New Innovations in Laser Hybrid Welding Processing and Monitoring

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: 30 November 2024 | Viewed by 48

Special Issue Editor


E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi'an Jiaotong University, Xi’an 710049, China
Interests: monitoring and on-line defect detection of laser welding; laser additive manufacturing; laser shock peening; acoustic emission monitoring; acoustic-optic fusion monitoring; sensor fusion; deep learning and data-driven-based intelligent manufacturing

Special Issue Information

Dear Colleagues,

Laser hybrid welding is a high-quality and efficient welding method that combines laser with other heat sources (such as arc, resistance heat, induction heat, etc.) and can optimize the heat input distribution in the welding process, improve the microstructure and mechanical properties of welded joints, and make up for the shortcomings of pure laser welding. It has been widely used in many fields including aerospace, automotive, and so on. Moreover, AI has become an important tool in terms of revealing the mechanisms and optimizing the process. Hence, due to the emerging AI technologies, we can learn more about the process of laser hybrid welding and produce complex parts of major equipment more precisely and more effectively with new monitoring methods and accurate controlling assistants with various deep learning technologies.

In this Special Issue of JMMP, we are looking for recent findings that focus on laser hybrid welding technologies including their application and associated research fields. Papers will be considered that show significant advancement according to the progress and quality of laser hybrid welding processing and mechanism aspects, as well as process monitoring, defect prediction, and process control.

We are interested in contributions that focus on topics such as:

  • New materials and new processing technology of laser hybrid welding;
  • Defect monitoring, on-line detecting, controlling and its forming mechanism;
  • Multi-source information fusion monitoring and its application to laser hybrid welding;
  • Explainable deep learning methods and their application to the laser hybrid welding process and parameter optimization;
  • Performance evaluation and prediction of typical parts in laser hybrid welding processing;
  • Large-sized parts processing and monitoring of laser hybrid welding.

Dr. Zhifen 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 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 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 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

  • laser hybrid welding
  • fusion monitoring
  • process control
  • AI
  • deep learning

Published Papers

This special issue is now open for submission.
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