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Intelligent Processing Technology of Materials
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Intelligent Processing Technology of Materials

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: closed (20 November 2025) | Viewed by 1672

Special Issue Editor

Dr. Yanming Zhang

E-Mail Website
Guest Editor
Gradual School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
Interests: advanced processing technology of materials; laser-material interaction; intelligent monitoring technology; modelling and simulation; intelligent control

Special Issue Information

Dear Colleagues,

The intelligent processing technology of materials is a valuable and interesting methodology used for simulating and controlling the processing of materials, which requires strong power to achieve the precise and efficient processing of high-performance components. It enables intelligent behavior in the machining system, including the modelling, sensing, processing optimization, and control of process variables and performance, as well as the monitoring and feedback technology of the process. It has been widely applied in the processing of metals, non-metallic materials, and composite materials.

The main purpose of this Special Issue “Intelligent Processing Technology of Materials” is to showcase the benefits of applying artificial intelligence, machine/deep learning, intelligent algorithms, and intelligent monitoring technology to the processing of materials. Different processing technologies of materials, such as high-precision machining, non-traditional machining, additive manufacturing, forming, and so on are all welcome. I am pleased to invite researchers to contribute original research articles and review articles. Potential research areas may include (but are not limited to) the following:

  • Intelligent control systems;
  • Model-based intelligent process optimizations;
  • Machine/deep learning methods applied to the processing of materials;
  • Intelligent monitoring and feedback technology;
  • The intelligent optimization of processing parameters;
  • Big data and cloud-based processing.

I look forward to receiving your contributions.

Dr. Yanming Zhang
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 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. 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

  • control system
  • modelling
  • machine/deep learning
  • monitoring
  • parameter optimization
  • big data

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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

13 pages, 3595 KB  
Article
Study on the Application of Machine Learning of Melt Pool Geometries in Silicon Steel Fabricated by Powder Bed Fusion
by Ho Sung Jang, Sujeong Kim, Jong Bae Jeon, Donghwi Kim, Yoon Suk Choi and Sunmi Shin
Materials 2026, 19(1), 68; https://doi.org/10.3390/ma19010068 - 24 Dec 2025
Abstract
In this study, regression-based machine learning models were developed to predict the melt pool width and depth formed during the Laser Powder Bed Fusion (LPBF) process for Fe-3.4Si and Fe-6Si alloys. Based on experimentally obtained melt pool width and depth data, a total [...] Read more.
In this study, regression-based machine learning models were developed to predict the melt pool width and depth formed during the Laser Powder Bed Fusion (LPBF) process for Fe-3.4Si and Fe-6Si alloys. Based on experimentally obtained melt pool width and depth data, a total of 11 regression models were trained and evaluated, and hyperparameters were optimized via Bayesian optimization. Key process parameters were identified through data preprocessing and feature engineering, and SHAP analysis confirmed that the input energy had the strongest influence on both melt pool width and depth. The comparison of prediction performance revealed that the support vector regressor with a linear kernel (SVR_lin) exhibited the best performance for predicting melt pool width, while the multilayer perceptron (MLP) model achieved the best results for predicting melt pool depth. Based on these trained models, a power–velocity (P-V) process map was constructed, incorporating boundary conditions such as the overlap ratio and the melt pool morphology. The optimal input energy range was derived as 0.45 to 0.60 J/mm, ensuring stable melt pool formation. Specimens manufactured under the derived conditions were analyzed using 3D X-ray CT, revealing porosity levels ranging from 0.29% to 2.89%. In particular, the lowest porosity was observed under conduction mode conditions when the melt pool depth was approximately 1.0 to 1.5 times the layer thickness. Conversely, porosity tended to increase in the transition mode and lack of fusion regions, consistent with the model predictions. Therefore, this study demonstrated that a machine learning-based regression model can reliably predict melt pool characteristics in the LPBF process of Fe-Si alloys, contributing to the development of process maps and optimization strategies. Full article
(This article belongs to the Special Issue Intelligent Processing Technology of Materials)
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16 pages, 35029 KB  
Article
Effects of Process Parameters on Defect Formation in Laser Additive Manufacturing of a Novel Ni-Based Superalloy
by Wen-Tao Liu, Jing-Cheng Zhou, Jing-Jing Ruan, Hua Zhang, Xin Zhou, Liang Jiang and Li-Long Zhu
Materials 2025, 18(13), 3102; https://doi.org/10.3390/ma18133102 - 1 Jul 2025
Cited by 1 | Viewed by 1026
Abstract
Laser additive manufacturing offers significant advantages for fabricating and repairing complex components. However, the complex solidification and remelting processes in nickel-based superalloys for additive manufacturing can introduce defects such as voids and cracks. Therefore, process parameters are crucial, as they significantly impact solidification [...] Read more.
Laser additive manufacturing offers significant advantages for fabricating and repairing complex components. However, the complex solidification and remelting processes in nickel-based superalloys for additive manufacturing can introduce defects such as voids and cracks. Therefore, process parameters are crucial, as they significantly impact solidification and remelting, thereby affecting defect formation. In this study, laser-directed energy deposition was employed to evaluate the effects of our key process parameters on the formation of voids and cracks in a novel superalloy. The findings reveal that laser power and linear energy density significantly influence the void content and crack density. However, the influence of other process parameters on defect formation is relatively minimal. The optimal parameter space is characterized by a laser power range of 600~700 W, a linear energy density range of 60~90 J/mm and a powder feeding rate of 0.7~0.8 rpm. Moreover, the precipitation of fine MC-type carbides near the dendrites and grain-boundary misorientations within the range of 31~42° are associated with a higher propensity for crack formation. These insights provide a valuable reference for controlling the process parameters and understanding the cracking mechanisms in laser additive manufacturing of superalloys. Full article
(This article belongs to the Special Issue Intelligent Processing Technology of Materials)
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