A fuzzy multiobjective approach for minimization of injection molding defects

Tan, Kim Hung; Yuen, Matthew Ming Fai

doi:10.1002/pen.11223

Cited by 26 publications

(11 citation statements)

References 11 publications

(2 reference statements)

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“…There are basically two forms of optimality for a multi-objective optimization problem: complete optimality and Pareto optimality (Tan andYuen 2000, Liu et al 2003). 'The nature of the injection moulding process makes the Pareto optimality a clear choice for the solution scheme' (Tan and Yuen 2000).…”

Section: Recommended Objective Functionmentioning

confidence: 99%

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Optimization of injection moulding conditions with user-definable objective functions based on a genetic algorithm

Deng

Lam

Britton

2004

International Journal of Production Research

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This paper applies a Genetic Algorithm (GA) method to optimize injection moulding conditions, such as melt temperature, mould temperature and injection time. A GA is very suitable for moulding conditions optimization where complex patterns of local minima are possible. Existing work in the literature has limited versatility because the optimization algorithm is hard-wired with specific objective function. However, for most of the practical applications, the appropriateness of optimization objective functions depends on each specific moulding problem. The paper develops a multi-objective GA optimization strategy, where the objective functions may be defined by the designers, including using different criteria and/or weights. For parts with general quality requirements, an objective function is also recommended with some quality measuring criteria, which are either more accurately represented or cover more moulding defects than those from existing simulation-based optimization approaches. The paper also elaborates on the effective GA attributes suited to moulding conditions optimization, such as population size, crossover rate and mutation rate. A case study demonstrates the effectiveness of the proposed approach and algorithm. The optimization results are compared with those from an exhaustive search method to determine the algorithm's accuracy in finding global optimum. It is found to be favourable. IntroductionInjection moulding conditions such as melt temperature, mould temperature and injection time are important parameters in injection moulding process design. The determination of appropriate moulding conditions is a nontrivial task (Pandelidis and Zou 1990). Increasing melt temperature can reduce viscosity, which results in the desired lower cavity pressure and shear stress. Yet it also increases cooling time which lowers productivity. Besides, too high a melt temperature may cause material degradation. Mould temperature has the similar effect but of much lower significance. Compared to melt temperature and mould temperature, injection time is the most important parameter affecting pressure and shear stress. Shorter injection time contributes to shorter cycle time, but it increases cavity pressure and shear stress. Excessive increase in injection time, however, will lead to an excessive decrease in melt temperature and increase in viscosity, causing cavity pressure and shear stress to increase significantly. Thus, it is necessary to optimize these parameters in order to produce a high quality part at minimum cost.There are generally two approaches in deriving optimal moulding conditions. The first is using artificial intelligence (AI) and knowledge-based system (KBS),

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Section: Recommended Objective Functionmentioning

confidence: 99%

“…'The nature of the injection moulding process makes the Pareto optimality a clear choice for the solution scheme' (Tan and Yuen 2000). As such, the present authors seek only Pareto optimality.…”

Section: Recommended Objective Functionmentioning

confidence: 99%

Optimization of injection moulding conditions with user-definable objective functions based on a genetic algorithm

Deng

Lam

Britton

2004

International Journal of Production Research

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“…Tan and Yuen 22 proposed a fuzzy multipleobjective approach to set up the process for minimizing injection molding defects. In their study, the defects were expressed by a scale number through fuzzy functions.…”

Section: Machine Setup Based On the Empirical Modelmentioning

confidence: 99%

A review of current developments in process and quality control for injection molding

2005

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ABSTRACT:Injection molding is one of the most versatile and important manufacturing processes capable of mass-producing complicated plastic parts in net shape with excellent dimensional tolerance. Injection molding process and quality control has been an active research area for many years, as part quality and yield requirements become more stringent. This paper reviews the state-of-the-art research and development in injection molding control. It organizes prior studies into four categories, namely, process setup, machine control, process control, and quality control, and presents the distinction and connection of these different levels of control. This paper further reviews and compares the typical variables, models, and control methods that have been proposed and employed for those control tasks. Strictly speaking, real online quality control without human intervention has yet to be realized, primarily due to the lack of transducers for online, real time quality response measurement, and a robust model that correlates the control variables with quantitative quality measurements. Based on the research progress to date, this paper suggests that the different levels of control tasks have to be integrated into a multilevel quality control system, and

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“…However, one of its limitations is that it usually requires a large number of experimental data sets, specially, in the nonlinear region. GAs have been applied in process optimization of various manufacturing processes successfully (Hussein & El-Ghazaly 2004, Tan & Yuen 2000, Wilson et al 2001.…”

Section: Introductionmentioning

confidence: 99%

Modelling and optimization of fluid dispensing for electronic packaging using neural fuzzy networks and genetic algorithms

Chan

Kwong

Tsim

2010

Engineering Applications of Artificial Intelligence

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Determination of process conditions for a fluid dispensing process of microchip encapsulation is a highly skilled task, which is usually based on engineers' knowledge and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach.Facing with the global competition, the current trial-and-error approach is inadequate. Modelling the fluid dispensing process is important because it enables us to understand the process behaviour, as well as determine the optimum operating conditions of the process for a high yield, low cost and robust operation. In this research, modelling and optimization of fluid dispensing processes based on neural fuzzy networks and genetic algorithms are described. First, neural fuzzy networks approach is used to model fluid dispensing process for microchip encapsulation.An N-fold validation tests were conducted. Results of the tests indicate that the mean errors and variances of errors of the modeling based on the neural fuzzy networks approach are all better than those of the other existing approaches, statistical regression, fuzzy regression and neural networks, on modeling the fluid dispensing. It is then followed by the determination of process conditions of the process based on a genetic algorithm approach. Validation tests were 2 conducted. Results of them indicate that process conditions determined based on the proposed approaches can achieve the specified quality requirements.

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A fuzzy multiobjective approach for minimization of injection molding defects

Cited by 26 publications

References 11 publications

Optimization of injection moulding conditions with user-definable objective functions based on a genetic algorithm

Optimization of injection moulding conditions with user-definable objective functions based on a genetic algorithm

A review of current developments in process and quality control for injection molding

Modelling and optimization of fluid dispensing for electronic packaging using neural fuzzy networks and genetic algorithms

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