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Metals
Special Issues
Simulation and Optimization of Steel and Metal Manufacturing Processes
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Simulation and Optimization of Steel and Metal Manufacturing Processes

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 1983

Special Issue Editor

Prof. Dr. Eric Feulvarch

E-Mail Website
Guest Editor
Laboratoire de Tribologie et Dynamique des Systèmes, Écully, France
Interests: computational mechanics; computational methods in manufacturing processes; nonlinear multi-physical models; FEM and XFEM; nitriding and carbo-nitriding processes; spot welding (SPW) processes; friction stir welding (FSW) processes; fusion welding processes (MAG); additive manufacturing (AM) processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

"Simulation and optimization of steel and metal manufacturing processes" has been a very active area of research over the past few decades. Significant advances in this field are the result of interdisciplinary multiphysics and multiscale studies in related areas of computational mechanics, constitutive material models, and mathematical analysis. At the same time, in the industrial production process, numerical simulation calculation plays an important role as an important tool for designing and optimizing the production process.

This Special Issue "Simulation and Optimization of Steel and Metal Manufacturing Process" will focus on the computational modeling and numerical simulation of the manufacturing process of metal materials, aiming to collect the latest progress in this field.

Prof. Dr. Eric Feulvarch
Guest Editor

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

  • computational modeling
  • metal
  • numerical simulation
  • finite elements
  • numerical methods
  • industrial applications.

Published Papers (2 papers)

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Research

20 pages, 3110 KiB  
Article
Predictive Modeling of Hardness Values and Phase Fraction Percentages in Micro-Alloyed Steel during Heat Treatment Using AI
by Ankur Bassi, Soham Tushar Bodas, Syed Shuja Hasan, Gaganpreet Sidhu and Seshasai Srinivasan
Metals 2024, 14(1), 49; https://doi.org/10.3390/met14010049 - 30 Dec 2023
Cited by 1 | Viewed by 991
Abstract
In this work, we have proposed an AI-based model that can simultaneously predict the hardness and phase fraction percentages of micro-alloyed steel with a predefined chemical composition and thermomechanical processing conditions. Specifically, the model uses a feed-forward neural network enhanced by the ensemble [...] Read more.
In this work, we have proposed an AI-based model that can simultaneously predict the hardness and phase fraction percentages of micro-alloyed steel with a predefined chemical composition and thermomechanical processing conditions. Specifically, the model uses a feed-forward neural network enhanced by the ensemble method. The model has been trained on experimental data derived from continuous cooling transformation (CCT) diagrams of 39 different steels. The inputs to the model include a cooling profile defined by a set of time-temperature values and the chemical composition of the steel. Sensitivity analysis was performed on the validated model to understand the impact of key input variables, including individual alloys and the thermomechanical processing conditions. This analysis, which measures the variability in output in response to changes in a specific input variable, showed excellent agreement with experimental data and the trends in the literature. Thus, our model not only predicts steel properties under varied cooling conditions but also aligns with existing theoretical knowledge and experimental data. Full article
(This article belongs to the Special Issue Simulation and Optimization of Steel and Metal Manufacturing Processes)
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20 pages, 8979 KiB  
Article
Sensitivity Study of Surface Roughness Process Parameters in Belt Grinding Titanium Alloys
by Yueru Shang, Sibo Hu and Hu Qiao
Metals 2023, 13(11), 1825; https://doi.org/10.3390/met13111825 - 30 Oct 2023
Viewed by 746
Abstract
In order to obtain the optimum range of process parameters for abrasive belt grinding of titanium alloys to achieve a surface roughness within a given range, titanium alloy TC4 was selected as the research object, and experiments on abrasive belt grinding surface roughness [...] Read more.
In order to obtain the optimum range of process parameters for abrasive belt grinding of titanium alloys to achieve a surface roughness within a given range, titanium alloy TC4 was selected as the research object, and experiments on abrasive belt grinding surface roughness were conducted. Firstly, an empirical formula for the surface roughness of titanium alloys after abrasive belt grinding was constructed based on the balanced weight analysis of the process parameters for titanium alloy surface roughness. Sensitivity analysis was carried out to identify the process parameters with the greatest effect on surface roughness, and the stable and unstable domains of the process parameters were determined. Combined with range analysis in orthogonal experiments, the influence curves of the process parameters on surface roughness were obtained, and the optimal parameter ranges were selected. The research results showed that surface roughness is the most sensitive to changes in abrasive grain size and the least sensitive to changes in abrasive belt linear speed. The optimal ranges of abrasive grain size, abrasive belt linear speed, and grinding pressure were determined to be 120# to 150#, 15 m/s to 20 m/s, and 10 N to 15 N, respectively. This study provides a theoretical method and experimental basis for the control of surface roughness in abrasive belt grinding of titanium alloys. Full article
(This article belongs to the Special Issue Simulation and Optimization of Steel and Metal Manufacturing Processes)
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