An effective warpage optimization method in injection molding based on the Kriging model

Gao, Yansha; Wang, Xicheng

doi:10.1007/s00170-007-1044-6

Cited by 114 publications

(54 citation statements)

References 14 publications

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“…Warpage become important to improve the quality of that particular design concept. Warpage is caused by uneven shrinkage of plastic parts [5]. Meanwhile, shrinkage in the mould happens is caused by thermal changes of the plastics while it is in the mould.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Shrinkage on Thick Plate Part using Genetic Algorithm

et al. 2016

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Abstract. Injection moulding is the most widely used processes in manufacturing plastic products. Since the quality of injection improves plastic parts are mostly influenced by process conditions, the method to determine the optimum process conditions becomes the key to improving the part quality. This paper presents a systematic methodology to analyse the shrinkage of the thick plate part during the injection moulding process. Genetic Algorithm (GA) method was proposed to optimise the process parameters that would result in optimal solutions of optimisation goals. Using the GA, the shrinkage of the thick plate part was improved by 39.1% in parallel direction and 17.21% in the normal direction of melt flow.

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Section: Introductionmentioning

confidence: 99%

Analysis of Shrinkage on Thick Plate Part using Genetic Algorithm

et al. 2016

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“…In this paper, an adaptive optimization method based on the kriging surrogate model 15−17 is proposed. Compared to prior work, 12 this optimization method has greater ability in exploring unknown space by considering the predicted uncertainty. In addition, it allows optimization iterations to start with a small initial sample size and maintains the stability of the kriging surrogate model while it is being improved adaptively.…”

mentioning

confidence: 95%

“…For the sake of effectively obtaining accurate optimal solution, Gao and Wang proposed a sequential optimization method based on the kriging surrogate model. 12 The sequential optimization method can improve the surrogate function using the current optimal solution until the convergence criterion is satisfied. The optimization results show that this optimization method is effective and efficient for reducing part warpage.…”

mentioning

confidence: 99%

Adaptive geometry and process optimization for injection molding using the kriging surrogate model trained by numerical simulation

Gao¹,

Turng²,

Wang³

2008

Adv Polym Technol

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An adaptive optimization method based on the kriging surrogate model has been developed to intelligently determine the optimal geometric dimensions and processing parameters for minimizing warpage in injection-molded components. The kriging surrogate model is a statistics-based interpolated technique that provides the approximate functional relationship ADAPTIVE GEOMETRY AND PROCESS OPTIMIZATION FOR INJECTION MOLDINGbetween warpage and factors that influence warpage. In this study, it is used to be first trained by-and later replaced-the full-fledged, time-consuming numerical simulation in the optimization process. Based on this surrogate model, an adaptive iteration scheme that takes into account the predicted uncertainty is performed to improve the accuracy of the surrogate model while finding the optimum solution. The optimization process starts with a small number of initial training sample points and then adds additional key points during iterations by evaluating the correlations among the candidate points. As an example of validation and application, optimization of geometric dimensions and processing parameters for a box-shape part with different and stepwise wall thicknesses has been performed. The results demonstrate the feasibility and effectiveness of the proposed optimization method.

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“…Considering the huge computational cost in the aerodynamic optimization, especially in the shape optimization of high-speed trains, a sequential optimization method [14] based on minimizing the response surface criteria [13] has been adopted in the present paper so as to reduce the training points. A basic Kriging model could be firstly constructed from the initial 20 training points.…”

Section: Construction Of Kriging Model and Accuracy Analysismentioning

confidence: 99%

“…The Kriging surrogate model is an interpolation technique based on statistical theory [11][12][13][14]. This model takes full account of the relevant characteristics of the variable space, containing the regression part and the nonparametric part:…”

Section: Kriging Surrogate Modelmentioning

confidence: 99%

Multi-objective optimization of the streamlined head of high-speed trains based on the Kriging model

Yao

Guo

Sun

et al. 2012

Sci. China Technol. Sci.

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As the running speed of high-speed trains increases, aerodynamic drag becomes the key factor which limits the further increase of the running speed and energy consumption. Aerodynamic lift of the trailing car also becomes the key force which affects the amenity and safety of the train. In the present paper, a simplified CRH380A high-speed train with three carriages is chosen as the model in order to optimize aerodynamic drag of the total train and aerodynamic lift of the trailing car. A constrained multi-objective optimization design of the aerodynamic head shape of high-speed trains based on adaptive non-dominated sorting genetic algorithm is also developed combining local function three-dimensional parametric approach and central Latin hypercube sampling method with maximin criteria based on the iterative local search algorithm. The results show that local function parametric approach can be well applied to optimal design of complex three-dimensional aerodynamic shape, and the adaptive non-dominated sorting genetic algorithm can be more accurate and efficient to find the Pareto front. After optimization the aerodynamic drag of the simplified train with three carriages is reduced by 3.2%, and the lift coefficient of the trailing car by 8.24%, the volume of the streamlined head by 2.16%; the aerodynamic drag of the real prototype CRH380A is reduced by 2.26%, lift coefficient of the trailing car by 19.67%. The variation of aerodynamic performance between the simplified train and the true train is mainly concentrated in the deformation region of the nose cone and tail cone. The optimization approach proposed in the present paper is simple yet efficient, and sheds lights on the constrained multi-objective engineering optimization design of aerodynamic shape of high-speed trains.

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An effective warpage optimization method in injection molding based on the Kriging model

Cited by 114 publications

References 14 publications

Analysis of Shrinkage on Thick Plate Part using Genetic Algorithm

Analysis of Shrinkage on Thick Plate Part using Genetic Algorithm

Adaptive geometry and process optimization for injection molding using the kriging surrogate model trained by numerical simulation

Multi-objective optimization of the streamlined head of high-speed trains based on the Kriging model

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