Control of properties in injection molding by neural networks

Kenig, S.; Ben-David, Arie; Omer, Mohamed I.; Sadeh, Arik

doi:10.1016/s0952-1976(02)00006-4

Cited by 75 publications

(29 citation statements)

References 9 publications

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“…ANN mimic some basic aspects of brain functions [9], which processes information by means of interaction among several neurons. We adopted the most popular model, the multilayer perceptron that contains only feedforward connections, with one hidden layer with H processing units.…”

Section: Data Mining Modelsmentioning

confidence: 99%

Application of Data Mining Techniques in the Estimation of Mechanical Properties of Jet Grouting Laboratory Formulations over Time

Tinoco

Correia

Cortez

2011

Advances in Intelligent and Soft Computing

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Sometimes, the soil foundation is inadequate for constructions purpose (soft-soils). In these cases there is need to improve its mechanical and physical properties. For this purpose, there are several geotechnical techniques where Jet Grouting (JG) is highlighted. In many geotechnical structures, advance design incorporates the ultimate limit state (ULS) and the serviceability limit state (SLS) design criteria, for which uniaxial compressive strength and deformability properties of the improved soils are needed. In this paper, three Data Mining models, i.e. Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Functional Networks (FN), were used to estimate the tangent elastic Young modulus at 50% of the maximum stress applied (E tg50% ) of JG laboratory formulations over time. A sensitivity analysis procedure was also applied in order to understand the influence of each parameter in E tg50% estimation. It is shown that the data driven model is able to learn the complex relationship between E tg50% and its contributing factors. The obtained results, namely the relative importance of each parameter, were compared with the predictive models of elastic Young modulus at very small strain (E 0 ) as well as the uniaxial compressive strength (Q u ). The obtained results can help to understand the behavior of soil-cement mixtures over time and reduce the costs with laboratory formulations.

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Section: Data Mining Modelsmentioning

confidence: 99%

Application of Data Mining Techniques in the Estimation of Mechanical Properties of Jet Grouting Laboratory Formulations over Time

Tinoco

Correia

Cortez

2011

Advances in Intelligent and Soft Computing

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“…Kenig et al 1 used a feed-forward artificial neural network (ANN) with an error back-propagation algorithm for the adequate control of product properties in injection-molded plastics. W. C. Chen et al 2 developed a self-organizing map and a back-propagation neural-network model to generate a dynamic quality predictor for the plastic-injection-molding process.…”

Section: Introductionmentioning

confidence: 99%

Predictive model and optimization of processing parameters for plastic injection moulding

Kramar¹,

Cica²

2017

Mater. Tehnol.

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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 the experimental ones, which indicates that the developed particle-swarm-optimization-based fuzzy expert system can be effectively used to predict the mechanical properties of molded parts. In addition, optimization based on a particle-swarmoptimization algorithm was carried out to obtain the optimum process parameters based on the objective to maximize the tensile strength of the molded product. Keywords: plastics, injection molding, particle-swarm optimization, tensile strength Brizganje je eden izmed najpogosteje uporabljenih postopkov za izdelavo in`enirskih delov v industriji plastike. Cilj te raziskave je predlagati enostavni ekspertni sistem za napovedovanje mehanskih lastnosti delov brizganih kosov, kjer je enostavni sistem optimiziran z uporabo optimizacije z rojem delcev. Vhodni parametri procesa so temperature orodja, temperatura taline, hitrost brizganja, zapiralni pritisk,~as hlajenja in zapiralni~as. Napovedane vrednosti se dobro ujemajo z eksperimentalnimi, kar ka`e, da se razvit enostavni ekspertni sistem na osnovi optimizacije z rojem delcev lahko u~inkovito uporablja za napovedovanje mehanskih lastnosti brizganih delov. Algoritem optimizacije z rojem delcev je podal tudi optimalne procesne parametre za doseganje~im vi{je natezne trdnosti brizganega izdelka. Klju~ne besede: plastika, injekcijsko brizganje, optimizacija z roji delcev, natezna trdnost

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“…To apply this method, displacement transducers placed in the partition line to control and to measure flash defects were used. In addition, an indirect method based on the tensional module control was proposed by Kenig et al [6] to avoid injection defects and to establish a relation between tensional module, part quality, and injection parameters.…”

Section: Introductionmentioning

confidence: 99%

Inspection model and correlation functions to assist in the correction of qualitative defects of injected parts

Chaves

Vizán

Márquez

et al. 2010

Polymer Engineering & Sci

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To perform quality inspection in the injection process is a complex task due to high number of defects that could occur in an injected part and the high number of process parameters that could produce them. Injection defects, in particular qualitative ones, are not a clear reference to determine correct process parameter value setting to produce good quality parts. Research results show that the occurrence of each injection defect could be caused by specific parameters with values above or below an optimal one. Although this information is a guide for the defect correction, the effective correction of qualitative defects with parameter modifications is very complex. This is due to the problems that arise when transforming a qualitative defect into a quantitative inspection. This article shows an inspection model to assist the qualitative defect intensity classification using defect behavior tendency curves. These curves have been deduced from generic analytical relationships established between injection defects and injection process parameters. Conducted tests allow validating the approach and its initial effectiveness.

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Control of properties in injection molding by neural networks

Cited by 75 publications

References 9 publications

Application of Data Mining Techniques in the Estimation of Mechanical Properties of Jet Grouting Laboratory Formulations over Time

Application of Data Mining Techniques in the Estimation of Mechanical Properties of Jet Grouting Laboratory Formulations over Time

Predictive model and optimization of processing parameters for plastic injection moulding

Inspection model and correlation functions to assist in the correction of qualitative defects of injected parts

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