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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: 20 November 2025 | Viewed by 502

Special Issue Editor


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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

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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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Published Papers (1 paper)

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Research

16 pages, 35029 KiB  
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
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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