An intelligent hybrid system for initial process parameter setting of injection moulding

Mok, S. L.; Kwong, C. K.; Lau, W.S.

doi:10.1080/00207540050205307

Cited by 39 publications

(17 citation statements)

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“…However, the above approaches might include drawbacks and limitations of RSM commented before. Mok et al (2000) presented an intelligent system based on case-based reasoning, TOPSIS, ANN and GA to optimise injection moulding process. Tong et al (2004a) designed the approach to optimise parameters of a transfer moulding process, using case-based reasoning, ANN and GA. Holden and Serearuno (2005) introduced an AI-based method that integrates rule-based knowledge representation, fuzzy logic and GAs for precious stone manufacturing improvement.…”

Section: Multiresponse Optimisation Based On Genetic Algorithmmentioning

confidence: 99%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

Advanced Multiresponse Process Optimisation

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The review of methods used for multiresponse process parameter design and similar multiobjective optimisation problems is discussed in this chapter, implying the following classification: (1) conventional methods based on statistical or mathematical techniques: (i) experimental design techniques (response surface methodology, Taguchi's robust parameter design and related approaches), and (ii) iterative mathematical search techniques; (2) non-conventional methods based on artificial intelligence techniques: (i) fuzzy logic, (ii) artificial neural networks, (iii) metaheuristic search techniques (genetic algorithm, simulated annealing, particle swarm optimisation, ant colony optimisation, tabu search, and recent evolutionary algorithms such as artificial bee colony algorithm, biogeography-based optimisation, and teaching-learning-based optimisation), and (iv) expert systems.

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Section: Multiresponse Optimisation Based On Genetic Algorithmmentioning

confidence: 99%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

Advanced Multiresponse Process Optimisation

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“…As opposed to physical models, empirical models, such as those based on statistical regression and neural networks, are built from experimental data. Neural networks have been used in the development of process models for various manufacturing processes such as injection moulding [1] and grinding [2]. They have the capability to transform nonlinear mathematical modelling into a simplified black-box structure.…”

Section: Introductionmentioning

confidence: 99%

The process modelling of epoxy dispensing for microchip encapsulation using fuzzy linear regression with fuzzy intervals

Kwong

Bai

et al. 2003

The International Journal of Advanced Manufacturing Technology

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Epoxy dispensing is a popular way to perform microchip encapsulation for chip-on-board (COB) packages. However, the determination of the proper process parameters setting for a satisfactory encapsulation quality is difficult due to the complex behaviour of the encapsulant during the dispensing process and the inherent fuzziness of epoxy dispensing systems. Sometimes, the observed values from the process may be irregular. In conventional regression models, deviations between the observed values and the estimated values are supposed to have a probability distribution. However, when data is scattered, the obtained regression model has too wide of a possibility range. These deviations in processes such as epoxy dispensing can be regarded as system fuzziness that can be dealt with satisfactorily using a fuzzy regression method. In this paper, the fuzzy linear regression concept with fuzzy intervals and its application to the process modelling of epoxy dispensing for microchip encapsulation are described. Two fuzzy regression models, expressing the correlation between various process parameters and the two quality characteristics, respectively, were developed. Validation experiments were performed to demonstrate the effectiveness of the method for process modelling.

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“…It involves the determination of a number of processing parameters, e.g., pressure (injection, holding, back and melt), temperature (coolant, nozzle, barrel, melt and mould), time (fill, holding, cooling and cycle), clamping force, injection speed, screw rotational speed, injection stroke, etc. [2,3]. Among these parameters, a few are dominant because others may be determined from them either directly or by relating to some other factors such as the specific moulding machine used.…”

Section: Introductionmentioning

confidence: 99%

A computer-aided system for an optimal moulding conditions design using a simulation-based approach

Lam

Britton

Deng

2003

The International Journal of Advanced Manufacturing Technology

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Moulding conditions design refers to parameter settings of moulding conditions such as melt temperature, mould temperature and injection time. They are important factors in plastic injection moulding design. The determination of these parameters is a highly skilled job and has largely been dependent on injection moulding engineers' experience and intuitiveness. In this paper, the authors present a flexible computer-aided system to assist the designers by using simulation-based technology. One of the most prominent characteristics of the system is that it allows the designers to specify their intended quality measuring criteria such as minimum cavity pressure and shear stress, a uniform distribution of cooling time, end-of-fill temperature and volumetric shrinkage. The system uses weighted criteria as an objective function to determine optimal moulding conditions when multiple criteria are specified. Moldflow simulation results are used to compare different designs. A two-step exhaustive search strategy (from coarse to refined) is used to search for a near-optimal design.

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An intelligent hybrid system for initial process parameter setting of injection moulding

Cited by 39 publications

References 0 publications

Review of Multiresponse Process Optimisation Methods

Review of Multiresponse Process Optimisation Methods

The process modelling of epoxy dispensing for microchip encapsulation using fuzzy linear regression with fuzzy intervals

A computer-aided system for an optimal moulding conditions design using a simulation-based approach

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