Data-Driven Modeling, Simulation and Design for Additive Manufacturing
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".
Deadline for manuscript submissions: 20 May 2024 | Viewed by 142
Special Issue Editors
Interests: additive manufacturing; microstructure simulation; welding and laser cladding; spray; gas atomization
2. Department of Materials Science and Engineering, Monash University, Melbourne, VIC 3800, Australia
Interests: additive manufacturing; design optimization; process optimization; lattice structures; smart manufacturing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As additive manufacturing (AM) technology surges forward, it is important to remain aligned with the most recent developments in modeling and simulation methodologies. Such advancements not only foster effective design for additive manufacturing (DfAM), but also provide guidance on process planning to ensure the desired properties are obtained and to control the uncertainties. This Special Issue delves extensively into AM with an acute focus on the mathematical and numerical strategies that underpin our comprehension and predictions concerning material behaviors and the AM workflow.
A novel and particularly exciting avenue being explored is the application of machine learning in AM. By adopting data-driven methodologies, these machine learning techniques are used to meticulously analyze vast datasets collected from AM processes. These data-driven insights bring forth remarkable enhancements in the realms of the efficiency, precision, and overall capabilities of AM. Furthermore, predictions derived from these models are not just theoretical musings; they offer tangible, actionable insights right at the inception stage of design and planning.
By encompassing both traditional models and emerging machine learning methods, this Special Issue aims to have a holistic perspective on the future trajectory of additive manufacturing.
It is our pleasure to invite you to submit your work to this Special Issue. Research papers, reviews, and communications are welcome.
Dr. Jinghao Li
Dr. Yunlong Tang
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 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. Materials is an international peer-reviewed open access semimonthly 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
- additive manufacturing
- modeling and simulation
- machine learning
- design for additive manufacturing
- process planning
- material behaviors
- uncertainty control
