Multiobjective optimization of injection molding process parameters based on Opt LHD, EBFNN, and MOPSO

Zhang, Mengjie; Wang, Jian; Lin, Jiewei; Guo, Qian; Chen, Kongwu; Ma, Liang

doi:10.1007/s00170-015-8100-4

Cited by 46 publications

(24 citation statements)

References 25 publications

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“…Warpage and cycle time [128,129] Shrinkage and clamping force [130] Warpage [131] function to the resulting sum in order to determine the output. e ANN learns to approximate the functions through a training process and adjusts their weights and biases until a performance index reaches a preset threshold value.…”

Section: Taguchi Methodmentioning

confidence: 99%

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Intelligent Injection Molding on Sensing, Optimization, and Control

Zhao

Zhang

Dong

et al. 2020

Advances in Polymer Technology

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Injection molding is one of the most significant material processing methods for mass production of plastic products. It is widely used in various industry sectors, and its products are ubiquitous in our daily life. The settings and optimization of the injection molding process dictate the geometric precision and mechanical properties of the final products. Therefore, sensing, optimization, and control of the injection molding process have a crucial influence on product quality and have become an active research field with abundant literature. This paper defines the concept of intelligent injection molding as the integral application of these three procedures—sensing, optimization, and control. This paper reviews recent studies on methods for the detection of relevant physical variables, optimization of process parameters, and control strategies of machine variables in the molding process. Finally, conclusions are drawn to discuss future research directions and technologies, as well as algorithms worthy of being explored and developed.

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Section: Taguchi Methodmentioning

confidence: 99%

“…ey have similar roles in optimization and will not be specifically introduced here. ese surrogate models serve an objective function for the characterization of the iteration results when conducting iterate optimization strategies, thus making the evaluation of iteration results less time-consuming [123,130,131,133,135,136,141,144,145].…”

Section: Taguchi Methodmentioning

confidence: 99%

Intelligent Injection Molding on Sensing, Optimization, and Control

Zhao

Zhang

Dong

et al. 2020

Advances in Polymer Technology

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“…They combined the Taguchi method, RSM, and nondominated sorting GAs (NSGA‐II) to optimize the injection molding process. Furthermore, Zhang et al . considered the influence of IMPP on the plastic production quality, manufacturing cost, and molding efficiency.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective optimization of injection molding parameters, based on the Gkriging‐NSGA‐vague method

Fan

Huang

et al. 2019

J of Applied Polymer Sci

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Plastic production quality, manufacturing cost, and molding efficiency are three important indices for a new product development. In addition to injection molding process parameters (IMPP), runner system also has an important role in the injection molding process. In this study, the plastic production quality, manufacturing costs, and molding efficiency are considered as the optimized objectives. The design parameters include runner diameters and IMPP. The improved Kriging surrogate model (Gkriging), nondominated sorting genetic algorithm (NSGA-II), and multicriteria fuzzy decision-making approach (vague sets) are combined, and the Gkriging-NSGA-vague scheme is proposed to optimize the runner diameters and the IMPP. Firstly, the Gkriging model is established to map the correlation between design parameters and optimized objectives. Based on the Gkriging model, the NSGA-II is combined with predictive models to obtain the Pareto-optimal solutions. Then, the optimal Pareto-optimal solution is obtained by the vague approach. A multicavity mold with two different plastic parts is utilized as the design case. The optimization results indicate that the Gkriging-NSGAvague method is a powerful method for solving the multi-objective optimization problems.

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“…The surrogate‐based methodology was then proposed, which combines surrogate model and optimization approach, to overcome the inherent weakness of Taguchi method and is capable of solving multi‐objective optimization problems. Zhang et al carried out a multi‐objective optimization of the injection molding process parameters for a diesel engine oil cooler cover based on the optimal Latin hypercube sampling (LHS), Elliptical Basis Function Neural Network (EBFNN), and Multiobjective Particle Swarm Optimization (MPSO). Tian et al combined the RSM and non‐dominated sorting genetic algorithm II (NSGA‐II) to optimize the multiple objectives including quality characteristics and energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Optimization strategy of the multihole products weld lines based on the multi‐objective evaluation system and kriging surrogate model

Wang

Chen

2018

Polymer Engineering & Sci

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The plastic multihole products are widely used in Household Appliance and Automobile industry. The densely distributed weld lines resulting from the multiaperture structure seriously damage the surface appearance and mechanical properties of the products. However, there are relatively few studies on the optimization of weld lines of the plastic multihole products. Therefore, a multihole plate was taken as an example to exploit an optimization strategy for this kind of defects. The multi‐objective evaluation system was established to assess bonding quality of weld lines based on Numerical Simulation. And a multi‐objective optimization methodology was presented to efficiently optimize the process parameters. First, back propagation neural networks and Kriging method were combined to build the surrogate models based on the experiments arranged by Latin hypercube sampling. Second, a systematic test strategy was proposed to ensure high accuracy of the surrogate model. Then, the pareto optimal solution was obtained by integrating the surrogate model and Non‐dominated Sorting Genetic Algorithm II. Finally, the simulated and practical confirmation experiments indicate that bonding quality of weld lines can be effectively characterized and significantly improved by the multi‐objective evaluation system and the multi‐objective optimization methodology, respectively. POLYM. ENG. SCI., 59:781–790, 2019. © 2018 Society of Plastics Engineers

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Multiobjective optimization of injection molding process parameters based on Opt LHD, EBFNN, and MOPSO

Cited by 46 publications

References 25 publications

Intelligent Injection Molding on Sensing, Optimization, and Control

Intelligent Injection Molding on Sensing, Optimization, and Control

Multi‐objective optimization of injection molding parameters, based on the Gkriging‐NSGA‐vague method

Optimization strategy of the multihole products weld lines based on the multi‐objective evaluation system and kriging surrogate model

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