Advances in Virtual Prototyping of Mechanical Systems for Design and Manufacturing

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machine Design and Theory".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1043

Special Issue Editors


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Guest Editor
Enzo Ferrari Department of Engineering, University of Modena and Reggio Emilia, Via P. Vivarelli 10, 41125 Modena, Italy
Interests: design methods; design and simulation of foundry equipment; design by simulation; human intelligent system integration; advanced driver assistance systems; vehicle actuations and autonomous driving
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Department of Mechanical Engineering, Energetics, Management and Transportation, University of Genoa, Via all’Opera Pia 15/A, 16145 Genova, Italy
2. Department of Advanced Robotics, Istituto Italiano di Tecnologia, Via S. Quirico 19d, 16163 Genova, Italy
Interests: robot design; compliant mechanisms; smart-material-based transducers; variable stiffness actuators
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Virtual Prototyping (VP) refers to the development of digital models that simulate the behavior and performance of physical systems, allowing evaluation and testing before any physical prototype is built. Utilizing tools such as Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE), as well as other advanced modeling technologies, VP is a core pillar of modern competitive design and manufacturing.

VP fosters collaboration by streamlining feedback loops and bridging the gap between product design and manufacturing. It accelerates time-to-market by identifying design flaws and production issues early in the life cycle, while also supporting rapid iterations and modifications. By enabling comprehensive multiphysics simulations, VP contributes to improved product quality. Furthermore, it significantly reduces costs by reducing the need for expensive physical prototypes and minimizing downtime caused by trial-and-error setups on the shop floor. In addition, VP promotes innovation by encouraging risk-free experimentation and enhances the reliability of design processes by leveraging optimization techniques to effectively manage system parameters.

This Special Issue aims to highlight the latest advancements in VP for product and production system design, along with optimization techniques and the integration of digital twins within the framework of Industry 4.0, providing a platform for researchers and practitioners to disseminate their latest findings and innovations in these fields.

Research topics that are of interest for this Special Issue include, but are not limited to:

  • Process modelling, simulation, and optimization;
  • Product modelling, simulation, and optimization;
  • Multiphysics modeling;
  • Assembly simulation;
  • Computer-aided tolerancing;
  • Digital manufacturing twin;
  • Integrated design and process optimization;
  • Ergonomic and human factors simulation;
  • Virtual commissioning;
  • Resource efficiency;
  • Energy saving;
  • Integrated part and process design;
  • Industry 4.0 applications;
  • Case studies and applications in specific industries.

Dr. Alberto Vergnano
Prof. Dr. Giovanni Berselli
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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. Machines 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 2400 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.

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Published Papers (1 paper)

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Research

18 pages, 1563 KB  
Article
Composition-Aware SDAS Prediction in Recycled Aluminum Alloys via Physics-Informed Machine Learning Guided by Analytical Solidification Physics
by Hamed Rezvanpour, Alberto Vergnano, Paolo Veronesi and Francesco Leali
Machines 2026, 14(3), 311; https://doi.org/10.3390/machines14030311 - 10 Mar 2026
Viewed by 559
Abstract
The mechanical performance of secondary aluminum alloys depends on Secondary Dendrite Arm Spacing (SDAS). Commercial casting simulations accurately predict local thermal history but typically neglect the influence of compositional variability on SDAS by using fixed material constants. This study introduces a physics-informed machine [...] Read more.
The mechanical performance of secondary aluminum alloys depends on Secondary Dendrite Arm Spacing (SDAS). Commercial casting simulations accurately predict local thermal history but typically neglect the influence of compositional variability on SDAS by using fixed material constants. This study introduces a physics-informed machine learning framework to bridge macroscopic process simulation and microscopic solidification physics. A computational Design of Experiments covering 500 AlSi7 alloy variants was generated, and a theoretical SDAS ground truth was calculated using an analytical model incorporating the growth restriction factor. A Gradient Boosting Regressor surrogate was trained to predict the physics-informed SDAS from thermal and chemical inputs. The analysis reveals a solute sensitivity gap, where standard simulations misestimate SDAS by up to 20% for high-impurity batches. The surrogate model captures this variance (R2=0.95, MAE=0.24μm), enabling rapid, composition-specific microstructural prediction without additional simulation cost. This approach supports the reliable simulation of casting with secondary alloys, where the composition can be hardly considered constant. Full article
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