Multi-objective optimization of volume shrinkage and clamping force for plastic injection molding via sequential approximate optimization

Kitayama, Satoshi; Natsume, Shinji

doi:10.1016/j.simpat.2014.07.004

Cited by 58 publications

(24 citation statements)

References 21 publications

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“…where α represents the weight coefficient, and p is the parameter that defines the Lp space. The p was set as 4 based on Kitayama and Natsume (2014) and Kitayama, Saikyo, Nishio, and Tsutsumi, (2015).…”

Section: Optimizationmentioning

confidence: 99%

Shape optimization of screwdriver handle for improving grasping comfort of precision and power grips

Chihara

Seo

Sakamoto

2019

Hum Ftrs & Erg Mfg Svc

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Improving grasping comfort is a significant factor in enhancing the value of industrial products, as most products are handled by human hands. Our aim was to optimize hand tool shapes and maximize grasping comfort, considering multiple‐shape parameters and grasping types. A screwdriver handle was used as the reference tool for this case study. The measurements of handle length, end diameter, and middle diameter were utilized as the shape parameters of the handle. Twelve participants were included in this study. We measured the participants' subjective perceptions of comfort while grasping the precision and power grips during screw‐driving and screw‐tightening tasks, respectively. The design of the screwdriver handle was formulated as a bi‐objective optimization problem with respect to the grasping comfort of the precision and power grips. A Pareto frontier was determined by optimizing the formulated problem. Well‐balanced and optimal shapes for the precision and power grips were identified.

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Section: Optimizationmentioning

confidence: 99%

Shape optimization of screwdriver handle for improving grasping comfort of precision and power grips

Chihara

Seo

Sakamoto

2019

Hum Ftrs & Erg Mfg Svc

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“…Injection molding is a complicated process of nonlinear coupling system with multiple input and output parameters. It takes the characteristics of polymer materials, mold, and process parameters as the input, while the molding efficiency, manufacturing cost, and product quality are taken as the output parameters . When the characteristics of the polymer material are determined, the runner diameters in a multicavity mold and injection molding process parameters (IMPP) are the most important factors that affect the injection molding process.…”

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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“…This model is intended to estimate overvoltage transients by taking into account frequency effects when the cable system is fed by a PWM-modulated switching power converter. The cable model applied in this paper is similar to that proposed in [22] Genetic algorithms (GA) have been widely applied in optimization problems in many disciplines [23,24] including the optimal estimation of transmission line parameters [25], transformers parameters [16,26] or impulse generators [27] among others. In [28] nonlinear transfer functions were approximated by applying a GA approach, by which the optimal values of the expansion coefficients were found, thus providing more accurate results than the classical Chebyshev polynomial approximation.…”

Section: Introductionmentioning

confidence: 99%

Optimal fitting of high-frequency cable model parameters by applying evolutionary algorithms

Bogarra

Riba

Sala-Caselles

et al. 2017

International Journal of Electrical Power & Energy Systems

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Due to the widespread use of electronic power converters, low-voltage high-frequency cable models are being increasingly applied in industry, automobile or aeronautics applications among others. It is known that depending on switching frequency, cable configuration and length, transient overvoltage effects comprising a wide frequency range from dc up to several tens of MHz can appear. However, to accurately reproduce the wide-band frequency response, such models often require the use of ladder networks, thus being necessary to adjust the values of a relatively large number of R, L and C components, which is a complex task. This paper is focused to solve this problem, which is done by applying an iterative genetic algorithm (IGA) optimization approach. From a set of experimental short circuit and open circuit tests the high-frequency cable model of a given cable configuration is obtained, whose parameters are fitted by means of the proposed IGA-based method. Finally, the accuracy of the model obtained is validated experimentally by comparing the frequency-domain and time-domain responses through overvoltage predictions of different samples of the analyzed cable.Postprint (author's final draft

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Multi-objective optimization of volume shrinkage and clamping force for plastic injection molding via sequential approximate optimization

Cited by 58 publications

References 21 publications

Shape optimization of screwdriver handle for improving grasping comfort of precision and power grips

Shape optimization of screwdriver handle for improving grasping comfort of precision and power grips

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

Optimal fitting of high-frequency cable model parameters by applying evolutionary algorithms

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