Optimization of Process Parameters using DOE, RSM, and GA in Plastic Injection Molding

Chen, Wen Chin; Kurniawan, Denni; Fu, Gong Loung

doi:10.4028/www.scientific.net/amr.472-475.1220

Cited by 22 publications

(8 citation statements)

References 6 publications

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“…Their study used regression and ANOVA to explore the relationships between input and output, with the significant experimental parameters being melt temperature, injection pressure, packing pressure, and packing time. Chen et al [14], in their optimization of the process parameters, used design of experiment (DOE), response surface methodology (RSM), and GA. Significant plastic injection molding (PIM) process parameters were demonstrated by the ANOVA and DOE screening experiments via the CAE simulations, in which the significant parameters were melt temperature, injection velocity, injection pressure, packing pressure, and packing time.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO

Chen

Nguyen

Chiu

et al. 2015

Int J Adv Manuf Technol

123

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This paper proposes a systematic optimization model of process parameters in plastic injection molding (PIM). Firstly, the Taguchi method is employed for experimentation and data analysis, in which the quality characteristics for the plastic injection product are length and warpage. The control factors for the process are melt temperature, injection velocity, packing pressure, packing time, and cooling time. Moreover, the signal-to-noise (S/N) ratio and analysis of variance (ANOVA) are used to obtain a combination of parameter settings. Experimental data are set for the response surface methodology (RSM) in order to analyze and create two quality predictors and two S/N ratio predictors. The two quality predictors are associated with genetic algorithms (GA) to search for an optimal combination of process parameters that meets multiple-objective quality characteristics. Finally, four predictors are combined with the hybrid GA-PSO to find the final optimal combination of process parameters. The confirmation results show that the proposed model not only enhances the stability in the injection molding process, including the quality in length and warpage, but also reduces the costs of and time spent in the PIM process.

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Section: Introductionmentioning

confidence: 99%

Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO

Chen

Nguyen

Chiu

et al. 2015

Int J Adv Manuf Technol

123

View full text Add to dashboard Cite

show abstract

“…In this study, injection moulding part quality depending on the value of the warpage. In other words, the lowers of warpage value, the better indication of the response condition [14]. …”

Section: Design Of Experiments Setupmentioning

confidence: 99%

Optimisation of warpage on plastic injection moulding part with MGSS conformal cooling channels moulds using response surface methodology (RSM)

Hazwan

Shayfull

Sharif

et al. 2017

AIP Conference Proceedings

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Abstract.Nowadays, there are various methods of optimisation works that have been explored by many researchers to find the appropriate processing parameters setting of injection moulding process. From the previous researches, it was reported that the optimisation work improved the moulded part quality. Apart from quality, the productivity in the injection moulding process also plays an important role. Therefore, some of the researchers have introduced the conformal cooling channels which have been proven to improve the quality and productivity of the moulded part. However, the application of optimisation work to determine an appropriate processing parameters setting in previous researches are only focusing on the conventional straight-drilled cooling channels and the application in conformal cooling channels are still lacking. In this study, the application of optimisation work to improve warpage of the front panel housing part with Milled Groove Square Shape (MGSS) conformal cooling channels has been explored. By selecting cooling time, cooling temperature, packing pressure and melt temperature as the variables, design of experiment (DOE) has been constructed by using the central composite design (CCD) approach. By performing Response Surface Methodology (RSM) optimisation approach, warpage of the moulded part has been optimised. Based on the results warpage has optimised by 46.5%. The finding shows that the application of optimisation work on the conformal cooling channels offers the best outcome.

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“…In this study, injection moulding part quality depending on the value of the warpage. In other words, the lowers of warpage value, the better indication of the response characteristics [13]. …”

Section: Design Of Experiments Setupmentioning

confidence: 99%

Optimisation of warpage on plastic injection moulding part using response surface methodology (RSM)

Hazwan

Shayfull

Sharif³

et al. 2017

AIP Conference Proceedings

View full text Add to dashboard Cite

Abstract.Todays, there are various of optimisation methods that have been studied by many researchers in order to find the appropriate combination of processing parameters setting in the injection moulding process. From the previous literatures, the optimisation works have been proven will improve the moulded part quality. In this study, the application of optimisation work to improve warpage of front panel housing has been explored. By selecting cooling time, coolant temperature, packing pressure and melt temperature as the variable parameters, design of experiment (DOE) have been constructed by using the rotatable central composite design (CCD) approach. Response Surface Methodology (RSM) was performed in order to define the optimal processing parameters setting which will optimise the warpage condition. Based on the results, melt temperature is the most significant factor contribute to the warpage condition and warpage have optimised by 47.1% after optimisation. The findings show that the application of optimisation work offers the best quality of moulded part produced.

show abstract

Optimization of Process Parameters using DOE, RSM, and GA in Plastic Injection Molding

Cited by 22 publications

References 6 publications

Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO

Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO

Optimisation of warpage on plastic injection moulding part with MGSS conformal cooling channels moulds using response surface methodology (RSM)

Optimisation of warpage on plastic injection moulding part using response surface methodology (RSM)

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