Experimental-Based Multi-objective Optimization of Injection Molding Process Parameters

Mukras, Saad M. S.; Omar, Hanafy M.; Al-Mufadi, Fahad A.

doi:10.1007/s13369-019-03855-1

Cited by 30 publications

(16 citation statements)

References 26 publications

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“…In equation 2y max i refers to the maximum out-of-plane displacement along the four edges of the product. is definition has previously been used in [40].…”

Section: Warpage Quantificationmentioning

confidence: 99%

“…A variety of models have previously been used to represent the relationship between the process parameters and the product defect. Some of these techniques include the artificial neural network [41][42][43][44][45][46][47], support vector regression [48], kriging [40,49], and response surface method [50][51][52]. In this work, we adopt the kriging model to develop the relationship between the process parameters and the product defect and the relationship between the process parameters and the product cycle time.…”

Section: Kriging Modelmentioning

confidence: 99%

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Experimental-Based Optimization of Injection Molding Process Parameters for Short Product Cycle Time

Mukras

2020

Advances in Polymer Technology

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This paper presents a framework for optimizing injection molding process parameters for minimum product cycle time subjected to constraints on the product defects. Two product defects, namely, volumetric shrinkage and warpage, as well as seven process parameters including injection speed, injection pressure, cooling time, packing pressure, mold temperature, packing time, and melt temperature, were considered. Injection molding experiments were conducted on specifically chosen test points and results were used to compute the volumetric shrinkage and warpage (at each test point). Thereafter, three relationships between the product cycle time (one relationship), the two product defects (two relationships), and the injection molding parameters were constructed using the kriging technique. An optimization problem to minimize the product cycle time (described by the first relationship) subject to constraints on the product defects (described by the latter two relationships) was then formulated. A combination set of points between the lower and upper extreme values of acceptable product defect was generated to serve as constraints for the two product defects. The optimization problem was then solved using the Fmincon function, available in the Matlab optimization toolbox. A plot of the optimization results revealed an appreciable tradeoff between the cycle time and the two product defects. To validate the optimization, an additional injection molding experiment was conducted for one of the optimization results. Results from the additional experiment showed reasonably close agreement with simulation optimization results differing in the cycle time, the warpage and volumetric shrinkage by 6.7%, 3.2%, and 8%, respectively.

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“…In equation 2y max i refers to the maximum out-of-plane displacement along the four edges of the product. is definition has previously been used in [40].…”

Section: Warpage Quantificationmentioning

confidence: 99%

Section: Kriging Modelmentioning

confidence: 99%

Experimental-Based Optimization of Injection Molding Process Parameters for Short Product Cycle Time

Mukras

2020

Advances in Polymer Technology

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“…This type of defect results in uneven clearance or malfunction during the assembly process [ 2 ]. However, it can be reduced by properly setting the processing parameters [ 6 ]. So far, most mould making companies have relied on the best and guess approach to seek for an ideal setting of processing parameters in reducing warpage.…”

Section: Introductionmentioning

confidence: 99%

Warpage Optimisation on the Moulded Part with Straight Drilled and Conformal Cooling Channels Using Response Surface Methodology (RSM), Glowworm Swarm Optimisation (GSO) and Genetic Algorithm (GA) Optimisation Approaches

et al. 2021

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It is quite challenging to control both quality and productivity of products produced using injection molding process. Although many previous researchers have used different types of optimisation approaches to obtain the best configuration of parameters setting to control the quality of the molded part, optimisation approaches in maximising the performance of cooling channels to enhance the process productivity by decreasing the mould cycle time remain lacking. In this study, optimisation approaches namely Response Surface Methodology (RSM), Genetic Algorithm (GA) and Glowworm Swarm Optimisation (GSO) were employed on front panel housing moulded using Acrylonitrile Butadiene Styrene (ABS). Each optimisation method was analysed for both straight drilled and Milled Groove Square Shape (MGSS) conformal cooling channel moulds. Results from experimental works showed that, the performance of MGSS conformal cooling channels could be enhanced by employing the optimisation approach. Therefore, this research provides useful scientific knowledge and an alternative solution for the plastic injection moulding industry to improve the quality of moulded parts in terms of deformation using the proposed optimisation approaches in the used of conformal cooling channels mould.

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“…It was found that the optimal combination parameters provide better shear stress for injection molding of PC/ABS parts. Mukras et al [10] determined the optimal molding process parameters to minimize the molding defects through experimental multiobjective optimization and then studied the warpage and volume shrinkage of the product quality.…”

Section: Introductionmentioning

confidence: 99%

Study on Injection Molding Process Simulation and Process Parameter Optimization of Automobile Instrument Light Guiding Support

Wang

Fang

2021

Advances in Materials Science and Engineering

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In the present study, Moldflow® software is applied to simulate the injection molding of automobile instrument light guide bracket and optimize the injection gate position. In this regard, Taguchi orthogonal experimental design is adopted, and five processing parameters, the mold temperature, melt temperature, cooling time, packing pressure, and packing time, are considered as the test factors. Moreover, volume shrinkage and warping amount are considered as quality evaluation indices. Then range analysis and variance analysis are carried out to obtain the optimal combination of molding parameters with the volume shrinkage rate and the warpage amount. The grey correlation analysis was used to analyze the test results and obtain the optimal combination of volume shrinkage rate and amount of warping. Based on the performed simulations, it is found that the maximum volume shrinkage rate and the maximum amount of warping in the optimal design are 6.753% and 1.999 mm, respectively. According to the optimal process parameters, the injection molding of the automobile instrument light guide bracket meets the quality requirements.

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Experimental-Based Multi-objective Optimization of Injection Molding Process Parameters

Cited by 30 publications

References 26 publications

Experimental-Based Optimization of Injection Molding Process Parameters for Short Product Cycle Time

Experimental-Based Optimization of Injection Molding Process Parameters for Short Product Cycle Time

Warpage Optimisation on the Moulded Part with Straight Drilled and Conformal Cooling Channels Using Response Surface Methodology (RSM), Glowworm Swarm Optimisation (GSO) and Genetic Algorithm (GA) Optimisation Approaches

Study on Injection Molding Process Simulation and Process Parameter Optimization of Automobile Instrument Light Guiding Support

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