Comparison of Numerical and Experimental Data in Multi-objective Optimization of a Thermoplastic Molded Part

Natalini, Massimo; Sasso, M.; Amodio, D.

doi:10.3139/217.2699

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…The algorithm proposed is independent of the simulation code used and produced results with physical meaning by taking some advantages of the use of Pareto curves. Natalini et al 181 studied the effects of process parameters that heavily influence the warpage of a specified surface and two diameter sizes of a thermoplastic injected part. Pareto front was extracted using a GA, and the best set of parameters was determined after application of specific selection criteria and a weighted objective function.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Section: Injection‐molding Optimizationmentioning

confidence: 99%

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Modeling and Optimization of the Injection‐Molding Process: A Review

et al. 2016

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ABSTRACT:The purpose of this article is to review the research done in the field of mathematical modeling and optimization of the injection-molding (IM) process. Various papers related to the mathematical description of the filling, postfilling, and plasticating phases of the IM process were assessed, and some recent advances on the IM field are described. In addition, research devoted to the optimization of the IM process based on various techniques is also discussed. These optimization techniques include design of experiments, artificial neural networks, and evolutionary algorithms. The strengths and weaknesses of each approach were discussed. Finally, this paper also discusses the optimization research performed in the IM process regarding some of the specific features associated with the processes such as runner system and cooling channel configurations, process conditions, gate location, and cavity pressure balancing. C

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Section: Evolutionary Algorithmsmentioning

confidence: 99%

Section: Injection‐molding Optimizationmentioning

confidence: 99%

Modeling and Optimization of the Injection‐Molding Process: A Review

et al. 2016

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“…The Genetic Algorithms discussed in this paper were mostly used for the optimization task of setting the operating conditions, e.g., by Mok et al [ 98 ], Turng et al [ 99 , 100 ], Ozcelik et al [ 101 , 102 , 103 ], Gaspar-Cunha et al [ 104 , 105 , 106 ], Shen et al [ 107 ], Chen et al [ 108 , 109 ], Wu et al [ 110 ], Ding et al [ 111 ], Natalini et al [ 112 ], and Tsai and Luo [ 113 ]. The optimization of the runner system was discussed by Wu et al [ 110 ] and Alam and Kamal [ 114 , 115 ].…”

Section: Optimization For Extrusionmentioning

confidence: 99%

Optimization and Scale-Up for Polymer Extrusion

Nastaj

Wilczyński

2021

Polymers

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A review paper is presented on optimization and scale-up for polymer extrusion, both single screw and twin screw extrusion. Optimization consists in obtaining a multidimensional space of process output variables (response surface) on the basis of an appropriate set of input data and searching for extreme values in this space. Scaling consists in changing the scale of the process according to specific criteria, that is, changing the process while maintaining the scaling parameters at a level that is as close to the reference process parameters as possible. It consists in minimizing the differences between the parameters characterizing the reference process and the resulting process. This may be obtained by using optimization techniques leading to the minimization of discrepancies between the parameters of scaled processes. In the paper, it was stated that optimization and scale-up based on process simulation are more effective than those based on experimentation which is time consuming and expensive. The state-of-the-art on extrusion process modeling which is the basis of optimization and scale-up has been presented. Various optimization techniques have been discussed, and the Genetic Algorithms have been identified as powerful and very efficient. Optimization and scale-up based on the process simulation using Genetic Algorithms have been broadly reviewed and discussed. It was concluded that, up to date, there is a lack of optimization studies on the counter-rotating twin screw extrusion, although the global models of this process are known. There is also a lack of process simulation-based scaling-up studies, both on the counter-rotating twin screw extrusion and on the starve fed single screw extrusion. Finally, development perspectives in this field have been discussed.

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“…Their experimental results show that the proposed optimization model is very successful and can be used in industrial applications. In a latest study [19], a real case of a thermoplastic injected part is analyzed.…”

Section: Introductionmentioning

confidence: 99%

The effect of different parameters on mechanical properties of PA-6/clay nanocomposite through genetic algorithm and response surface methods

Moghri¹,

Shamaee²,

Shahrajabian³

et al. 2015

Int Nano Lett

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This paper presents a methodology for determination of the optimal material and processing parameters (i.e., nanoclay content, melt temperature, feeding rate, and screw speed) to maximize simultaneously tensile modulus and tensile strength of injection-molded PA-6/clay nanocomposites through coupling response surface method and genetic algorithm. The tensile tests on PA-6/clay nanocomposites are conducted to obtain tensile modulus and tensile strength values, and then analysis of variance is performed. The predicted models for tensile modulus and tensile strength are created by response surface method, and then the functions are optimized by a genetic algorithm code implemented in MATLAB. Acceptable agreement has been observed between the values of the process parameters predicted by the response surface method and genetic algorithm and those of the process parameters obtained through experimental measurements. This study shows that the response surface method coupled with the GA can be utilized effectively to find the optimum process variables in tensile test of PA-6/NC nanocomposites.

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Comparison of Numerical and Experimental Data in Multi-objective Optimization of a Thermoplastic Molded Part

Cited by 6 publications

References 8 publications

Modeling and Optimization of the Injection‐Molding Process: A Review

Modeling and Optimization of the Injection‐Molding Process: A Review

Optimization and Scale-Up for Polymer Extrusion

The effect of different parameters on mechanical properties of PA-6/clay nanocomposite through genetic algorithm and response surface methods

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