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Processes
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Application of Artificial Intelligence Techniques in Additive Manufacturing
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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: closed (25 June 2023) | Viewed by 6138

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 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

  • 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 (4 papers)

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Research

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17 pages, 6946 KiB  
Article
A Method for Predicting Surface Finish of Polylactic Acid Parts Printed Using Fused Deposition Modeling
by Meifa Huang, Shangkun Jin, Zhemin Tang, Yuanqing Chen and Yuchu Qin
Processes 2023, 11(6), 1820; https://doi.org/10.3390/pr11061820 - 15 Jun 2023
Cited by 1 | Viewed by 1071
Abstract
Accurately predicting the surface finish of fused deposition modeling (FDM) parts is an important task for the engineering application of FDM technology. So far, many prediction models have been proposed by establishing a mapping relationship between printing parameters and surface roughness. Each model [...] Read more.
Accurately predicting the surface finish of fused deposition modeling (FDM) parts is an important task for the engineering application of FDM technology. So far, many prediction models have been proposed by establishing a mapping relationship between printing parameters and surface roughness. Each model can work well in its specific context; however, existing prediction models cannot meet the requirements of multi-factor and multi-category prediction of surface finish and cope with imbalanced data. Aiming at these issues, a prediction method based on a combination of the adaptive particle swarm optimization and K-nearest neighbor (APSO-KNN) algorithms is proposed in this paper. Seven input variables, including nozzle diameter, layer thickness, number of perimeters, flow rate, print speed, nozzle temperature, and build orientation, are considered. The printing values of each specimen are determined using an L27 Taguchi experimental design. A total of 27 specimens are printed and experimental data for the 27 specimens are used for model training and validation. The results indicate that the proposed method can achieve a minimum classification error of 0.01 after two iterations, with a maximum accuracy of 99.0%, and high model training efficiency. It can meet the requirements of predicting surface finish for FDM parts with multiple factors and categories and can handle imbalanced data. In addition, the high accuracy demonstrates the potential of this method for predicting surface finish, and its application in actual industrial manufacturing. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence Techniques in Additive Manufacturing)
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13 pages, 2416 KiB  
Article
Synthesis of 2-DOF Decoupled Rotation Stage with FEA-Based Neural Network
by Tingting Ye and Yangmin Li
Processes 2023, 11(1), 192; https://doi.org/10.3390/pr11010192 - 6 Jan 2023
Cited by 3 | Viewed by 1855
Abstract
Transfer printing technology has developed rapidly in the last decades, offering a potential demand for 2-DOF rotation stages. In order to remove decoupling modeling, improve motion accuracy, and simplify the control method, the 2-DOF decoupled rotation stages based on compliant mechanisms present notable [...] Read more.
Transfer printing technology has developed rapidly in the last decades, offering a potential demand for 2-DOF rotation stages. In order to remove decoupling modeling, improve motion accuracy, and simplify the control method, the 2-DOF decoupled rotation stages based on compliant mechanisms present notable merits. Therefore, a novel 2-DOF decoupled rotation stage is synthesized of which the critical components of decoupling are the topological arrangement and a novel decoupled compound joint. To fully consider the undesired deformation of rigid segments, an FEA-based neural network model is utilized to predict the rotation strokes and corresponding coupling ratios, and optimize the structural parameters. Then, FEA simulations are conducted to investigate the static and dynamic performances of the proposed 2-DOF decoupled rotation stage. The results show larger rotation strokes of 4.302 mrad in one-axis actuation with a 1.697% coupling ratio, and 4.184 and 4.151 mrad in two-axis actuation with undesired Rz rotation of 0.014 mrad with fewer actuators than other works. In addition, the first natural frequency of 2151 Hz is also higher, enabling a wider working frequency range. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence Techniques in Additive Manufacturing)
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20 pages, 17702 KiB  
Article
Description Logic Ontology-Supported Part Orientation for Fused Deposition Modelling
by Meifa Huang, Nan Zheng, Yuchu Qin, Zhemin Tang, Han Zhang, Bing Fan and Ling Qin
Processes 2022, 10(7), 1290; https://doi.org/10.3390/pr10071290 - 30 Jun 2022
Cited by 2 | Viewed by 2066
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
(This article belongs to the Special Issue Application of Artificial Intelligence Techniques in Additive Manufacturing)
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Review

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27 pages, 902 KiB  
Review
Multi-Attribute Decision-Making Methods in Additive Manufacturing: The State of the Art
by Yuchu Qin, Qunfen Qi, Peizhi Shi, Shan Lou, Paul J. Scott and Xiangqian Jiang
Processes 2023, 11(2), 497; https://doi.org/10.3390/pr11020497 - 7 Feb 2023
Cited by 10 | Viewed by 5237
Abstract
Multi-attribute decision-making (MADM) refers to making preference decisions via assessing a finite number of pre-specified alternatives under multiple and usually conflicting attributes. Many problems in the field of additive manufacturing (AM) are essentially MADM problems or can be converted into MADM problems. Recently, [...] Read more.
Multi-attribute decision-making (MADM) refers to making preference decisions via assessing a finite number of pre-specified alternatives under multiple and usually conflicting attributes. Many problems in the field of additive manufacturing (AM) are essentially MADM problems or can be converted into MADM problems. Recently, a variety of MADM methods have been applied to solve MADM problems in AM. This generates a series of interesting questions: What is the general trend of this research topic from the perspective of published articles every year? Which journals published the most articles on the research topic? Which articles on the research topic are the most cited? What MADM methods have been applied to the field of AM? What are the main strengths and weaknesses of each MADM method used? Which MADM method is the most used one in this field? What specific problems in AM have been tackled via using MADM methods? What are the main issues in existing MADM methods for AM that need to be addressed in future studies? To approach these questions, a review of MADM methods in AM is presented in this paper. Firstly, an overview of existing MADM methods in AM was carried out based on the perspective of specific MADM methods. A statistical analysis of these methods is then made from the aspects of published journal articles, applied specific methods, and solved AM problems. After that, the main issues in the application of MADM methods to AM are discussed. Finally, the research findings of this review are summarised. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence Techniques in Additive Manufacturing)
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