Prediction of ductile cast iron quality by artificial neural networks

Perzyk, M.; Kochański, Andrzej

doi:10.1016/s0924-0136(00)00822-0

Cited by 51 publications

(31 citation statements)

References 2 publications

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“…To avoid the rule of thumb, expert advice, try-error method used in shop floor practice, neural networks has been successfully implemented to predict filling time, solidification time and casting defects ,surface defects [75,76], solidification time [77,78], filling time and porosity , injection time [79,80], of pressure die casting process. To predict interfacial heat transfer coefficients at metal-mould interface [81], compressive strength, secondary dendrite arm spacing [82], mechanical properties [83], permeability [84] of different casting processes the soft computing based neural networks were used. To accurately control the quality of the moulding sands [85] and to predict the presence/absence of the casting defects [86] such as hot crack, misrun, scab blow hole and air lock in the sand mould system, NN is used.…”

Section: Modelling Using Soft Computing Approachmentioning

confidence: 99%

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Modelling in Squeeze Casting Process-Present State and Future Perspectives

Patel¹,

MB²

2015

Adv Automob Eng

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The growing demand in today's competitive manufacturing environment has encouraged the researchers to develop and apply modelling tools. The development and application of modelling tools help the casting industries to considerably increase productivity and casting quality. Till date there is no universal standard available to model and optimize any of the manufacturing processes. However the present work discusses the advantages and limitations of some conventional and non-conventional modelling tools applied for various casting processes. In addition the research effort made by various authors till date in modelling and optimization of the squeeze casting process has been reported. Furthermore the necessary steps for prediction and optimization are high lightened by identifying the trends in the literature. Ultimately this research paper explores the scope for future research in online control of the process by automatically adjusting the squeeze cast process parameters through reverse prediction by utilizing the soft computing tools namely, Neural Network, Genetic Algorithms, Fuzzy-logic Controllers and their different combinations. The present work also proposed a detailed methodology, starting from the selection of process variables till the best process variable combinations for extreme values of the outputs responsible for better product quality using experimental, prediction and optimization methodology. Citation: Manjunath Patel GC, Krishna P, Parappagoudar MB (2015) Modelling in Squeeze Casting Process-Present State and Future Perspectives. Modelling in Squeeze Casting

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Section: Modelling Using Soft Computing Approachmentioning

confidence: 99%

“…The most practical requirement in industry is to predict the combination of process variables capable to produce the desired output through reverse prediction [83]. Till date, No much work reported yet to carry out the reverse mapping for the squeeze casting process.…”

Section: Modelling Using Neural-network Based Approachesmentioning

confidence: 99%

Modelling in Squeeze Casting Process-Present State and Future Perspectives

Patel¹,

MB²

2015

Adv Automob Eng

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“…Ductile cast iron strength as the function of its chemical composition [4]. In addition to the training subset containing 700 records, the verifying subset was also created, which contained 90 records.…”

Section: Data Setsmentioning

confidence: 99%

Modeling of manufacturing processes by learning systems: The naïve Bayesian classifier versus artificial neural networks

Perzyk¹,

Biernacki²,

Kochański³

2005

Journal of Materials Processing Technology

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“…The authors present the possibility of using the backpropagation and generalized regression algorithms to predict properties of the moulds as a function of the parameters of moulding sands as well as to determine the input parameters of moulds based on the properties of the mould. Also some interesting studies using neural networks to predict the parameters of moulding sands are presented by M. Perzyk [26,27] These methods are helpfull to prepare the moulds with expected moisture and bentonite content, but there is a need to improve using data mining to suport rebonding of moulding sand for getting results closer to expected moisture.…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Moulding Sand Moisture Content Based on the Knowledge Acquired by Data Mining Techniques

Regulski¹,

Jakubski²,

Opaliński³

et al. 2016

Archives of Metallurgy and Materials

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The subject of the study is the improvement of the quality of moulding sand preparation. An exploration research performed on the data concerning moulding sand quality parameters was described. The aim of the research was to find relationships between various factors determining the properties of moulding sands and, based on the results obtained, build models predicting the sand moisture content with the induction of classification and regression trees. A two-match prediction approach was demonstrated and its effectiveness in evaluating the moulding sand moisture content was discussed. The knowledge in the form of rules acquired in this way can be used in the creation of knowledge bases for systems supporting decisions in the diagnostics of the moulding sand rebonding process. Formalized knowledge also facilitates further processing of the measurement data.

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Prediction of ductile cast iron quality by artificial neural networks

Cited by 51 publications

References 2 publications

Modelling in Squeeze Casting Process-Present State and Future Perspectives

Modelling in Squeeze Casting Process-Present State and Future Perspectives

Modeling of manufacturing processes by learning systems: The naïve Bayesian classifier versus artificial neural networks

The Prediction of Moulding Sand Moisture Content Based on the Knowledge Acquired by Data Mining Techniques

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