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

Li, Sai; Fan, Xinping; Huang, Haiyue; Cao, Yanli

doi:10.1002/app.48659

Cited by 20 publications

(5 citation statements)

References 23 publications

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“…Each point in the Pareto solution set can be chosen as the best one dependent on the designer's considerations on the compromise among all the objection functions. In this work, the classical NSGA-II [27] was adopted to identify the (11)…”

Section: Multi-objective Optimization Based On Nsga-iimentioning

confidence: 99%

Multi-objective optimization of heating system for rapid thermal cycling molding mold with internal induction heating

Xiao

Kahve

2021

SN Appl. Sci.

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A rapid thermal cycling molding (RTCM) with novel internal induction heating mode is proposed in this work. The induction coils are directly inserted in the corresponding mounting holes of mold with an annular gap in between. During mold heating, eddy current losses confined at the walls of the mounting holes act as thermal sources to rapidly heat the mold cavity surface. Water passed through the annular gaps can be utilized to cool the mold in the cooling stage. Moreover, a design framework of the internal induction heating system in the RTCM mold is also developed. Firstly, a unit cell model of the mold was established to evaluate mold thermal response via numerical simulations, in which the effect of frequency and magnitude of coil current, the layout of induction coils and the annular gap size were examined. Then, a hybrid multi-objective optimization method was applied to optimize the induction heating system for the unit cell model. Finally, based on the obtained optimal parameters, a novel design strategy was adopted to conformally arrange the induction coils for the industrial RTCM molds. The blow mold of automotive spoiler was taken as an example to validate the effectiveness of the proposed approach. The results show that the present approach cannot only improve the mold thermal response performance, but also facilitate the mold heating system design process. This work may provide an effective method to realize RTCM of industrial plastics parts with free-form shape.

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Section: Multi-objective Optimization Based On Nsga-iimentioning

confidence: 99%

Multi-objective optimization of heating system for rapid thermal cycling molding mold with internal induction heating

Xiao

Kahve

2021

SN Appl. Sci.

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“…Numerous studies about the relations between engine performance and injection parameters have been done in this field, both theoretically , and experimentally, − to improve engine performance. For theoretical study, Chang et al established a practical model based on the pressure–volume–temperature property and Li et al combined NSGA-II with predictive models to obtain Pareto optimal solutions for injection parameters. Liu et al found the favorable injection parameters and combustion chamber shape of a natural gas/diesel dual fuel engine based on NSGA-II and KIVA-3A.…”

Section: Introductionmentioning

confidence: 99%

Multi-Optimization of Injection Parameters Affecting Emissions in a Diesel Engine Fueled with Fischer–Tropsch Fuel Based on NSGA-II

Shi

Liu

et al. 2023

ACS Omega

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In this paper, an electronically controlled diesel engine fueled with Fischer–Tropsch fuel was selected to optimize soot and NO x emissions. First, the effects of injection parameters on exhaust performance and combustion properties were studied on an engine test bench and then a prediction model based on a support vector machine (SVM) was established according to the test results. On this basis, a decision analysis of soot and NO x solutions assigned with different weights was performed based on the TOPSIS analysis method. It turned out that the “trade-off” relation between soot and NO x emission was improved effectively. As a matter of fact, the Pareto front selected by this method showed a significant decline compared with the original operating points, in which soot declined by 3.7–7.1% and NO x declined by 1.2–2.6%. Finally, the experiments were used to confirm the validity of the results, which indicated that the Pareto front corresponded well with the test value. The maximum relative error between the soot Pareto front and the measured value is 8% while it is 5% for NO x emission, and the R 2 values of soot and NO x under various conditions are more than 0.9. This instance proved that research on diesel engine emission optimization based on the SVM and NSGA-II is feasible and valid.

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“…Furthermore, improving the optical properties of polymer optics as a result of reducing residual stress in injection molded plastic lenses was the subject of another study [26]. Li et al employed Kriging and NSGA-II methods to investigate the effect of runner diameter and process parameters on the quality, cost, and production efficiency [27]. The Taguchi method has been used to investigate the effect of process parameters on the mechanical properties of parts produced with recycled plastic.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning for Prediction and Process Optimization—Case Study of Blush Defect in Plastic Injection Molding

et al. 2023

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Injection molding is one of the most important processes for the mass production of plastic parts. In recent years, many researchers have focused on predicting the occurrence and intensity of defects in injected molded parts, as well as the optimization of process parameters to avoid such defects. One of the most frequent defects of manufactured parts is blush, which usually occurs around the gate location. In this study, to identify the effective parameters on blush formation, eight design parameters with effect probability on the influence of this defect have been investigated. Using a combination of design of experiments (DOE), finite element analysis (FEA), and ANOVA, the most significant parameters have been identified (runner diameter, holding pressure, flow rate, and melt temperature). Furthermore, to provide an efficient predictive model, machine learning methods such as basic artificial neural networks, their combination with genetic algorithms, and particle swarm optimization have been applied and their performance analyzed. It was found that the basic artificial neural network (ANN), with an average accuracy error of 1.3%, provides the closest predictions to the FEA results. Additionally, the process parameters were optimized using ANOVA and a genetic algorithm, which resulted in a significant reduction in the blush defect area.

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

Cited by 20 publications

References 23 publications

Multi-objective optimization of heating system for rapid thermal cycling molding mold with internal induction heating

Multi-objective optimization of heating system for rapid thermal cycling molding mold with internal induction heating

Multi-Optimization of Injection Parameters Affecting Emissions in a Diesel Engine Fueled with Fischer–Tropsch Fuel Based on NSGA-II

Application of Machine Learning for Prediction and Process Optimization—Case Study of Blush Defect in Plastic Injection Molding

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