Data and Modeling in Industrial Manufacturing

Grzegorzewski, Przemysław; Kochański, Andrzej

doi:10.1007/978-3-030-03201-2_1

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…1. The basic criterion for the division of methods used in phenomena modelling [1] Physical modelling involves the design of special test stands, where the controlled conditions of the process are simulated and process behaviour is observed (e.g. Gleeble apparatus).…”

Section: Methods Of Modelling the Behaviour Of Metals During Hot Defo...mentioning

confidence: 99%

Archives of Foundry Engineering

Mrzygłód,

Łukaszek-Sołek,

Olejarczyk-Wożeńska

et al. 2022

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Hot deformation of metals is a widely used process to produce end products with the desired geometry and required mechanical properties. To properly design the hot forming process, it is necessary to examine how the tested material behaves during hot deformation. Model studies carried out to characterize the behaviour of materials in the hot deformation process can be roughly divided into physical and mathematical simulation techniques. The methodology proposed in this study highlights the possibility of creating rheological models for selected materials using methods of artificial intelligence, such as neuro-fuzzy systems. The main goal of the study is to examine the selected method of artificial intelligence to know how far it is possible to use this method in the development of a predictive model describing the flow of metals in the process of hot deformation. The test material was Inconel 718 alloy, which belongs to the family of austenitic nickel-based superalloys characterized by exceptionally high mechanical properties, physicochemical properties and creep resistance. This alloy is hardly deformable and requires proper understanding of the constitutive behaviour of the material under process conditions to directly enable the optimization of deformability and, indirectly, the development of effective shaping technologies that can guarantee obtaining products with the required microstructure and desired final mechanical properties. To be able to predict the behaviour of the material under non-experimentally tested conditions, a rheological model was developed using the selected method of artificial intelligence, i.e. the Adaptive Neuro-Fuzzy Inference System (ANFIS). The source data used in these studies comes from a material experiment involving compression of the tested alloy on a Gleeble 3800 thermomechanical simulator at temperatures of 900, 1000, 1050, 1100, 1150 o C with the strain rates of 0.01 -100 s -1 to a constant true strain value of 0.9. To assess the ability of the developed model to describe the behaviour of the examined alloy during hot deformation, the values of yield stress determined by the developed model (ANFIS) were compared with the results obtained experimentally. The obtained results may also support the numerical modelling of stress-strain curves.

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Section: Methods Of Modelling the Behaviour Of Metals During Hot Defo...mentioning

confidence: 99%

Archives of Foundry Engineering

Mrzygłód,

Łukaszek-Sołek,

Olejarczyk-Wożeńska

et al. 2022

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“…This is why monitoring and adapting the most important production processes on the basis of the actual process are necessary. High flexibility is soft modeling advantage, as it allows quick and easy analysis of process behaviour [19].…”

Section: State Of Artmentioning

confidence: 99%

Management and Production Engineering Review

Sika

Szajewski

Hajkowski

et al. 2019

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The paper presents an example of Instance-Based Learning using a supervised classification method of predicting selected ductile cast iron castings defects. The test used the algorithm of k-nearest neighbours, which was implemented in the authors' computer application. To ensure its proper work it is necessary to have historical data of casting parameter values registered during casting processes in a foundry (mould sand, pouring process, chemical composition) as well as the percentage share of defective castings (unrepairable casting defects). The result of an algorithm is a report with five most possible scenarios in terms of occurrence of a cast iron casting defects and their quantity and occurrence percentage in the casts series. During the algorithm testing, weights were adjusted for independent variables involved in the dependent variables learning process. The algorithms used to process numerous data sets should be characterized by high efficiency, which should be a priority when designing applications to be implemented in industry. As it turns out in the presented mathematical instance-based learning, the best quality of fit occurs for specific values of accepted weights (set #5) for number k = 5 nearest neighbours and taking into account the search criterion according to "product index".

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“…This article presents a solution to the engineering problem of the unacceptable cracking of flanges in the production process. For this purpose, the authors used the design of experiment methodology, which is widely used in the determination of parameters of technological processes (Bober, 2017;Grzegorzewski & Kochański, 2019). The impact of selected process parameters on defect occurrence has been examined thanks to numerous experimental tests.…”

Section: Introductionmentioning

confidence: 99%

Application of the design of experiments and statistical hypothesis to reduce defects in the flange of the steel plate process

Sadłowska¹,

W�jcik²

2022

cmms

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The paper presents a discussion on the occurrence of defects in the circumferential flange of steel plate. The numerous flange face defects have been analysed. The types of defects have been selected and categorized using an experimental planning procedure involving 600 samples for 24 different process variants, considering variables such as material, hole diameter, tool size and tool position. The analysis of experimental results enabled to determine of the optimal values of process parameters to minimize the occurrence of defects. Furthermore, the influence of individual parameters on the quality of the flange surface has been carried out to obtain the process parameters' impact using statistical hypotheses. As a result, it was possible to develop rules which will be helpful in the design process, especially important when changing the material to be processed.

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Data and Modeling in Industrial Manufacturing

Cited by 6 publications

References 14 publications

Archives of Foundry Engineering

Archives of Foundry Engineering

Management and Production Engineering Review

Application of the design of experiments and statistical hypothesis to reduce defects in the flange of the steel plate process

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