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Metals
Special Issues
Optimization and Machine Learning in Metal Additive Manufacturing
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Optimization and Machine Learning in Metal Additive Manufacturing

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Additive Manufacturing".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 1245

Special Issue Editor

Prof. Dr. Chris Aldrich

E-Mail Website
Guest Editor
Western Australian School of Mines, Minerals, Energy and Chemical Engineering, Curtin University, Perth, WA 6845, Australia
Interests: mineral processing; extractive metallurgy; physical metallurgy; corrosion; artificial intelligence; process automation; machine learning; complex systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unlike traditional subtractive manufacturing methods that involve cutting or shaping a material to obtain the desired object, additive manufacturing (AM) builds three-dimensional objects by adding successive layers of material until the final product is formed. This technology has gained significant attention and has various applications across industries, owing to its versatility and ability to produce complex geometries.

Although AM has seen significant advancements in recent years, it still faces several challenges that impact its widespread adoption and implementation. This includes issues related to material limitations, process control and reproducibility, post-processing requirements, scale and speed, and design and cost.

In response to these challenges, machine learning has emerged as an active area of research and development, aiming to improve efficiency, reliability, and overall capabilities of additive manufacturing processes.

In this Special Issue, we welcome articles on advances in the application of artificial intelligence and machine learning in metal additive manufacturing, related among other to:

  • Material characterization and selection, including through the analysis of material properties, performance data, and historical trends to identify suitable materials for specific applications and to predict their behavior in AM processes.
  • Design optimization by leveraging machine learning algorithms to generate and evaluate numerous design iterations to identify optimal geometries, lattice structures, and support strategies that improve performance, reduce weight, or enhance specific properties.
  • Process variable optimization, where machine learning models can be used to identify the optimal combination of variable settings and process parameters for achieving desired outcomes.
  • Process monitoring, defect detection, and quality control, where machine learning models can exploit images, sensor data, or acoustic signals to identify defects and assist in corrective action.

Prof. Dr. Chris Aldrich
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

  • metal additive manufacturing
  • 3D printing
  • artificial intelligence
  • machine learning
  • material characterization
  • anomaly detection
  • process monitoring
  • design optimization

Published Papers (1 paper)

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Research

21 pages, 3566 KiB  
Article
Anomaly Detection in WAAM Deposition of Nickel Alloys—Single-Material and Cross-Material Analysis
by Aditya Rajesh, Wei Ya and Marcel Hermans
Metals 2023, 13(11), 1820; https://doi.org/10.3390/met13111820 - 28 Oct 2023
Viewed by 979
Abstract
The current research work investigates the possibility of using machine learning models to deduce the relationship between WAAM (wire arc additive manufacturing) sensor responses and defect presence in the printed part. The work specifically focuses on three materials from the nickel alloy family [...] Read more.
The current research work investigates the possibility of using machine learning models to deduce the relationship between WAAM (wire arc additive manufacturing) sensor responses and defect presence in the printed part. The work specifically focuses on three materials from the nickel alloy family (Inconel 718, Invar 36 and Inconel 625) and uses three sensor responses for data analysis, which are welding voltage, welding current and welding audio. Two different machine learning models are used—artificial neural networks (ANNs) and random forests (RF). The results for each of the materials, separately, indicate that the accuracies range from 60% to 90% and the correlation coefficient is less than 0.5 (indicating weak positive correlation), depending on the model and material. In addition to separate material analysis, a cross-material data analysis was formed to test the models’ general prediction capabilities. This led to predictions that are significantly worse, with accuracies ranging from 20% to 27% and very weak correlation coefficients (less than 0.1), indicating that the choice of material is still important as a boundary condition. Analysis of the results indicates that the relative importance of audio sensor response depends on the nature of defect formation. Random forests are found to perform the best for single material analysis, with the comparatively inferior performance of ANNs possibly being due to lack of sufficient datapoints. Full article
(This article belongs to the Special Issue Optimization and Machine Learning in Metal Additive Manufacturing)
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