Effect Of Heat Treatment Parameters On The Formation Of ADI Microstructure With Additions Of Ni, Cu, Mo

Mrzygłód, B.; Kowalski, Adam F.; Olejarczyk-Wożeńska, I.; Adrian, H.; Głowacki, M.; Opaliński, Andrzej

doi:10.1515/amm-2015-0330

Cited by 10 publications

(7 citation statements)

References 7 publications

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“…Casting technology still represents a large scale of uncertainty [23], mainly as regards the processes involved in the melt preparation [24,25]. Scientific approaches often refer to methods of reasoning and artificial intelligence [26,27].…”

Section: Combined Techniquesmentioning

confidence: 99%

Approximation of Ausferrite Content in the Compacted Graphite Iron with the Use of Combined Techniques of Data Mining

Regulski

Wilk-Kołodziejczyk

Kacprzyk

et al. 2017

Archives of Foundry Engineering

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This article presents the methodology for exploratory analysis of data from microstructural studies of compacted graphite iron to gain knowledge about the factors favouring the formation of ausferrite. The studies led to the development of rules to evaluate the content of ausferrite based on the chemical composition. Data mining methods have been used to generate regression models such as boosted trees, random forest, and piecewise regression models. The development of a stepwise regression modelling process on the iteratively limited sets enabled, on the one hand, the improvement of forecasting precision and, on the other, acquisition of deeper knowledge about the ausferrite formation. Repeated examination of the significance of the effect of various factors in different regression models has allowed identification of the most important variables influencing the ausferrite content in different ranges of the parameters variability.

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Section: Combined Techniquesmentioning

confidence: 99%

Approximation of Ausferrite Content in the Compacted Graphite Iron with the Use of Combined Techniques of Data Mining

Regulski

Wilk-Kołodziejczyk

Kacprzyk

et al. 2017

Archives of Foundry Engineering

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“…Increasingly, all these sources of information can be found on the World Wide Web, but to accomplish this it is often not enough to use Google.com, and even if it is, the task may turn out to be quite difficult [18].…”

Section: Multi-source-data-linkage Frameworkmentioning

confidence: 99%

Austempered Ductile Iron Manufacturing Data Acquisition Process with the Use of Semantic Techniques

Wilk-Kołodziejczyk¹,

Kluska-Nawarecka

Regulski³

et al. 2016

Archives of Metallurgy and Materials

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AustemPereD DuctIle Iron mAnufActurIng DAtA AcquIsItIon Process WIth the use of semAntIc technIquesThe aim of this work was to propose a methodology supporting the task of collecting the comparative data on studies of the mechanical properties of ADI. Collecting of research data is an important step in the process of finding the optimum design solutions for newly made products -experimental data allow us properly calibrate the manufacturing process of ADI to let the final product achieve the required properties. Parameters of the ADI production process, i.e. the time and temperature of austenitising and austempering, as well as the alloying elements added to ductile iron affect the ADI properties. The design process can use research data collected, among others, from the Web. As stated in the article, the process of data acquisition can be supported by semantic technologies, including ontologies which are descriptive logic formalism.

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“…Experimental studies aim to determine how different parameters of the manufacturing process affect the final results in both qualitative and quantitative manners (Ref [4][5][6]. Analytical modeling tools, in particular the methods of machine learning, are expected to allow users to create models of approximation of properties on the example of KV.…”

Section: Introductionmentioning

confidence: 99%

The Selection of Heat Treatment Parameters to Obtain Austempered Ductile Iron with the Required Impact Strength

Wilk-Kołodziejczyk

Regulski

Giętka

et al. 2018

J. of Materi Eng and Perform

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The effect of austempering parameters on impact energy of non-alloyed cast iron with nodular graphite was examined. The analysis involved the austenitizing temperature in the range of 830-950°C for the time of 60 min and the austempering temperature in the range of 300-400°C for the time of 8, 16, 32 and 64 min. The value of the impact energy KV was determined. It has been found that the austenitizing temperature and austempering conditions affect the impact energy of ADI. The conducted analytical studies helped to develop a method for the selection of heat treatment parameters to obtain the ausferritic ductile iron with appropriate impact energy KV. The applied machine learning methods included support vector machine, decision trees, artificial neural networks and the method of variational component analysis.

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Effect Of Heat Treatment Parameters On The Formation Of ADI Microstructure With Additions Of Ni, Cu, Mo

Cited by 10 publications

References 7 publications

Approximation of Ausferrite Content in the Compacted Graphite Iron with the Use of Combined Techniques of Data Mining

Approximation of Ausferrite Content in the Compacted Graphite Iron with the Use of Combined Techniques of Data Mining

Austempered Ductile Iron Manufacturing Data Acquisition Process with the Use of Semantic Techniques

The Selection of Heat Treatment Parameters to Obtain Austempered Ductile Iron with the Required Impact Strength

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