Artificial neural network modeling to evaluate and predict the mechanical strength of duplex stainless steel during casting

Thankachan, Titus; Prakash, K. Soorya; Jothi, Sathiskumar

doi:10.1007/s12046-021-01742-w

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…In the case of the duplex stainless steels, which are currently being introduced in the market and under continuous development for the design of new grades with improved properties, the ANNs methodology is also being applied to evaluate the mechanical properties of this stainless steel family with good results. For instance, it is the case of the developed model by Thankachan et al [ 20 ] to estimate the Rm under casting conditions for the standard duplex S32205 [ 21 ], the modelling of the hot plastic flow curves of the super duplex stainless steel S32507 [ 21 ] by Contini Jr. and Balancin [ 22 ], the prediction of the hardness of the ferrite during low-temperature ageing of duplex stainless steel by Karlsson and Giard [ 23 ], the design of a model to estimate the Rm and A from other mechanical properties such as E , Rp , etc. of the duplex S32205 by Ono and Miyoshi [ 11 ], or the modelling of the impact energy of as cast duplex stainless steel based on its chemical composition by Thankachan and Sooryaprakash [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels

Contreras-Fortes,

Rodríguez-García,

Sales

et al. 2023

Materials

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Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one of the most widespread processes for manufacturing flat stainless steel products, the prediction of their strain-hardening mechanical properties is of great importance to materials engineering. This work uses artificial neural networks (ANNs) to forecast the mechanical properties of the stainless steel as a function of the chemical composition and the applied cold thickness reduction. Multiple linear regression (MLR) is also used as a benchmark model. To achieve this, both traditional and new-generation austenitic, ferritic, and duplex stainless steel sheets are cold-rolled at a laboratory scale with different thickness reductions after the industrial intermediate annealing stage. Subsequently, the mechanical properties of the cold-rolled sheets are determined by tensile tests, and the experimental cold-rolling curves are drawn based on those results. A database is created from these curves to generate a model applying machine learning techniques to predict the values of the tensile strength (Rm), yield strength (Rp), hardness (H), and elongation (A) based on the chemical composition and the applied cold thickness reduction. These models can be used as supporting tools for designing and developing new stainless steel grades and/or adjusting cold-forming processes.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels

Contreras-Fortes,

Rodríguez-García,

Sales

et al. 2023

Materials

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“…The use of machine learning and deep learning methods for regression, or clustering in material science is very common due to the high accuracy of the results using artificial neural networks [9][10][11]. They can be very efficiently used in the case of very complex problems or if there is no algorithmic solution available [12].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Approaches to Determine Graphite Nodularity in Ductile Iron

Brait

Koppensteiner

Schindelbacher

et al. 2021

JCME

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The complex metallurgical interrelationships in the production of ductile cast iron can lead to enormous diﬀerences in graphite formation and local microstructure by small variations during production. Artiﬁcial intelligence algorithms were used to describe graphite formation, which is inﬂuenced by a variety of metallurgical parameters. Moreover, complex physical relationships in the formation of graphite morphology are also controlled by boundary conditions of processing, the eﬀect of which can hardly be assessed in everyday foundry operations. The inﬂuence of relevant input parameters can be predetermined using artiﬁcial intelligence based on conditions and patterns that occur simultaneously. By predicting the local graphite formation, measures to stabilise production were deﬁned and thereby the accuracy of structure simulations improved. In course of this work, the most important dominating variables, from initial charging to ﬁnal casting, were compiled and analysed with the help of statistical regression methods to predict the nodularity of graphite spheres. We compared the accuracy of the prediction by using Linear Regression, Gaussian Process Regression, Regression Trees, Boosted Trees, Support Vector Machines, Shallow Neural Networks and Deep Neural Networks. As input parameters we used 45 characteristics of the production process consisting of the basic information including the composition of the charge, the overheating time, the type of melting vessel, the type of the inoculant, the fading, and the solidification time. Additionally, the data of several thermal analysis, oxygen activity measurements and the final chemical analysis were included.Initial programme designs using machine learning algorithms based on neural networks achieved encouraging results. To improve the degree of accuracy, this algorithm was subsequently adapted and reﬁned for the nodularity of graphite.

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Improving Production Rate by Analyzing Wire-Electrical Discharge Machining Parameters and Developing a Prediction Model

Suresh,

Ramesh,

Natarajan

et al. 2024

Lecture Notes in Electrical Engineering

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Artificial neural network modeling to evaluate and predict the mechanical strength of duplex stainless steel during casting

Cited by 5 publications

References 30 publications

A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels

A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels

Artificial Intelligence Approaches to Determine Graphite Nodularity in Ductile Iron

Improving Production Rate by Analyzing Wire-Electrical Discharge Machining Parameters and Developing a Prediction Model

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