Skip to main content
Log in

International Journal of Material Forming - Call for Papers: Topical Collection on Artificial Intelligence Empowering Composites Science and Technology

Guest Editors: 
Prof. Francisco Chinesta, Arts et Métiers Institute of Technology, France
Prof. Chady Ghnatios, University of North Florida, USA

Open for Submissions: February 2025 

Deadline for Submissions: February 2026


Description
Recently, machine learning techniques paved the way to novel possibilities in composite materials modelling and simulation, with multiple applications emerging daily. This Topical Collection focuses on the added value of data, machine learning and artificial intelligence in composites science and technology, aiming at addressing, among many other topics, monitoring, microstructure descriptors, scales bridging, modelling, properties and performances prediction and optimization, … at the level of materials, processing and structures, for design and operation.

How to submit
Each submitted paper will be assigned at least to three reviewers and a minimum of two reviewer’s reports will be required to label the paper as “required review completed” and making a decision on it. If the evaluations of the reviewers are conflicting the paper will be assigned to a third reviewer. The reviewers will be selected among the experts in the fields of forming processes and sustainable manufacturing. The peer review system (either Single anonymized review or double anonymized review) of IJMF will be used.

All submissions must be original and may not be under review by another publisher. Interested authors should consult the journal’s “Submission Guidelines” at https://www.springer.com/journal/12289/submission-guidelines.

Articles can be submitted through Editorial Manager: https://www.editorialmanager.com/ijfo/default.aspx

The Topical Collection is created as submission questionnaire in the system. When you submit your paper you will be asked if your paper belongs to a Topical Collection/Special Issue. If you answer yes, a pull down menu prompts up where you can select the title of the Topical Collection to which you are submitting your paper. 
Please indicate in your cover letter that you wish your manuscript to be considered for the Topical Collection on "Artificial Intelligence Empowering Composites Science and Technology". All submitted papers will be reviewed as soon as they are received. Accepted papers are published Online First until the complete Topical Collection is published.

Guest Editor Biographies

New Content ItemProf. Francisco Chinesta is currently a full Professor of computational physics at Arts et Metiers Institute of Technology (Paris, France), Honorary Fellow of the “Institut Universitaire de France” – IUF- and Fellow of the Spanish Royal Academy of Engineering. He is the president of the ESI Group scientific committee and director of its scientific department. He was (2008-2012) AIRBUS Group chair professor and since 2013 he is ESI Group chair professor on advanced modeling and simulation of materials, processes, structures and systems. He received many scientific awards, among them the IACM Fellow award, the IACM Zienkiewicz award (New York, 2018), the ESAFORM award, …. the Academic Palms, the French Order of Merit, … in 2018 the Doctorate Honoris Causa at the University of Zaragoza (Spain) and in 2019 the Silver medal from the French CNRS. He is author of about 440 papers in peer-reviewed international journals and more than 1200 contributions in conferences.

New Content ItemProf. Chady Ghnatios holds a PhD in mechanical engineering from Ecole Centrale Nantes, and HDR from CNAM-Paris. He served as Fulbright visiting associate professor to Stanford University, visiting professor to Toronto Metropolitan University, and full professor at Arts et Métiers institute of technology, Paris. Currently, he holds a professorial position at the University of North Florida. Among his grants is the SKF research chair on the scientific machine learning. His research interests are model reduction techniques, hybrid modelling and model augmentation with data.

Navigation