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Advanced Materials, Machinability and Intelligent Manufacturing Systems.

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Title: Advanced Materials, Machinability and Intelligent Manufacturing Systems.
Authors: Burduk, Anna1 (AUTHOR) kamil.krot@pwr.edu.pl, Batako, Andre2 (AUTHOR), Michael, Anthony Xavior3 (AUTHOR), Butdee, Suthep4 (AUTHOR), Machado, Jose5 (AUTHOR), Krot, Kamil1 (AUTHOR)
Source: Materials (1996-1944). Apr2026, Vol. 19 Issue 7, p1435. 5p.
Subjects: Machining, Manufacturing process automation, Composite materials, Artificial intelligence, Smart materials, Machine learning, Electric metal-cutting, Sustainability
Abstract: This article focuses on recent advances in the integration of advanced materials, machinability, and intelligent manufacturing systems, highlighting research that addresses challenges in processing high-performance materials and optimizing manufacturing processes. It discusses the application of machine learning and artificial intelligence methods to improve process prediction, decision support, and material selection in industries such as aerospace, automotive, and energy. Key contributions include studies on electrical discharge machining (EDM) optimization, manufacturability assessment for small-batch production, composite laminate reinforcement, adhesive joint quality, and sustainable thermal management materials. The article also reviews developments in metal forming models, ultrasonic liquid metal processing, and precision forming technologies relevant to emerging energy systems, emphasizing the growing role of intelligent computational tools in enhancing manufacturing efficiency and product reliability. [Extracted from the article]
Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 192958797
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Advanced Materials, Machinability and Intelligent Manufacturing Systems.
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  Data: <searchLink fieldCode="JN" term="%22Materials+%281996-1944%29%22">Materials (1996-1944)</searchLink>. Apr2026, Vol. 19 Issue 7, p1435. 5p.
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  Data: <searchLink fieldCode="DE" term="%22Machining%22">Machining</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+process+automation%22">Manufacturing process automation</searchLink><br /><searchLink fieldCode="DE" term="%22Composite+materials%22">Composite materials</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Smart+materials%22">Smart materials</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+metal-cutting%22">Electric metal-cutting</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainability%22">Sustainability</searchLink>
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  Data: This article focuses on recent advances in the integration of advanced materials, machinability, and intelligent manufacturing systems, highlighting research that addresses challenges in processing high-performance materials and optimizing manufacturing processes. It discusses the application of machine learning and artificial intelligence methods to improve process prediction, decision support, and material selection in industries such as aerospace, automotive, and energy. Key contributions include studies on electrical discharge machining (EDM) optimization, manufacturability assessment for small-batch production, composite laminate reinforcement, adhesive joint quality, and sustainable thermal management materials. The article also reviews developments in metal forming models, ultrasonic liquid metal processing, and precision forming technologies relevant to emerging energy systems, emphasizing the growing role of intelligent computational tools in enhancing manufacturing efficiency and product reliability. [Extracted from the article]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/ma19071435
    Languages:
      – Code: eng
        Text: English
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        PageCount: 5
        StartPage: 1435
    Subjects:
      – SubjectFull: Machining
        Type: general
      – SubjectFull: Manufacturing process automation
        Type: general
      – SubjectFull: Composite materials
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Smart materials
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Electric metal-cutting
        Type: general
      – SubjectFull: Sustainability
        Type: general
    Titles:
      – TitleFull: Advanced Materials, Machinability and Intelligent Manufacturing Systems.
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      – PersonEntity:
          Name:
            NameFull: Burduk, Anna
      – PersonEntity:
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            NameFull: Batako, Andre
      – PersonEntity:
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            NameFull: Michael, Anthony Xavior
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            NameFull: Butdee, Suthep
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            NameFull: Machado, Jose
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            NameFull: Krot, Kamil
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          Dates:
            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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              Value: 19961944
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              Value: 19
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              Value: 7
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            – TitleFull: Materials (1996-1944)
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