Evaluation of Effect of Plastic Injection Molding Process Parameters on Shrinkage Based on Neural Network Simulation

Wang, Rongji; Zeng, Junliang; Feng, Xianying; Xia, Yuejun

doi:10.1080/00222348.2012.700234

Cited by 36 publications

(12 citation statements)

References 12 publications

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“…The results show that melt temperature was the most significant factor contributed to the warpage condition. This result was inline with previous researchers which found out the same significant factor which influencing the warpage condition [7,[17][18][19][20][21]. Then it follows by cooling temperature and cooling time.…”

Section: Analysis Of Variance (Anova)supporting

confidence: 80%

Warpage Optimisation on the Moulded Part using Response Surface Methodology (RSM) and Glowworm Swarm Optimisation (GSO)

et al. 2017

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Abstract. Nowadays, there are various of optimisation methods that have been explored by many researchers to find the appropriate processing parameters setting for the injection moulding process. From the previous researches, it was reported that the optimisation work has improved the moulded part quality. In this study, the application of optimisation work to improve warpage of the front panel housing have 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 to obtain the mathematical model. This mathematical model then will be used in Glowworm Swarm Optimisation (GSO) method in order to determine 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 39.1% after optimisation. The finding shows that the application of optimisation work offers the best quality of moulded part produced.

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Section: Analysis Of Variance (Anova)supporting

confidence: 80%

Warpage Optimisation on the Moulded Part using Response Surface Methodology (RSM) and Glowworm Swarm Optimisation (GSO)

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The results show that melt temperature was the most significant factor contributed to the warpage condition. This result was inline with previous researchers which found out the same significant factor which influencing the warpage condition [7,[16][17][18][19][20]. Then it follows by cooling temperature and cooling time.…”

Section: Analysis Of Variance (Anova)supporting

confidence: 80%

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

Hazwan

Shayfull

Sharif³

et al. 2017

AIP Conference Proceedings

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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.

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“…Furthermore, a significant amount of field data is required to support the analysis. Neural networks perform well with nonlinear systems and can perceive any inherent law after training [29]- [32]. Therefore, two novel neural networks, i.e., GA-NN and PSO-NN, which use GAs and PSO to improve BP-NN are proposed for the second task.…”

Section: Modeling the Correlation Between Water Parameters And Chlmentioning

confidence: 99%

Modeling Chlorophyll <formula formulatype="inline"> <tex Notation="TeX">$a$</tex> </formula> Concentration Affected by Artificial Upwelling in Qiandao Lake

Zhou

Huang

Ding

et al. 2016

IEEE J. Oceanic Eng.

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Artificial upwelling of ocean water has been increasingly used to enhance primary marine productivity. This approach brings nutrient-rich deep water to the surface and alters the physical characteristics of water and thus the distribution of the nutrient over an effective period. Various water physical characteristics may affect chlorophyll concentration, a key indicator for assessing primary marine productivity. Therefore, it is important to study the correlation between chlorophyll concentration and various water physical characteristics in the artificial upwelling, which has never been examined in literature. In this paper, several water quality parameters, which have potential correlation with chlorophyll concentration, are selected and multiple linear regression (MLR) and multivariate quadratic regression (MQR) are employed to test the correlation. Then, two novel neural network approaches, i.e., genetic-algorithm-based neural network (GA-NN) and particle-swarm-optimization-based neural network (PSO-NN) are proposed to model the correlation, which are further compared with regular backpropagation neural network (BP-NN), MLR, and MQR. The experimental data are collected from Qiandao Lake, China. The modeling results show that chlorophyll concentration has a strong correlation with salinity, temperature, dissolved oxygen (DO), and pH in the process of artificial upwelling, and GA-NN and PSO-NN can provide reliable prediction of the correlation.

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Evaluation of Effect of Plastic Injection Molding Process Parameters on Shrinkage Based on Neural Network Simulation

Cited by 36 publications

References 12 publications

Warpage Optimisation on the Moulded Part using Response Surface Methodology (RSM) and Glowworm Swarm Optimisation (GSO)

Warpage Optimisation on the Moulded Part using Response Surface Methodology (RSM) and Glowworm Swarm Optimisation (GSO)

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

Modeling Chlorophyll <formula formulatype="inline"> <tex Notation="TeX">$a$</tex> </formula> Concentration Affected by Artificial Upwelling in Qiandao Lake

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