Laser Ablation and Precision Cutting of Sheet Metal

A special issue of Metals (ISSN 2075-4701).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 593

Special Issue Editor


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Guest Editor
ETH Zurich, Zurich, Switzerland
Interests: metal cutting; laser materials processing; numerical modeling and simulation; machine learning

Special Issue Information

Dear Colleagues,

Laser technology has revolutionized the sheet metal processing industry, enabling precise, efficient, and environmentally friendly cutting and ablation processes. This Special Issue aims to collate cutting-edge research results and generate valuable insights into the recent developments, challenges, and innovations in this field.

Researchers and experts from academia and industry are invited to submit their original research and review articles that address various aspects of the laser ablation and precision cutting of sheet metals. Topics of interest include, but are not limited to, the following:

  1. Advanced laser systems and beam profiling techniques;
  2. Novel materials and alloys with tailored or improved properties;
  3. Surface quality, integrity, and microstructure analysis;
  4. Real-world applications in aerospace, automotive, and electronics industries;
  5. Environmental and sustainability considerations;
  6. Process optimization, modeling, and simulation;
  7. The deployment of artificial intelligence (AI) and machine learning (ML) algorithms.

The primary aim of this Special Issue is to provide a unique opportunity to disseminate cutting-edge research, share practical insights, and foster collaboration among experts in the field. We encourage submissions that highlight innovative techniques, address real-world challenges, and push the boundaries of laser-based sheet metal processing and ablation technologies.

Dr. Mohamadreza Afrasiabi
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. Metals 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 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

  • laser ablation
  • material removal
  • precision cutting
  • sheet metal

Published Papers (1 paper)

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Research

15 pages, 54480 KiB  
Article
Vision-Assisted Probabilistic Inference of Milling Stability through Fully Bayesian Gaussian Process
by Vahid Ostad Ali Akbari, Andrea Eichenberger and Konrad Wegener
Metals 2024, 14(7), 739; https://0-doi-org.brum.beds.ac.uk/10.3390/met14070739 - 21 Jun 2024
Viewed by 326
Abstract
This paper presents a physics-free Bayesian approach for the learning and inference of probabilistic stability charts in milling operations. The approach does not require any information from machine tool structural dynamics or cutting force coefficients, and the underlying learning algorithm can operate with [...] Read more.
This paper presents a physics-free Bayesian approach for the learning and inference of probabilistic stability charts in milling operations. The approach does not require any information from machine tool structural dynamics or cutting force coefficients, and the underlying learning algorithm can operate with limited training data. A Fully Bayesian Gaussian Process with distributions on its kernel hyperparameters is employed to enable information transfer between different machine and process configurations. The vision system further automates the detection of necessary dimensions from the tool–holder assembly in the machine’s tool magazine, further enhancing the applicability of the approach. Experiments demonstrated the effectiveness of this approach, offering great promise as an industry-friendly solution. Full article
(This article belongs to the Special Issue Laser Ablation and Precision Cutting of Sheet Metal)
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