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Metals
Special Issues
Advances in Constitutive Modeling for Metals and Alloys
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Advances in Constitutive Modeling for Metals and Alloys

A special issue of Metals (ISSN 2075-4701).

Deadline for manuscript submissions: closed (31 July 2025) | Viewed by 893

Special Issue Editors

Dr. Edoardo Mancini

E-Mail Website
Guest Editor
DIIIE, Università degli studi dell’Aquila, Piazzale Ernesto Pontieri, Monteluco di Roio, 67100 L’Aquila, Italy
Interests: constitutive modeling; inverse methods; mechanical behavior at high strain rate; finite element analysis procedure; Finite Element Analysis (FEA)
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Dell’Avvocato

E-Mail Website
Guest Editor Assistant
DIIIE, Università degli studi dell’Aquila, Piazzale Ernesto Pontieri, Monteluco di Roio, 67100 L’Aquila, Italy
Interests: thermography; material characterization; ndt; mechanical characterization; materials; steel; composites; laser thermography; welding assessment

Special Issue Information

Dear Colleagues,

In order to achieve a detailed insight into the deformation and stress distribution within a sample or a component by finite element modeling, it is clear that the quality of the results depends on the quality of the material constitutive model chosen and the accuracy of the its calibration.

To describe and refine the description of the visco-plastic behaviour of the material and to evaluate the plastic strain and sometimes the stress triaxiality, the latter to determine the damage evolution inside the material, especially in tensile loading where premature failure can occur at limited strains, it is of the primary importance to identify the more appropriate analytical model. Sometimes, the models available in the literature are not appropriate and require improvement to better reproduce the experimental evidence and the physical behaviour of the material.

This Special Issue aims to present constitutive material models related to numerical modeling through finite element analysis, model parameter optimizations, and their improvement. Papers on constitutive models of metals and alloys as well as papers on metals produced by additive manufacturing and their behaviour at high strain rates are welcome. Calibration methodologies are also of interest for this special issue.

Dr. Edoardo Mancini
Guest Editor

Dr. Giuseppe Dell’Avvocato
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • constitutive models
  • visco-plastic behaviour
  • metals alloys
  • metals testing
  • calibration procedure
  • finite element analysis

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Published Papers (1 paper)

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Research

24 pages, 6554 KiB  
Article
Modeling Mechanical Properties of Industrial C-Mn Cast Steels Using Artificial Neural Networks
by Saurabh Tiwari, Seongjun Heo, Nokeun Park and Nagireddy Gari S. Reddy
Metals 2025, 15(7), 790; https://doi.org/10.3390/met15070790 - 12 Jul 2025
Viewed by 269
Abstract
This study develops a comprehensive artificial neural network (ANN) model for predicting the mechanical properties of carbon–manganese cast steel, specifically, the yield strength (YS), tensile strength (TS), elongation (El), and reduction of area (RA), based on the chemical composition (16 alloying elements) and [...] Read more.
This study develops a comprehensive artificial neural network (ANN) model for predicting the mechanical properties of carbon–manganese cast steel, specifically, the yield strength (YS), tensile strength (TS), elongation (El), and reduction of area (RA), based on the chemical composition (16 alloying elements) and heat treatment parameters. The neural network model, employing a 20-44-44-4 architecture and trained on 400 samples from an industrial dataset of 500 samples, achieved 90% of test predictions within a 5% deviation from actual values, with mean prediction errors of 3.45% for YS and 4.9% for %EL. A user-friendly graphical interface was developed to make these predictive capabilities accessible, without requiring programming expertise. Sensitivity analyses revealed that increasing the copper content from 0.05% to 0.2% enhanced the yield strength from 320 to 360 MPa while reducing the ductility, whereas niobium functioned as an effective grain refiner, improving both the strength and ductility. The combined effects of carbon and manganese demonstrated complex synergistic behavior, with the yield strength varying between 280 and 460 MPa and the tensile strength ranging from 460 to 740 MPa across the composition space. Optimal strength–ductility balance was achieved at moderate compositions of 1.0–1.2 wt% Mn and 0.20–0.24 wt% C. The model provides an efficient alternative to costly experimental trials for optimizing C-Mn steels, with prediction errors consistently below 6% compared with 8–20% for traditional empirical methods. This approach establishes quantitative guidelines for designing complex multi-element alloys with targeted mechanical properties, representing a significant advancement in computational material engineering for industrial applications. Full article
(This article belongs to the Special Issue Advances in Constitutive Modeling for Metals and Alloys)
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