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Research Progress of Machine Learning and Sensor Technology in Additive Manufacturing

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4630

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Special Issue Editors

Dr. Jian Qin

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Guest Editor

Welding Engineering and Laser Processing Centre, School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK
Interests: additive manufacturing (AM); directed energy deposition AM; process monitoring and control; Industrial Internet of Things; intelligent manufacturing

Dr. Samuel Bigot

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Guest Editor

School of Engineering, Cardiff University, Cardiff Wales CF10 3AT, UK
Interests: machine learning/AI in emerging and advanced manufacturing technologies
Special Issues, Collections and Topics in MDPI journals

Dr. Fangda Xu

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Guest Editor

Additive Manufacturing and Digital System Lab, Huzhou Institute, Zhejiang University, Huzhou, China
Interests: additive manufacturing; digital twin; process control

Dr. Shakirudeen Lasisi

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Guest Editor

WAAM3D, 5 Thornton Chase, Milton Keynes MK14 6FD, UK
Interests: numerical modelling; additive manufacturing; process monitoring and control; digital manufacturing

Special Issue Information

Dear Colleagues,

Additive manufacturing (AM) has received increased interest from industry due to its potential benefits of reducing costs and lead time, improving manufacturing sustainability, and reducing CO₂ emissions. However, a number of challenges stem from not only the complexity of manufacturing systems, but also from the demand for increasingly complex and high-quality products. These challenges create barriers to the widespread adoption of AM in industry and in-depth research on AM in academia. To tackle these challenges, machine learning (ML) technologies play a critical role. These technologies generally require a completed sensor system and novel sensing technology. ML and sensor technologies are continuously enhancing their power in a large range of applications, which have been widely used in many perspectives of AM, such as in the Design for AM (DfAM), material analytics, in situ monitoring and defect detection, property prediction, and sustainability.

To further apply ML techniques in AM, there is a need for researchers to realise the “Research and Application of Machine Learning and Sensor Technology for Additive Manufacturing”. The special issues will include theoretical numerical and experimental contributions that describe original research that addresses all aspects of ML research, sensor technology development and application for AM to the context as mentioned above. Potential topics include but are not limited to the following:

ML methods in DfAM;
Internet of Things (IoT) for AM;
ML and sensor technologies in quality control and process optimization for AM;
Real-time data analytics using smart sensors and ML for AM;
ML methods for AM sustainability.

Special applications demonstrating multi-functionality and that are related to similar are also welcome.

Dr. Jian Qin
Dr. Samuel Bigot
Dr. Fangda Xu
Dr. Shakirudeen Lasisi
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. Sensors is an international peer-reviewed open access semimonthly 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

additive manufacturing
3D printing
rapid prototyping
machine learning
smart sensor
Internet of Things
intelligent manufacturing

Published Papers (4 papers)

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Research

16 pages, 5411 KiB

Open AccessArticle

Monitoring of Single-Track Melting States Based on Photodiode Signal during Laser Powder Bed Fusion

by Longchao Cao, Wenxing Hu, Taotao Zhou, Lianqing Yu and Xufeng Huang

Sensors 2023, 23(24), 9793; https://doi.org/10.3390/s23249793 - 13 Dec 2023

Viewed by 778

Abstract

Single track is the basis for the melt pool modeling and physics work in laser powder bed fusion (LPBF). The melting state of a single track is closely related to defects such as porosity, lack of fusion, and balling, which have a significant impact on the mechanical properties of an LPBF-created part. To ensure the reliability of part quality and repeatability, process monitoring and feedback control are emerging to improve the melting states, which is becoming a hot topic in both the industrial and academic communities. In this research, a simple and low-cost off-axial photodiode signal monitoring system was established to monitor the melting pools of single tracks. Nine groups of single-track experiments with different process parameter combinations were carried out four times and then thirty-six LPBF tracks were obtained. The melting states were classified into three classes according to the morphologies of the tracks. A convolutional neural network (CNN) model was developed to extract the characteristics and identify the melting states. The raw one-dimensional photodiode signal data were converted into two-dimensional grayscale images. The average identification accuracy reached 95.81% and the computation time was 15 ms for each sample, which was promising for engineering applications. Compared with some classic deep learning models, the proposed CNN could distinguish the melting states with higher classification accuracy and efficiency. This work contributes to real-time multiple-sensor monitoring and feedback control. Full article

(This article belongs to the Special Issue Research Progress of Machine Learning and Sensor Technology in Additive Manufacturing)

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22 pages, 5257 KiB

Open AccessArticle

Prediction of Electron Beam Welding Penetration Depth Using Machine Learning-Enhanced Computational Fluid Dynamics Modelling

by Yi Yin, Yingtao Tian, Jialuo Ding, Tim Mitchell and Jian Qin

Sensors 2023, 23(21), 8687; https://doi.org/10.3390/s23218687 - 24 Oct 2023

Viewed by 945

Abstract

The necessity for precise prediction of penetration depth in the context of electron beam welding (EBW) cannot be overstated. Traditional statistical methodologies, including regression analysis and neural networks, often necessitate a considerable investment of both time and financial resources to produce results that meet acceptable standards. To address these challenges, this study introduces a novel approach for predicting EBW penetration depth that synergistically combines computational fluid dynamics (CFD) modelling with artificial neural networks (ANN). The CFD modelling technique was proven to be highly effective, yielding predictions with an average absolute percentage deviation of around 8%. This level of accuracy is consistent across a linear electron beam (EB) power range spanning from 86 J/mm to 324 J/mm. One of the most compelling advantages of this integrated approach is its efficiency. By leveraging the capabilities of CFD and ANN, the need for extensive and costly preliminary testing is effectively eliminated, thereby reducing both the time and financial outlay typically associated with such predictive modelling. Furthermore, the versatility of this approach is demonstrated by its adaptability to other types of EB machines, made possible through the application of the beam characterisation method outlined in the research. With the implementation of the models introduced in this study, practitioners can exert effective control over the quality of EBW welds. This is achieved by fine-tuning key variables, including but not limited to the beam power, beam radius, and the speed of travel during the welding process. Full article

(This article belongs to the Special Issue Research Progress of Machine Learning and Sensor Technology in Additive Manufacturing)

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20 pages, 22569 KiB

Open AccessArticle

Automated Interlayer Wall Height Compensation for Wire Based Directed Energy Deposition Additive Manufacturing

by Jian Qin, Javier Vives, Parthiban Raja, Shakirudeen Lasisi, Chong Wang, Thomas Charrett, Jialuo Ding, Stewart Williams, Jonathan Mark Hallam and Ralph Tatam

Sensors 2023, 23(20), 8498; https://doi.org/10.3390/s23208498 - 16 Oct 2023

Viewed by 941

Abstract

Part quality monitoring and control in wire-based directed energy deposition additive manufacturing (w-DEDAM) processes has been garnering continuous interest from both the academic and industrial sectors. However, maintaining a consistent layer height and ensuring that the wall height aligns closely with the design, as depicted in computer-aided design (CAD) models, pose significant challenges. These challenges arise due to the uncertainties associated with the manufacturing process and the working environment, particularly with extended processing times. To achieve these goals in an industrial scenario, the deposition geometry must be measured with precision and efficiency throughout the part-building process. Moreover, it is essential to comprehend the changes in the interlayer deposition height based on various process parameters. This paper first examines the behaviour of interlayer deposition height when process parameters change within different wall regions, with a particular focus on the transition areas. In addition, this paper explores the potential of geometry monitoring information in implementing interlayer wall height compensation during w-DEDAM part-building. The in-process layer height was monitored using a coherent range-resolved interferometry (RRI) sensor, and the accuracy and efficiency of this measurement were carefully studied. Leveraging this information and understanding of deposition geometry, the control points of the process parameters were identified. Subsequently, appropriate and varied process parameters were applied to each wall region to gradually compensate for wall height. The wall height discrepancies were generally compensated for in two to three layers. Full article

(This article belongs to the Special Issue Research Progress of Machine Learning and Sensor Technology in Additive Manufacturing)

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15 pages, 2721 KiB

Open AccessArticle

A Quality Assessment Network for Failure Detection in 3D Printing for Future Space-Based Manufacturing

by Jianning Tang and Xiaofeng Wu

Sensors 2023, 23(10), 4689; https://doi.org/10.3390/s23104689 - 12 May 2023

Cited by 1 | Viewed by 1240

Abstract

The application of space manufacturing technology holds tremendous potential for the advancement of space exploration. With significant investment from respected research institutions such as NASA, ESA, and CAST, along with private companies such as Made In Space, OHB System, Incus, and Lithoz, this sector has recently experienced a notable surge in development. Among the available manufacturing technologies, 3D printing has been successfully tested in the microgravity environment onboard the International Space Station (ISS), emerging as a versatile and promising solution for the future of space manufacturing. In this paper, an automated Quality Assessment (QA) approach for space-based 3D printing is proposed, aiming to enable the autonomous evaluation on the 3D printed results, thus freeing the system from reliance on human intervention, an essential requirement for the operation of space-based manufacturing platforms functioning in the exposed space environment. Specifically, this study investigates three types of common 3D printing failures, namely, indentation, protrusion, and layering to design an effective and efficient fault detection network that outperforms its counterparts backboned with other existing networks. The proposed approach has achieved a detection rate of up to 82.7% with an average confidence of 91.6% by training with the artificial samples, demonstrating promising results for the future implementation of 3D printing in space manufacturing. Full article

(This article belongs to the Special Issue Research Progress of Machine Learning and Sensor Technology in Additive Manufacturing)

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Research Progress of Machine Learning and Sensor Technology in Additive Manufacturing

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