Review of research in the determination of process parameters for plastic injection molding

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

doi:10.1002/(sici)1098-2329(199923)18:3<225::aid-adv3>3.0.co;2-3

Cited by 39 publications

(18 citation statements)

References 25 publications

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“…Neural networks generate their own rules by learning from a certain amount of training examples [21]. However, all the efforts can be largely reduced with the aid of Taguchi method in which the case studies have been discussed previously.…”

Section: Integration Of Taguchi Methods With Artificial Neuralmentioning

confidence: 99%

“…Moreover, the adjustments and modifications of processing parameters rely heavily on the experience and intuition of the moulding personnel [20]. Nevertheless, the growing demand in industry for expert moulding personnel far exceeds the supply and it needs more than 10 years' experiences for an amateur moulding personnel to become an expert [21]. Due to the shortage of the experienced moulding personnel, the traditional trial-anderror method is no longer good enough in determining the parameter setting of injection moulding.…”

Section: Optimization Of Injection Moulding Processing Parametersmentioning

confidence: 99%

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Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review

Fei

Mehat²,

Kamaruddin

2013

ISRN Industrial Engineering

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Determining the optimal processing parameter is routinely performed in the plastic injection moulding industry as it has a direct and dramatic influence on product quality and costs. In this volatile and fiercely competitive market, traditional trial-and-error is no longer sufficient to meet the challenges of globalization. This paper aims to review the research of the practical use of Taguchi method in the optimization of processing parameters for injection moulding. Taguchi method has been employed with great success in experimental designs for problems with multiple parameters due to its practicality and robustness. However, it is realized that there is no single technique that appears to be superior in solving different kinds of problem. Improvements are to be expected by integrating the practical use of the Taguchi method into other optimization approaches to enhance the efficiency of the optimization process. The review will shed light on the standalone Taguchi method and integration of Taguchi method with various approaches including numerical simulation, grey relational analysis (GRA), principal component analysis (PCA), artificial neural network (ANN), and genetic algorithm (GA). All the features, advantages, and connection of the Taguchi-based optimization approaches are discussed.

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Section: Integration Of Taguchi Methods With Artificial Neuralmentioning

confidence: 99%

Section: Optimization Of Injection Moulding Processing Parametersmentioning

confidence: 99%

Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review

Fei

Mehat²,

Kamaruddin

2013

ISRN Industrial Engineering

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“…Although there is ongoing research in the molding machine parameter setting, most published approaches include: mathematical models, numerical simulation, process windows, design of experiment (DOE), expert systems, NNs, case based reasoning (CBR), genetic algorithms (GAs) and evolutionary strategies. For an excellent review of the state-of-art on the topic, see [38]. Here, we present a new approach for quality monitoring of online molded parts by analyzing complete data patterns based on SVMs.…”

Section: B Process Data Analysismentioning

confidence: 99%

Support Vector Machines for Quality Monitoring in a Plastic Injection Molding Process

Ribeiro

2005

IEEE Trans. Syst., Man, Cybern. C

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Abstract-Support vector machines (SVMs) are receiving increased attention in different application domains for which neural networks (NNs) have had a prominent role. However, in quality monitoring little attention has been given to this more recent development encompassing a technique with foundations in statistic learning theory. In this paper, we compare -SVM and -SVM classifiers with radial basis function (RBF) NNs in data sets corresponding to product faults in an industrial environment concerning a plastics injection molding machine. The goal is to monitor in-process data as a means of indicating product quality and to be able to respond quickly to unexpected process disturbances. Our approach based on SVMs exploits the first part of this goal. Model selection which amounts to search in hyperparameter space is performed for study of suitable condition monitoring. In the multiclass problem formulation presented, classification accuracy is reported for both strategies. Experimental results obtained thus far indicate improved generalization with the large margin classifier as well as better performance enhancing the strength and efficacy of the chosen model for the practical case study. Index Terms-Fault detection and diagnosis, kernel learning methods, model selection, radial basis function (RBF) neural networks (NNS), support vector machines (SVMs).

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“…The treatment applied is a print temperature. HDPE or high density polyethylene normally is rigid, thin and not transparent or opaque (Mok, Kwong and Lau, 1999; Sarker, Rashid and Molla, 2011; Susilawati, Mustafa and Maulina, 2011). HDPE (Ethylene polymer with densities ranging from 0.941 to 0.965 grams per cubic centimeter) are typically used in grocery stores or used as a plastic bag (Gogte, 2009).…”

Section: Introductionmentioning

confidence: 99%

Characteristics Of Disabled Porosity Development Engine Hot Press For Recycle Plastic HDPE

Sabarudin¹,

Arendra²,

Hidayat³

et al. 2019

Proceedings of the Proceedings of the 2nd International Conference on Advance and Scientific Innovation, ICASI 2019, 18 July, B

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The use of plastic is now inseparable from human life, ranging from the use of plastic bags, furniture, tableware, toys, electronic products are even used in building construction. Plastic consumption per capita in Indonesia has reached 17 kilograms every year with consumption growth reached 6-7 percent every year. Problem plastic waste is not only happening in the world or national scale but also occur in the area. As was the case in one of the districts in Madura Island, which is in Bangkalan. Plastic recycling is required to utilize plastic waste that is thrown away and pollute the environment to be a product of value and worth sales.Dalam wears this study, the machines used are machine Hot Press for recycled HDPE plastic. Design of the machine hot press is required to create a machine that can recycle plastic waste HDPE and has the advantages of portable and light. So in this study, will be produced little knowledge about the characteristics of the machine hot press is based on factors-factors and response-a response that is used. Based on advanced test Tukey, of all combinations, the best combination to produce the smallest porosity defects or products are Using temperature heating to 200 0 C, the heating time of 5 minutes and a temperature of 80 0 C. While opening the best combination to produce the best energy consumption is heating temperature 180 0 C, the heating time of 1 minute and a temperature of 500 0 C opening.

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Review of research in the determination of process parameters for plastic injection molding

Cited by 39 publications

References 25 publications

Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review

Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review

Support Vector Machines for Quality Monitoring in a Plastic Injection Molding Process

Characteristics Of Disabled Porosity Development Engine Hot Press For Recycle Plastic HDPE

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