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Computation
Special Issues
Computational Approaches for Manufacturing
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Computational Approaches for Manufacturing

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2279

Special Issue Editor

Dr. Lichao Fang

E-Mail Website
Guest Editor
Materials Science and Engineering, Stanford University 450 Serra Mall, Stanford, CA 94305, USA
Interests: additive manufacturing; computational mechanics; heat transfer; X-ray experiment; fluid mechanics; machine learning; data-driven model; microstructure material

Special Issue Information

Dear Colleagues,

Manufacturing, such as additive manufacturing, welding, and casting, is a complex process for fabricating material and building parts, which involves heat transfer, fluid flow, evaporation, phase changes, and solidification. Computational methods have played a key role in obtaining a deep understanding of how manufacturing processes affect material microstructure, mechanical properties, and parts performance. The development of multi-physics models and data-driven models in manufacturing offers efficient ways to explore the fundamental science and findings related to the manufacturing process.

The aim of this Special Issue is to highlight the technologies and progress in simulations and data-driven models for manufacturing. Specific fields and topics of interest include, but are not limited to, the following:

  • Multiscale and multiphysics modeling;
  • Data-driven modeling;
  • Model calibration;
  • Reduced order modeling;
  • Uncertainty quantification;
  • Optimization and design;
  • Experimental image processing;
  • Image segmentation and analysis;
  • Heat transfer;
  • Alloy design;
  • Solidification modeling;
  • Process–structure property.

Dr. Lichao Fang
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. Computation 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 1800 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

  • multiphysics modeling
  • data-driven modeling
  • additive manufacturing
  • welding
  • casting
  • image processing
  • process–structure property

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

17 pages, 2522 KiB  
Article
Organization of the Optimal Shift Start in an Automotive Environment
by Gábor Lakatos, Bence Zoltán Vámos, István Aupek and Mátyás Andó
Computation 2025, 13(8), 181; https://doi.org/10.3390/computation13080181 - 1 Aug 2025
Viewed by 223
Abstract
Shift organizations in automotive manufacturing often rely on manual task allocation, resulting in inefficiencies, human error, and increased workload for supervisors. This research introduces an automated solution using the Kuhn-Munkres algorithm, integrated with the Moodle learning management system, to optimize task assignments based [...] Read more.
Shift organizations in automotive manufacturing often rely on manual task allocation, resulting in inefficiencies, human error, and increased workload for supervisors. This research introduces an automated solution using the Kuhn-Munkres algorithm, integrated with the Moodle learning management system, to optimize task assignments based on operator qualifications and task complexity. Simulations conducted with real industrial data demonstrate that the proposed method meets operational requirements, both logically and mathematically. The system improves the start of shifts by assigning simpler tasks initially, enhancing operator confidence and reducing the need for assistance. It also ensures that task assignments align with required training levels, improving quality and process reliability. For industrial practitioners, the approach provides a practical tool to reduce planning time, human error, and supervisory burden, while increasing shift productivity. From an academic perspective, the study contributes to applied operations research and workforce optimization, offering a replicable model grounded in real-world applications. The integration of algorithmic task allocation with training systems enables a more accurate matching of workforce capabilities to production demands. This study aims to support data-driven decision-making in shift management, with the potential to enhance operational efficiency and encourage timely start of work, thereby possibly contributing to smoother production flow and improved organizational performance. Full article
(This article belongs to the Special Issue Computational Approaches for Manufacturing)
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15 pages, 2875 KiB  
Article
A Frugal Approach Toward Modeling of Defects in Metal 3D Printing Through Statistical Methods in Finite Element Analysis
by Antonio Martínez Raya, Matías Braun, Cristina Carrasco-Garrido and Vicente F. González-Albuixech
Computation 2025, 13(2), 35; https://doi.org/10.3390/computation13020035 - 3 Feb 2025
Viewed by 1409
Abstract
Metal additive manufacturing has emerged as a revolutionary technology for the fabrication of high-complexity components. However, this technique presents unique challenges related to the structural integrity and final strength of the parts produced due to inherent defects, such as porosity, cracks, and geometric [...] Read more.
Metal additive manufacturing has emerged as a revolutionary technology for the fabrication of high-complexity components. However, this technique presents unique challenges related to the structural integrity and final strength of the parts produced due to inherent defects, such as porosity, cracks, and geometric deviations. These defects significantly impact the fatigue life of the material by acting as stress concentrators that accelerate failure under cyclic loading. On the one hand, this type of model is very complicated in its approach, since, even with encouraging results, the complexity of the calculation with these variables makes it difficult to obtain a simple result that allows for a generalized interpretation. On the other hand, using more familiar methods, it is possible to qualitatively guess the behavior that helps obtain results with better applicability, even at limited levels of precision. This paper presents a simplified finite element method combined with a statistical approach to model the presence of porosity in metal components produced by additive manufacturing. The proposed model considers a two-dimensional square plate subjected to tensile stress, with randomly introduced defects characterized by size, shape, and orientation. The percentage of porosity that affects each aspect determines the adjustment of the mechanical properties of finite elements. A series of simulations were performed to generate multiple models with random defect distributions to estimate maximum stress values. This approach demonstrates that complex models are not always necessary for a preliminary practical estimate of the effects of new manufacturing techniques. Furthermore, it demonstrates the potential for the extension of frugal computational techniques, which aim to minimize computational and experimental costs in the engineering field. The article discusses future research directions, particularly those related to potential business applications, including commercial uses. This follows a discussion of the existing limitations of this study. Full article
(This article belongs to the Special Issue Computational Approaches for Manufacturing)
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