weld pool geometry. Experimental results are provided to illustrate the proposed approach.
