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Machines
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Intelligent Welding
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Intelligent Welding

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 705

Special Issue Editor

Dr. Hessamoddin Moshayedi

E-Mail Website
Guest Editor
Institute for Steel Construction, Leibniz University Hannover, Hannover, Germany
Interests: robotic welding; wire-arc additive manufacturing; data-driven models

Special Issue Information

Dear Colleagues,

Nowadays, welding processes are becoming increasingly complex, with novel parameters to address the new requirements of users and customers, particularly in dynamic environments. As welding moves towards more customized production, next-generation welding systems should be able to intelligently adjust to changing welding tasks while maintaining high quality. Advancements in computer science, control theory, robotics, and machine learning are facilitating intelligent automation, real-time monitoring, analysis, process control, and decision making, i.e., areas of exploration in manufacturing research initiatives such as Industry 5.0 and smart manufacturing. There is a significant demand for the implementation of such new technologies to reduce manufacturing time, expand the range of welding applications, and enhance overall product quality. As such, there is a necessity to explore and integrate cutting-edge solutions that can help to achieve these objectives.

This Special Issue calls for papers that present innovative works on the improvement of the concepts, technologies, and system architectures of the welding processes, including sensing, monitoring and signal processing, feature extraction and selection, real-time modelling, decision-making, learning and developing of intelligent welding systems, and digital twin systems for welding processes.

Dr. Hessamoddin Moshayedi
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. Machines 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 2400 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

  • intelligent welding systems
  • machine learning
  • artificial intelligence
  • monitoring and control
  • advanced modelling
  • digital twins for welding

Published Papers (2 papers)

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Research

20 pages, 4187 KiB  
Article
A Neural-Network-Based Cost-Effective Method for Initial Weld Point Extraction from 2D Images
by Miguel-Angel Lopez-Fuster, Arturo Morgado-Estevez, Ignacio Diaz-Cano and Francisco J. Badesa
Machines 2024, 12(7), 447; https://doi.org/10.3390/machines12070447 (registering DOI) - 28 Jun 2024
Viewed by 127
Abstract
This paper presents a novel approach for extracting 3D weld point information using a two-stage deep learning pipeline based on readily available 2D RGB cameras. Our method utilizes YOLOv8s for object detection, specifically targeting vertices, followed by semantic segmentation for precise pixel localization. [...] Read more.
This paper presents a novel approach for extracting 3D weld point information using a two-stage deep learning pipeline based on readily available 2D RGB cameras. Our method utilizes YOLOv8s for object detection, specifically targeting vertices, followed by semantic segmentation for precise pixel localization. This pipeline addresses the challenges posed by low-contrast images and complex geometries, significantly reducing costs compared with traditional 3D-based solutions. We demonstrated the effectiveness of our approach through a comparison with a 3D-point-cloud-based method, showcasing the potential for improved speed and efficiency. This research advances the field of automated welding by providing a cost-effective and versatile solution for extracting key information from 2D images. Full article
(This article belongs to the Special Issue Intelligent Welding)
23 pages, 7155 KiB  
Article
Variable Layer Heights in Wire Arc Additive Manufacturing and WAAM Information Models
by Ethan Kerber, Heinrich Knitt, Jan Luca Fahrendholz-Heiermann, Emre Ergin, Sigrid Brell-Cokcan, Peter Dewald, Rahul Sharma and Uwe Reisgen
Machines 2024, 12(7), 432; https://doi.org/10.3390/machines12070432 - 25 Jun 2024
Viewed by 218
Abstract
In Wire Arc Additive Manufacturing (WAAM), variable layer heights enable the non-parallel or non-planar slicing of parts. In researching variable layer heights, this paper documents printing strategies for a reference geometry whose key features are non-orthogonal growth and unsupported overhangs. The complexity of [...] Read more.
In Wire Arc Additive Manufacturing (WAAM), variable layer heights enable the non-parallel or non-planar slicing of parts. In researching variable layer heights, this paper documents printing strategies for a reference geometry whose key features are non-orthogonal growth and unsupported overhangs. The complexity of 3D printing with welding requires parameter optimization to control the deposition of molten material. Thus, 3D printing with welding requires the precise deposition of molten material. Currently, there is no standard solution for the customization of process parameters and intelligent collection of data from sensors. To address this gap in technology, this research develops an Internet of Things (IoT)-enabled, distributed communication protocol to control process parameters, synchronize commands, and integrate device data. To intelligently collect sensor information, this research creates a query-able database during pre-planning and production. This contributes to fundamental research in WAAM by documenting strategies for printing variable layer heights, the customization of control parameters, and the collection of data through a WAAM Information Model (WIM). Full article
(This article belongs to the Special Issue Intelligent Welding)
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