2017
DOI: 10.17222/mit.2016.129
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Predictive model and optimization of processing parameters for plastic injection moulding

Abstract: Injection molding is one of the most widely used processes for producing engineered parts in the plastics industry. The objective of this study is to propose a fuzzy expert system for the prediction of mechanical properties of injection-molded parts where the fuzzy system is optimized using particle-swarm optimization. The input process parameters were the mold temperature, melt temperature, injection velocity, packing pressure, cooling time and packing time. The predicted values were in good agreement with th… Show more

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Cited by 8 publications
(5 citation statements)
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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%
See 1 more Smart Citation
“…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%
“…Applying PSO, Kramar and Cica [99] maximized the tensile strength of molded products. Zhang et al [131] carried out a multiobjective optimization of injection molding process parameters of diesel engine oil cooler covers based on the ellipsoid basis function neural network.…”
Section: Iterative Optimization Methodsmentioning
confidence: 99%
“…In addition, particle swarm optimization (PSO) [60,61,73,104,[115][116][117][118][119], model-free optimization (MFO) [120,121], support vector machines (SVM) [122], simulated annealing, and hill-climbing are all advanced and effective ways to conduct the optimization design of process parameters. These models can be used to construct a mathematical approximation to replace expensive finite element analyses.…”
Section: Kriging Modelmentioning
confidence: 99%
“…Lee et al [25] utilized the hybridization of fuzzy association rule mining and variable-length slippery GA to determine process parameter settings and improve finished quality of garment manufacturing. Kramar et al [26] developed a particle swarm optimization based fuzzy expert system to predict mechanical properties of molded parts, obtained optimal process parameters and effectively improved the injection molding quality. However, the association rules used to set relevant parameters in the optimization process are mostly based on the historical data analysis, making it hard to guide quality data prediction in time and later process decision-making.…”
Section: Quality Control In Process Industriesmentioning
confidence: 99%