Special Issue "Application of Artificial Intelligence Techniques in Additive Manufacturing"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Advanced Digital and Other Processes".

Deadline for manuscript submissions: 30 November 2022 | Viewed by 378

Special Issue Editors

Dr. Yuchu Qin
E-Mail Website
Guest Editor
EPSRC Future Advanced Metrology Hub, Centre for Precision Technologies, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: computer-aided additive manufacturing; computational intelligence; knowledge engineering
Dr. Peizhi Shi
E-Mail Website
Guest Editor
EPSRC Future Advanced Metrology Hub, Centre for Precision Technologies, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: intelligent manufacturing; machine learning; artificial intelligence

Special Issue Information

Dear Colleagues,

Additive manufacturing (AM), commonly known as 3D printing, is a set of technologies that build 3D objects directly from 3D models via adding materials layer upon layer. Compared with traditional manufacturing technologies, AM provides a high degree of freedom for designing and obtaining complex geometric shapes without any additional cost. Currently, the application of artificial intelligence techniques in AM is gaining importance and popularity with academia. This has triggered a series of interesting research topics, such as AM data and knowledge representation, topology optimization for AM, intelligent design, process planning and quality inspection for AM, AM product quality prediction, and AM process monitoring. The aim of this Special Issue is to collect recent methods regarding the application of artificial intelligence techniques in AM.

Dr. Yuchu Qin
Dr. Peizhi Shi
Guest Editors

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. Processes 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 2000 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

  • AM data and knowledge representation
  • computer-aided design for AM
  • computer-aided process planning for AM
  • AM product quality prediction
  • AM process monitoring
  • intelligent quality inspection for AM
  • artificial intelligence
  • machine learning
  • intelligent computation
  • data and knowledge engineering

Published Papers (1 paper)

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Research

Article
Description Logic Ontology-Supported Part Orientation for Fused Deposition Modelling
Processes 2022, 10(7), 1290; https://doi.org/10.3390/pr10071290 - 30 Jun 2022
Viewed by 289
Abstract
Fused deposition modelling (FDM) is well-known as an inexpensive and the most commonly used additive manufacturing process. In FDM, build orientation is one of the critical factors that affect the quality of the printed part. However, the activity of determining a build orientation [...] Read more.
Fused deposition modelling (FDM) is well-known as an inexpensive and the most commonly used additive manufacturing process. In FDM, build orientation is one of the critical factors that affect the quality of the printed part. However, the activity of determining a build orientation for an FDM part, i.e., part orientation for FDM, usually relies on the knowledge and experience of domain experts. This necessitates an approach that enables the capture, representation, reasoning, and reuse of the data and knowledge in this activity. In this paper, a description logic (DL) ontology-supported part orientation approach for FDM is presented. Firstly, a set of top-level entities are created to construct a DL ontology for FDM part orientation. Then a DL ontology-supported alternative orientation generation procedure, a DL ontology-supported factor value prediction procedure, and a DL ontology-supported optimal orientation selection procedure are developed successively. After that, the application of the presented approach is illustrated via part orientation on six FDM parts. Finally, the effectiveness and efficiency of the presented approach are demonstrated through theoretical predictions and printing experiments and the advantages of the approach are demonstrated via an example. The demonstration results suggest that the presented approach has satisfying effectiveness and efficiency and provides a semantic enrichment model for capturing and representing FDM part orientation data and knowledge to enable automatic checking, reasoning, query, and further reuse. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Automatic determination of part build orientation for fused 2deposition modeling based on multi-criteriondecision-making3
Authors: Meifa Huang; NanZheng; Yuchu Qin; Zhemin Tang; HanZhang; BingFan; LingQin
Affiliation: 1 Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology, School of5Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China 2School of Electrical and Mechanical Engineering, Guilin Institute of Information Technology, Guilin 541004, China

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