Mathematical Modeling and Optimization of Injection Molding of Plastics

Elsheikhi, Salah A.; Benyounis, K.Y.

doi:10.1016/b978-0-12-803581-8.04135-7

Cited by 4 publications

(4 citation statements)

References 65 publications

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“…Water injection Hollow part formed 2 Advances in Polymer Technology advantages for the surrogate model, such as small calculation amount, and high accuracy, which can ensure that the optimization algorithm searches for the optimal solution in the continuous space of design variables. At present, in the field of plastic injection molding, RSM, RBF, and Kriging models are frequently used [8].…”

Section: Melt Injectionmentioning

confidence: 99%

“…In this paper, cross-validation is adopted, which is an effective test method widely used in metamodeling techniques in support of engineering design optimization [8,26]. The testing samples consist of 10 groups, and the accuracy of the three surrogate models is tested using two evaluation indicators: relative error (RE) and relative precision (RP).…”

Section: Cross-validation Of Surrogate Modelsmentioning

confidence: 99%

“…In particular, the optimization of process parameters can be more feasible and reasonable to meet the requirement of product quality with the advantages of saving on materials and energy [7]. In recent years, there are widespread attentions for plastic injection molding optimization based on optimal design theory; more details on optimization theory and application can be found in an early study [8]. While compared with the many optimization techniques used in traditional injection molding, the optimization studies related to WAIM are few.…”

Section: Introductionmentioning

confidence: 99%

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Study of Residual Wall Thickness and Multiobjective Optimization for Process Parameters of Water-Assisted Injection Molding

Yang

2020

Advances in Polymer Technology

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Residual wall thickness is an important indicator for water-assisted injection molding (WAIM) parts, especially the maximization of hollowed core ratio and minimization of wall thickness difference which are significant optimization objectives. Residual wall thickness was calculated by the computational fluid dynamics (CFD) method. The response surface methodology (RSM) model, radial basis function (RBF) neural network, and Kriging model were employed to map the relationship between process parameters and hollowed core ratio, and wall thickness difference. Based on the comparison assessments of the three surrogate models, multiobjective optimization of hollowed core ratio and wall thickness difference for cooling water pipe by integrating design of experiment (DOE) of optimized Latin hypercubes (Opt LHS), RBF neural network, and particle swarm optimization (PSO) algorithm was studied. The research results showed that short shot size, water pressure, and melt temperature were the most important process parameters affecting hollowed core ratio, while the effects of delay time and mold temperature were little. By the confirmation experiments for the best solution resulted from the Pareto frontier, the relative errors of hollowed core ratio and wall thickness are 2.2% and 3.0%, respectively. It demonstrated that the proposed hybrid optimization methodology could increase hollowed core ratio and decrease wall thickness difference during the WAIM process.

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Section: Melt Injectionmentioning

confidence: 99%

Section: Cross-validation Of Surrogate Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study of Residual Wall Thickness and Multiobjective Optimization for Process Parameters of Water-Assisted Injection Molding

Yang

2020

Advances in Polymer Technology

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“…The lot size recommended by a mathematical manufacturing lot size model would be the most effective because it accounts for the tradeoff between the costs involved [7]. Optimization is the process of finding (activity of selecting [9]) the simplest possible solution to a given problem by examining several alternatives (assessed after a predefined criterion) [10] and maybe done by adjusting the inputs to or characteristics of a device, mathematical process, or experiment to determine the minimum or maximum output [11]. The optimization problem contains three basic parameters that must be considered, that is, the objective function, a collection of variables, and a collection of constraints [12].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Manufacturing Lot Size Under Stochastic Demand

S.¹

2022

ijcsrr

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In many manufacturing problems, multi-objective optimizations are representative models, as objectives are considered a conflict with one another. In real-life applications, optimizing a specific solution concerning one objective may end up in unacceptable results concerning the other objectives. Many Manufacturing companies operate under uncertainties and this affects the system performance. Stochastic product demand is one of the challenges faced by manufacturing companies and often affects the manufacturing system’s performance and decision-making. Making the proper decisions regarding manufacturing lot-sizing problems is critical for any manufacturer because it makes the firm compete within the market. In this paper, Markov chains in conjunction with stochastic goal programming were used to develop an optimization model for the manufacturing lot size. The over-achievement or under-achievement of the manufacturing lot size was determined by defining the goal constraints, deviation variables, priorities, and objective function. The different states of demand for the product with stochastic demand were represented by states of a Markov chain. Using the applied mathematics solver in MATLAB TM, the optimization model was then solved, determining the quantity of product to be manufactured in a given quarter of the year as demand changes from one state to another.

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