AI in Laser Materials Processing

Special Issue Editor


E-Mail Website
Guest Editor
The College of Optics & Photonics, University of Central Florida, 4304 Scorpius St., Orlando, FL 32816-2700, USA
Interests: laser materials processing; ultrafast laser; laser-matter interaction; laser-induced photo-physics and photo-chemistry; two-photon polymerization; beam shaping; nanolithography; additive manufacturing; modeling and simulation of laser materials processing

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is transforming laser materials processing by enabling smarter, more efficient, and adaptive manufacturing. AI-driven approaches, including machine learning, physics-informed modeling, and real-time process control, are rapidly transforming laser-based manufacturing by improving process stability, optimizing parameters, and enabling autonomous decision-making.

In this Special Issue of Journal of Manufacturing and Materials Processing (JMMP) , we seek recent advances in AI-enhanced laser materials processing, including innovations in data-driven modeling, in situ monitoring, and adaptive control strategies. We welcome contributions that address fundamental and applied research aimed at improving process understanding, enhancing material performance, and expanding the capabilities of laser-based manufacturing techniques.

We are particularly interested in research topics such as:

  • AI-driven process optimization for laser cutting, welding, drilling, and additive manufacturing.
  • Machine learning for real-time monitoring and defect detection in laser processing.
  • Predictive modeling and digital twins for laser–material interactions.
  • AI-enhanced control strategies for adaptive and autonomous laser machining.
  • Data-driven insights into material behavior and process dynamics in laser processing.

Dr. Xiaoming Yu
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. Journal of Manufacturing and Materials Processing 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

  • artificial intelligence
  • machine learning
  • digital twins
  • real-time monitoring
  • physics-informed optimization
  • laser machining
  • additive manufacturing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 4220 KiB  
Article
Predicting the Relative Density of Stainless Steel and Aluminum Alloys Manufactured by L-PBF Using Machine Learning
by José Luis Mullo, Iván La Fé-Perdomo, Jorge Ramos-Grez, Ángel F. Moreira Romero, Alejandra Ramírez-Albán, Mélany Yarad-Jácome and Germán Omar Barrionuevo
J. Manuf. Mater. Process. 2025, 9(6), 185; https://doi.org/10.3390/jmmp9060185 - 3 Jun 2025
Viewed by 194
Abstract
Metal additive manufacturing is a disruptive technology that is changing how various alloys are processed. Although this technology has several advantages over conventional manufacturing, it is still necessary to standardize its properties, which are dependent on the relative density (RD). In addition, since [...] Read more.
Metal additive manufacturing is a disruptive technology that is changing how various alloys are processed. Although this technology has several advantages over conventional manufacturing, it is still necessary to standardize its properties, which are dependent on the relative density (RD). In addition, since experimental designs are costly, one solution is using machine learning algorithms that allow the effects of variations in the processing parameters on the resulting density of the additively manufactured components to be anticipated. This work assembled a database based on data from 673 observations and 10 predictors to forecast the relative density of 316L stainless steel and AlSi10Mg components produced by laser powder bed fusion (L-PBF). LazyPredict was employed to select the algorithm that best models the variability of the inherent data. Ensemble boosting regressors offer higher accuracy, providing hyperparameter fitting and optimization advantages. The predictions’ precision for aluminum and stainless steel obtained an R2 value greater than 0.86 and 0.83, respectively. The results of the SHAP values indicated that laser power and energy density are the parameters that have the greatest impact on the predictability of the relative density of Al-Si10-Mg and SS 316L materials processed by L-PBF. This study presents a compendium of data for the additive fabrication of stainless steel and aluminum alloys, offering researchers a guide to understanding how processing parameters influence RD. Full article
(This article belongs to the Special Issue AI in Laser Materials Processing)
Show Figures

Figure 1

Back to TopTop