Optimization of Process Parameters for Vertical‐Faced Polypropylene Bottle Injection Molding

Wang, Youmin; Yan, Zhichao; Shan, Xuejun

doi:10.1155/2018/2635084

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Noniterative optimization methods are easy to implement and require less computation. ey have been widely used in research, including the following methods: case-based reasoning (CBR) [3], expert system, fuzzy system [99], and the Taguchi method [100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115][116].…”

Section: Noniterative Optimization Methodsmentioning

confidence: 99%

Intelligent Injection Molding on Sensing, Optimization, and Control

Zhao

Zhang

Dong

et al. 2020

Advances in Polymer Technology

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Injection molding is one of the most significant material processing methods for mass production of plastic products. It is widely used in various industry sectors, and its products are ubiquitous in our daily life. The settings and optimization of the injection molding process dictate the geometric precision and mechanical properties of the final products. Therefore, sensing, optimization, and control of the injection molding process have a crucial influence on product quality and have become an active research field with abundant literature. This paper defines the concept of intelligent injection molding as the integral application of these three procedures—sensing, optimization, and control. This paper reviews recent studies on methods for the detection of relevant physical variables, optimization of process parameters, and control strategies of machine variables in the molding process. Finally, conclusions are drawn to discuss future research directions and technologies, as well as algorithms worthy of being explored and developed.

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Section: Noniterative Optimization Methodsmentioning

confidence: 99%

Intelligent Injection Molding on Sensing, Optimization, and Control

Zhao

Zhang

Dong

et al. 2020

Advances in Polymer Technology

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“…The multistage experimentation [79], with Taguchi's experimentation stage for classifying important factors and a full-factorial design of experiments stage that analyzed both main and interaction effects, was conducted to assist the modeling shrinkage and warpage up to an error value of 0.23 and 0.07mm, respectively. L 9 [75], L 18 [80], L 25 [81], L 27 [82] orthogonal arrays were generated for multi-response optimization of PIM process parameters to reduce the cycle time and warpage. Huang and Lin [80,83] offered an innovative searching method to achieve robust injection molding quality based on a regression model.…”

Section: Orthogonal Array Designmentioning

confidence: 99%

Recent progress in minimizing the warpage and shrinkage deformations by the optimization of process parameters in plastic injection molding: a review

Zhao

Lian

Wang

et al. 2022

Int J Adv Manuf Technol

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The quality control of plastic products is an essential aspect of the plastic injection molding (PIM) process. However, the warpage and shrinkage deformations continue to exist because the PIM process is easily interfered with by several related or independent process parameters. Thus, great efforts have been devoted to optimizing process parameters to minimize the warpage and shrinkage deformations of products during the last decades. In this review, we begin by introducing the manufacturing process in PIM and the cause of warpage and shrinkage deformations, followed by the mechanism about how process parameters, like mold temperature, melt temperature, injection rate, injection pressure, holding pressure, holding and cooling duration, affect those defects. Then, we summarize the recent progress of the design of experiments and four advanced methods (artificial neural networks, genetic algorithm, response surface methodology, and Kriging model) on optimizing process parameters to minimize the warpage and shrinkage deformations. In the end, future perspectives of quality control in injection molding machines are discussed.

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“…Orthogonal test is an efficient, fast, and economical method for seeking the best level combination with multiple factors [14]. Some important test factors and representative level combinations are selected in order to find the optimal and effective level combination.…”

Section: Orthogonal Test Factorsmentioning

confidence: 99%

A Model Developed for Predicting Uniformity of Kyphoplasty Balloon Wall Thickness Based on the Orthogonal Test

Dai

Zhang

Jin

2020

Advances in Materials Science and Engineering

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In order to optimize the wall thickness distribution of medical balloon, kyphoplasty balloon was chosen as the research object, the uniformity of wall thickness distribution was taken as the evaluation index, and the influence of stretch blow molding process on the uniformity of kyphoplasty balloon was investigated. In this paper, 16 sets of orthogonal test schemes were studied by selecting four main parameters such as forming temperature, forming pressure, stretching distance, and holding time of stretch blow molding process based on the L16(44) Taguchi method orthogonal table. The statistical analysis showed that the forming temperature was an utmost parameter on the uniformity, while an optimal scheme was obtained and an optimal balloon with the uniformity of 95.86% was formed under the scheme. To further quantify the relationship between the uniformity and the parameters, artificial neural network (ANN) and nonlinear regression (NLR) models were developed to predict the uniformity of the balloon based on orthogonal test results. A feed-forward neural network based on backpropagation (BP) was made up of 4 input neurons, 11 hidden neurons, and one output neuron, an objective function of the NLR model was developed using second-order polynomial, and the BFGS method was used to solve the function. Adequacy of models was tested using hypothesis tests, and their performances were evaluated using the R2 value. Results show that both predictive models can be used for predicting the uniformity of the balloon with a higher reliability. However, the NLR model showed a slightly better performance than the ANN model.

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Optimization of Process Parameters for Vertical‐Faced Polypropylene Bottle Injection Molding

Cited by 3 publications

References 3 publications

Intelligent Injection Molding on Sensing, Optimization, and Control

Intelligent Injection Molding on Sensing, Optimization, and Control

Recent progress in minimizing the warpage and shrinkage deformations by the optimization of process parameters in plastic injection molding: a review

A Model Developed for Predicting Uniformity of Kyphoplasty Balloon Wall Thickness Based on the Orthogonal Test

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