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Applications of Modeling and Machine Learning in Additive Manufacturing
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Applications of Modeling and Machine Learning in Additive Manufacturing

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 10 June 2024 | Viewed by 8158

Special Issue Editors

Dr. Tuhin Mukherjee

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Iowa State University, Ames, IA, USA
Interests: additive manufacturing; heat transfer and fluid flow; mechanistic modeling; machine learning; compositionally graded alloys; residual stresses and distortion; defect formation
Special Issues, Collections and Topics in MDPI journals
Dr. Qianru Wu

E-Mail Website
Guest Editor
Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing, China
Interests: additive manufacturing; mechanistic modeling; machine learning; residual stresses and distortion; directed energy deposition - arc; finite element analysis; neural network

Special Issue Information

Dear Colleagues,

We are inviting you to submit a manuscript regarding the applications of modeling and machine learning in additive manufacturing for a Special Issue of Materials. Topics of interest include—but are not limited to—applications of modeling and machine learning for the novel design of additively manufactured products; additive manufacturing processes; alloy design; tailoring microstructure; customized mechanical and chemical properties; improved creep resistance, fatigue life, and serviceability; reducing defects and residual stresses and distortion. The scope of this Special Issue also includes all 3D printing processes for alloys, ceramics, and polymers. The paper types that will be considered include technical papers, short communications, perspectives, and reviews. The lengths of the reviews will depend on the topic but will be decided through prior agreement with the editors.

The contents must be original, unpublished work that has not been submitted for publication elsewhere.

Please review the guide for authors at https://www.mdpi.com/journal/materials/instructions.

Articles should be submitted to the MDPI submission system, which will be available from 20 February 2023 and will remain open until 31 December 2023. Please select the Special Issue name, “Applications of Modeling and Machine Learning in Additive Manufacturing”, as the article type during submission.

Papers will appear online as they are accepted. It is anticipated that the completed Special Issue will be available in spring of 2024.

We look forward to working with you on the publication of this Special Issue. Please feel free to contact us if you have any questions.

Dr. Tuhin Mukherjee
Dr. Qianru Wu
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. Materials 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
  • selective laser melting
  • wire-arc additive manufacturing
  • mechanistic modeling
  • statistical modeling
  • analytical modeling
  • dimensional analysis
  • machine learning
  • deep learning
  • tailoring microstructure
  • customized properties
  • defect formation
  • residual stresses and distortion

Published Papers (7 papers)

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Research

18 pages, 3765 KiB  
Article
Mitigation of Gas Porosity in Additive Manufacturing Using Experimental Data Analysis and Mechanistic Modeling
by Satyaki Sinha and Tuhin Mukherjee
Materials 2024, 17(7), 1569; https://doi.org/10.3390/ma17071569 - 29 Mar 2024
Viewed by 1245
Abstract
Shielding gas, metal vapors, and gases trapped inside powders during atomization can result in gas porosity, which is known to degrade the fatigue strength and tensile properties of components made by laser powder bed fusion additive manufacturing. Post-processing and trial-and-error adjustment of processing [...] Read more.
Shielding gas, metal vapors, and gases trapped inside powders during atomization can result in gas porosity, which is known to degrade the fatigue strength and tensile properties of components made by laser powder bed fusion additive manufacturing. Post-processing and trial-and-error adjustment of processing conditions to reduce porosity are time-consuming and expensive. Here, we combined mechanistic modeling and experimental data analysis and proposed an easy-to-use, verifiable, dimensionless gas porosity index to mitigate pore formation. The results from the mechanistic model were rigorously tested against independent experimental data. It was found that the index can accurately predict the occurrence of porosity for commonly used alloys, including stainless steel 316, Ti-6Al-4V, Inconel 718, and AlSi10Mg, with an accuracy of 92%. In addition, experimental data showed that the amount of pores increased at a higher value of the index. Among the four alloys, AlSi10Mg was found to be the most susceptible to gas porosity, for which the value of the gas porosity index can be 5 to 10 times higher than those for the other alloys. Based on the results, a gas porosity map was constructed that can be used in practice for selecting appropriate sets of process variables to mitigate gas porosity without the need for empirical testing. Full article
(This article belongs to the Special Issue Applications of Modeling and Machine Learning in Additive Manufacturing)
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30 pages, 2363 KiB  
Article
Transferability of Temperature Evolution of Dissimilar Wire-Arc Additively Manufactured Components by Machine Learning
by Håvard Mo Fagersand, David Morin, Kjell Magne Mathisen, Jianying He and Zhiliang Zhang
Materials 2024, 17(3), 742; https://doi.org/10.3390/ma17030742 - 03 Feb 2024
Viewed by 737
Abstract
Wire-arc additive manufacturing (WAAM) is a promising industrial production technique. Without optimization, inherent temperature gradients can cause powerful residual stresses and microstructural defects. There is therefore a need for data-driven methods allowing real-time process optimization for WAAM. This study focuses on machine learning [...] Read more.
Wire-arc additive manufacturing (WAAM) is a promising industrial production technique. Without optimization, inherent temperature gradients can cause powerful residual stresses and microstructural defects. There is therefore a need for data-driven methods allowing real-time process optimization for WAAM. This study focuses on machine learning (ML)-based prediction of temperature history for WAAM-produced aluminum bars with different geometries and process parameters, including bar length, number of deposition layers, and heat source movement speed. Finite element (FE) simulations are used to provide training and prediction data. The ML models are based on a simple multilayer perceptron (MLP) and performed well during baseline training and testing, giving a testing mean absolute percentage error (MAPE) of less than 0.7% with an 80/20 train–test split, with low variation in model performance. When using the trained models to predict results from FE simulations with greater length or number of layers, the MAPE increased to an average of 3.22% or less, with greater variability. In the cases of greatest difference, some models still returned a MAPE of less than 1%. For different scanning speeds, the performance was worse, with some outlier models giving a MAPE of up to 14.91%. This study demonstrates the transferability of temperature history for WAAM with a simple MLP approach. Full article
(This article belongs to the Special Issue Applications of Modeling and Machine Learning in Additive Manufacturing)
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10 pages, 3261 KiB  
Article
Deep-Learning-Based Segmentation of Keyhole in In-Situ X-ray Imaging of Laser Powder Bed Fusion
by William Dong, Jason Lian, Chengpo Yan, Yiran Zhong, Sumanth Karnati, Qilin Guo, Lianyi Chen and Dane Morgan
Materials 2024, 17(2), 510; https://doi.org/10.3390/ma17020510 - 21 Jan 2024
Cited by 1 | Viewed by 754
Abstract
In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality. The in-situ X-ray imaging technique can monitor the keyhole dynamics from [...] Read more.
In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality. The in-situ X-ray imaging technique can monitor the keyhole dynamics from the side and capture keyhole shapes in the X-ray image stream. Keyhole shapes in X-ray images are then often labeled by humans for analysis, which increasingly involves attempting to correlate keyhole shapes with defects using machine learning. However, such labeling is tedious, time-consuming, error-prone, and cannot be scaled to large data sets. To use keyhole shapes more readily as the input to machine learning methods, an automatic tool to identify keyhole regions is desirable. In this paper, a deep-learning-based computer vision tool that can automatically segment keyhole shapes out of X-ray images is presented. The pipeline contains a filtering method and an implementation of the BASNet deep learning model to semantically segment the keyhole morphologies out of X-ray images. The presented tool shows promising average accuracy of 91.24% for keyhole area, and 92.81% for boundary shape, for a range of test dataset conditions in Al6061 (and one AliSi10Mg) alloys, with 300 training images/labels and 100 testing images for each trial. Prospective users may apply the presently trained tool or a retrained version following the approach used here to automatically label keyhole shapes in large image sets. Full article
(This article belongs to the Special Issue Applications of Modeling and Machine Learning in Additive Manufacturing)
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13 pages, 2067 KiB  
Article
Data-Driven Prediction and Uncertainty Quantification of Process Parameters for Directed Energy Deposition
by Florian Hermann, Andreas Michalowski, Tim Brünnette, Peter Reimann, Sabrina Vogt and Thomas Graf
Materials 2023, 16(23), 7308; https://doi.org/10.3390/ma16237308 - 24 Nov 2023
Viewed by 888
Abstract
Laser-based directed energy deposition using metal powder (DED-LB/M) offers great potential for a flexible production mainly defined by software. To exploit this potential, knowledge of the process parameters required to achieve a specific track geometry is essential. Existing analytical, numerical, and machine-learning approaches, [...] Read more.
Laser-based directed energy deposition using metal powder (DED-LB/M) offers great potential for a flexible production mainly defined by software. To exploit this potential, knowledge of the process parameters required to achieve a specific track geometry is essential. Existing analytical, numerical, and machine-learning approaches, however, are not yet able to predict the process parameters in a satisfactory way. A trial-&-error approach is therefore usually applied to find the best process parameters. This paper presents a novel user-centric decision-making workflow, in which several combinations of process parameters that are most likely to yield the desired track geometry are proposed to the user. For this purpose, a Gaussian Process Regression (GPR) model, which has the advantage of including uncertainty quantification (UQ), was trained with experimental data to predict the geometry of single DED tracks based on the process parameters. The inherent UQ of the GPR together with the expert knowledge of the user can subsequently be leveraged for the inverse question of finding the best sets of process parameters by minimizing the expected squared deviation between target and actual track geometry. The GPR was trained and validated with a total of 379 cross sections of single tracks and the benefit of the workflow is demonstrated by two exemplary use cases. Full article
(This article belongs to the Special Issue Applications of Modeling and Machine Learning in Additive Manufacturing)
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42 pages, 16407 KiB  
Article
A Data-Driven Framework for Direct Local Tensile Property Prediction of Laser Powder Bed Fusion Parts
by Luke Scime, Chase Joslin, David A. Collins, Michael Sprayberry, Alka Singh, William Halsey, Ryan Duncan, Zackary Snow, Ryan Dehoff and Vincent Paquit
Materials 2023, 16(23), 7293; https://doi.org/10.3390/ma16237293 - 23 Nov 2023
Viewed by 1696
Abstract
This article proposes a generalizable, data-driven framework for qualifying laser powder bed fusion additively manufactured parts using part-specific in situ data, including powder bed imaging, machine health sensors, and laser scan paths. To achieve part qualification without relying solely on statistical processes or [...] Read more.
This article proposes a generalizable, data-driven framework for qualifying laser powder bed fusion additively manufactured parts using part-specific in situ data, including powder bed imaging, machine health sensors, and laser scan paths. To achieve part qualification without relying solely on statistical processes or feedstock control, a sequence of machine learning models was trained on 6299 tensile specimens to locally predict the tensile properties of stainless-steel parts based on fused multi-modal in situ sensor data and a priori information. A cyberphysical infrastructure enabled the robust spatial tracking of individual specimens, and computer vision techniques registered the ground truth tensile measurements to the in situ data. The co-registered 230 GB dataset used in this work has been publicly released and is available as a set of HDF5 files. The extensive training data requirements and wide range of size scales were addressed by combining deep learning, machine learning, and feature engineering algorithms in a relay. The trained models demonstrated a 61% error reduction in ultimate tensile strength predictions relative to estimates made without any in situ information. Lessons learned and potential improvements to the sensors and mechanical testing procedure are discussed. Full article
(This article belongs to the Special Issue Applications of Modeling and Machine Learning in Additive Manufacturing)
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19 pages, 2621 KiB  
Article
A Random Forest Classifier for Anomaly Detection in Laser-Powder Bed Fusion Using Optical Monitoring
by Imran Ali Khan, Hannes Birkhofer, Dominik Kunz, Drzewietzki Lukas and Vasily Ploshikhin
Materials 2023, 16(19), 6470; https://doi.org/10.3390/ma16196470 - 29 Sep 2023
Cited by 4 | Viewed by 1117
Abstract
Metal additive manufacturing (AM) is a disruptive production technology, widely adopted in innovative industries that revolutionizes design and manufacturing. The interest in quality control of AM systems has grown substantially over the last decade, driven by AM’s appeal for intricate, high-value, and low-volume [...] Read more.
Metal additive manufacturing (AM) is a disruptive production technology, widely adopted in innovative industries that revolutionizes design and manufacturing. The interest in quality control of AM systems has grown substantially over the last decade, driven by AM’s appeal for intricate, high-value, and low-volume production components. Geometry-dependent process conditions in AM yield unique challenges, especially regarding quality assurance. This study contributes to the development of machine learning models to enhance in-process monitoring and control technology, which is a critical step in cost reduction in metal AM. As the part is built layer upon layer, the features of each layer have an influence on the quality of the final part. Layer-wise in-process sensing can be used to retrieve condition-related features and help detect defects caused by improper process conditions. In this work, layer-wise monitoring using optical tomography (OT) imaging was employed as a data source, and a machine-learning (ML) technique was utilized to detect anomalies that can lead to defects. The major defects analyzed in this experiment were gas pores and lack of fusion defects. The Random Forest Classifier ML algorithm is employed to segment anomalies from optical images, which are then validated by correlating them with defects from computerized tomography (CT) data. Further, 3D mapping of defects from CT data onto the OT dataset is carried out using the affine transformation technique. The developed anomaly detection model’s performance is evaluated using several metrics such as confusion matrix, dice coefficient, accuracy, precision, recall, and intersection-over-union (IOU). The k-fold cross-validation technique was utilized to ensure robustness and generalization of the model’s performance. The best detection accuracy of the developed anomaly detection model is 99.98%. Around 79.40% of defects from CT data correlated with the anomalies detected from the OT data. Full article
(This article belongs to the Special Issue Applications of Modeling and Machine Learning in Additive Manufacturing)
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12 pages, 2118 KiB  
Article
Machine Learning-Enabled Quantitative Analysis of Optically Obscure Scratches on Nickel-Plated Additively Manufactured (AM) Samples
by Betelhiem N. Mengesha, Andrew C. Grizzle, Wondwosen Demisse, Kate L. Klein, Amy Elliott and Pawan Tyagi
Materials 2023, 16(18), 6301; https://doi.org/10.3390/ma16186301 - 20 Sep 2023
Cited by 1 | Viewed by 907
Abstract
Additively manufactured metal components often have rough and uneven surfaces, necessitating post-processing and surface polishing. Hardness is a critical characteristic that affects overall component properties, including wear. This study employed K-means unsupervised machine learning to explore the relationship between the relative surface hardness [...] Read more.
Additively manufactured metal components often have rough and uneven surfaces, necessitating post-processing and surface polishing. Hardness is a critical characteristic that affects overall component properties, including wear. This study employed K-means unsupervised machine learning to explore the relationship between the relative surface hardness and scratch width of electroless nickel plating on additively manufactured composite components. The Taguchi design of experiment (TDOE) L9 orthogonal array facilitated experimentation with various factors and levels. Initially, a digital light microscope was used for 3D surface mapping and scratch width quantification. However, the microscope struggled with the reflections from the shiny Ni-plating and scatter from small scratches. To overcome this, a scanning electron microscope (SEM) generated grayscale images and 3D height maps of the scratched Ni-plating, thus enabling the precise characterization of scratch widths. Optical identification of the scratch regions and quantification were accomplished using Python code with a K-means machine-learning clustering algorithm. The TDOE yielded distinct Ni-plating hardness levels for the nine samples, while an increased scratch force showed a non-linear impact on scratch widths. The enhanced surface quality resulting from Ni coatings will have significant implications in various industrial applications, and it will play a pivotal role in future metal and alloy surface engineering. Full article
(This article belongs to the Special Issue Applications of Modeling and Machine Learning in Additive Manufacturing)
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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: Mitigation of Gas Porosity in Additive Manufacturing Using Experimental Data Analysis and Mechanistic Modeling
Authors: Satyaki Sinha; Tuhin Mukherjee
Affiliation: Iowa State University
Abstract: Shielding gas, metal vapors, and gases trapped inside powders during atomization can result in gas porosity that is known to degrade fatigue strength and tensile properties of components made by laser powder bed fusion additive manufacturing. Post-processing and trial-and-error adjustment of processing conditions to reduce porosity are time-consuming and expensive. Here we combined mechanistic modeling and experimental data analysis and proposed an easy-to-use, verifiable, dimensionless gas porosity index to mitigate pore formation. We found that the index can accurately predict the occurrence of porosity for commonly used alloys, stainless steel 316, Ti-6Al-4V, Inconel 718, and AlSi10Mg. In addition, experimental data showed that the amount of pores increased at a higher value of the index. Among the four alloys, AlSi10Mg was found to be the most susceptible to gas porosity. Based on the results, we constructed a gas porosity map that can be used in practice for selecting appropriate sets of process variables to mitigate gas porosity without the need for empirical testing.

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