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Erschienen in:
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2023 | OriginalPaper | Buchkapitel

2. Feature Engineering in Additive Manufacturing

verfasst von : Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao

Erschienen in: Engineering of Additive Manufacturing Features for Data-Driven Solutions

Verlag: Springer Nature Switzerland

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Abstract

This chapter provides an overview of feature engineering landscape in additive manufacturing (AM). Domains and paradigms linked to data-driven AM are introduced and discussed. The sources of AM features are introduced in terms of their nature, properties, information variation, and digital representation. A comprehensive introduction to feature engineering techniques for AM is made, which are divided into five broad categories, namely subset selection, generation through transformation, generation through learning, knowledge-driven feature engineering, and integrated feature engineering. As a prerequisite to feature engineering, generic and AM-specific preprocessing techniques are discussed. At the end, different feature operations and libraries relevant to AM are introduced. These techniques are referenced in the next chapter on feature engineering applications.

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Vorheriges Kapitel Introduction
Nächstes Kapitel Applications in Data-Driven Additive Manufacturing
Fußnoten
1
Vector-based graphic representations may differ from pixel-based representations during featurization.
 
2
Some texts make distinction between different types of transfer learning (such as domain adaptation).
 
Literatur
1.
R. Bonnard, J.-Y. Hascoët, P. Mognol, Data model for additive manufacturing digital thread: state of the art and perspectives. Int. J. Comput. Integr. Manuf. 32(12), 1170–1191 (2019)CrossRef
2.
D.B. Kim et al., Streamlining the additive manufacturing digital spectrum: a systems approach. Addit. Manuf. 5, 20–30 (2015)
3.
Y. Lu et al., Self-improving additive manufacturing knowledge management, in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (American Society of Mechanical Engineers, 2018)
4.
S.C. Feng et al., Fundamental requirements for data representations in laser-based powder bed fusion, in International Manufacturing Science and Engineering Conference (American Society of Mechanical Engineers, 2015)
5.
G. Dong, H. Liu, Feature Engineering for Machine Learning and Data Analytics (CRC Press, 2018)
6.
A. Zheng, A. Casari, Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists (O’Reilly Media, Inc., 2018)
7.
H. Liu, H. Motoda, Feature Extraction, Construction and Selection: A Data Mining Perspective, vol. 453 (Springer Science and Business Media, 1998)
8.
Y. Zhang, M. Safdar, J. Xie, J. Li, M. Sage, Y.F. Zhao, A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management. J. Intell. Manuf. (2022) (forthcoming)
9.
Y. Qin et al., Status, comparison, and future of the representations of additive manufacturing data. Comput. Aided Des. 111, 44–64 (2019)CrossRef
10.
X. Lin et al., Metal-based additive manufacturing condition monitoring methods: from measurement to control. ISA Trans. 120, 147–166 (2022)CrossRef
11.
M. Khanzadeh et al., Porosity prediction: Supervised-learning of thermal history for direct laser deposition. J. Manuf. Syst. 47, 69–82 (2018)CrossRef
12.
Y. Guo, W.F. Lu, J.Y.H. Fuh, Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process. J. Intell. Manuf. 32(2), 347–359 (2021)CrossRef
13.
N. Johnson et al., Invited review: Machine learning for materials developments in metals additive manufacturing. Addit. Manuf. 36, 101641 (2020)
14.
B.-M. Roh et al., In-situ observation selection for quality management in metal additive manufacturing, in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (American Society of Mechanical Engineers, 2021)
15.
J. Han, J. Pei, H. Tong, Data Mining: Concepts and Techniques (Morgan Kaufmann, 2022)
16.
Y. Zhang et al., Predictive manufacturability assessment system for laser powder bed fusion based on a hybrid machine learning model. Addit. Manuf. 41, 101946 (2021)
17.
E.M. Sanfilippo, F. Belkadi, A. Bernard, Ontology-based knowledge representation for additive manufacturing. Comput. Ind. 109, 182–194 (2019)CrossRef
18.
E. Lanigan, A NASA Perspective on the Growing Role of In-Situ Process Monitoring in Managing Risk of AM Hardware (2019)
19.
W. He et al., In-situ monitoring and deformation characterization by optical techniques; part I: laser-aided direct metal deposition for additive manufacturing. Opt. Lasers Eng. 122, 74–88 (2019)CrossRef
20.
B. Wu et al., In situ monitoring methods for selective laser melting additive manufacturing process based on images—a review. China Foundry 18(4), 265–285 (2021)CrossRef
21.
K. Zhu, J.Y.H. Fuh, X. Lin, Metal-based additive manufacturing condition monitoring: a review on machine learning based approaches. IEEE/ASME Trans. Mechatron. 27(5), 2495–2510 (2021)
22.
P. Sreeraj, S.K. Mishra, P.K. Singh, A review on non-destructive evaluation and characterization of additively manufactured components. Prog. Addit. Manuf., 1–24 (2021)
23.
K. Lakshminarayan et al., Imputation of missing data using machine learning techniques, in KDD (1996)
24.
S.L. Harris, D. Harris, Digital Design and Computer Architecture (Morgan Kaufmann, 2015)
25.
J.M. Bland, D.G. Altman, Transformations, means, and confidence intervals. BMJ Br. Med. J. 312(7038), 1079 (1996)
26.
S.A. Alasadi, W.S. Bhaya, Review of data preprocessing techniques in data mining. J. Eng. Appl. Sci. 12(16), 4102–4107 (2017)
27.
(ISO), I.O.F.S., Information Technology—Sensor Networks—Services and Interfaces Supporting Collaborative Information Processing in Intelligent Sensor Networks (2005)
28.
Z. Snow et al., Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning. J. Manuf. Syst. 59, 12–26 (2021)CrossRef
29.
R. Chen et al., From design complexity to build quality in additive manufacturing—a sensor-based perspective. IEEE Sens. Lett. 3(1), 1–4 (2018)MathSciNetCrossRef
30.
R. Chen, M. Imani, F. Imani, Joint active search and neuromorphic computing for efficient data exploitation and monitoring in additive manufacturing. J. Manuf. Process. 71, 743–752 (2021)CrossRef
31.
J. Francis, L. Bian, Deep learning for distortion prediction in laser-based additive manufacturing using big data. Manuf. Lett. 20, 10–14 (2019)CrossRef
32.
S.C. Feng, Y. Lu, A.T. Jones, Meta-data for in-situ monitoring of laser powder bed fusion processes, in International Manufacturing Science and Engineering Conference (American Society of Mechanical Engineers, 2020)
33.
Y. Lu et al., Camera-based coaxial melt pool monitoring data registration for laser powder bed fusion additive manufacturing, in ASME International Mechanical Engineering Congress and Exposition (American Society of Mechanical Engineers, 2020)
34.
F. Imani, M. Khanzadeh, Image-guided multi-response modeling and characterization of design defects in metal additive manufacturing, in ASME International Mechanical Engineering Congress and Exposition (American Society of Mechanical Engineers, 2021)
35.
S.C. Feng et al., Additive manufacturing in situ and ex situ geometric data registration. J. Comput. Inf. Sci. Eng. 22(6), 061003 (2022)CrossRef
36.
J.A. Mitchell et al., Linking pyrometry to porosity in additively manufactured metals. Addit. Manuf. 31, 100946 (2020)
37.
Y. Zhu et al., Unraveling pore evolution in post-processing of binder jetting materials: X-ray computed tomography, computer vision, and machine learning. Addit. Manuf. 34, 101183 (2020)
38.
I.A. Okaro et al., Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Addit. Manuf. 27, 42–53 (2019)
39.
M. Salloum et al., Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets. Comput. Mech. 63(3), 491–510 (2019)MathSciNetMATHCrossRef
40.
L. Scime, B. Fisher, J. Beuth, Using coordinate transforms to improve the utility of a fixed field of view high speed camera for additive manufacturing applications. Manuf. Lett. 15, 104–106 (2018)CrossRef
41.
S.C. Feng, Y. Lu, A.T. Jones, Measured data alignments for monitoring metal additive manufacturing processes using laser powder bed fusion methods, in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (American Society of Mechanical Engineers, 2020)
42.
Z. Yang et al. In-process data fusion for process monitoring and control of metal additive manufacturing, in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (American Society of Mechanical Engineers, 2021)
43.
D. Mies, W. Marsden, S. Warde, Overview of additive manufacturing informatics: “a digital thread”. Integr. Mater. Manuf. Innov. 5(1), 114–142 (2016)CrossRef
44.
C. Liu et al., Digital twin-enabled collaborative data management for metal additive manufacturing systems. J. Manuf. Syst. (2020)
45.
S. Solorio-Fernández, J.A. Carrasco-Ochoa, J.F. Martínez-Trinidad, A review of unsupervised feature selection methods. Artif. Intell. Rev. 53(2), 907–948 (2020)CrossRef
46.
S. Liu et al., Machine learning for knowledge transfer across multiple metals additive manufacturing printers. Addit. Manuf. 39, 101877 (2021)
47.
S. Lee et al., Data analytics approach for melt-pool geometries in metal additive manufacturing. Sci. Technol. Adv. Mater. 20(1), 972–978 (2019)CrossRef
48.
Z. Zhang et al., Data-driven predictive modeling of tensile behavior of parts fabricated by cooperative 3d printing. J. Comput. Inf. Sci. Eng. 20(2), 021002 (2020)CrossRef
49.
J. Li et al., Multi-objective process parameters optimization of SLM using the ensemble of metamodels. J. Manuf. Process. 68, 198–209 (2021)CrossRef
50.
T.-W. Chang et al., Predicting magnetic characteristics of additive manufactured soft magnetic composites by machine learning. Int. J. Adv. Manuf. Technol. 114(9), 3177–3184 (2021)CrossRef
51.
Z. Li et al., Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robot. Comput. Integr. Manuf. 57, 488–495 (2019)CrossRef
52.
Q. Wu et al., Residual stresses in wire-arc additive manufacturing–Hierarchy of influential variables. Addit. Manuf. 35, 101355 (2020)
53.
S.L. Chan, Y. Lu, Y. Wang, Data-driven cost estimation for additive manufacturing in cybermanufacturing. J. Manuf. Syst. 46, 115–126 (2018)CrossRef
54.
M. Nixon, A. Aguado, Feature Extraction and Image Processing for Computer Vision (Academic Press, 2019)
55.
I.T. Jolliffe, J. Cadima, Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 374(2065), 20150202 (2016)MathSciNetMATHCrossRef
56.
Y. Gao et al., A review on recent advances in vision-based defect recognition towards industrial intelligence. J. Manuf. Syst. 62, 753–766 (2021)
57.
B.L. DeCost et al., Computer vision and machine learning for autonomous characterization of am powder feedstocks. JOM 69(3), 456–465 (2017)CrossRef
58.
L. Scime, J. Beuth, Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Addit. Manuf. 25, 151–165 (2019)
59.
L. Scime, J. Beuth, Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Addit. Manuf. 19, 114–126 (2018)
60.
D.J. Roach et al., Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement structures. Addit. Manuf. 41, 101950 (2021)
61.
T. Özel et al., Surface topography investigations on nickel alloy 625 fabricated via laser powder bed fusion. Int. J. Adv. Manuf. Technol. 94(9), 4451–4458 (2018)CrossRef
62.
Z. Zhang et al., Predicting flexural strength of additively manufactured continuous carbon fiber-reinforced polymer composites using machine learning. J. Comput. Inf. Sci. Eng. 20(6) (2020)
63.
X. Lin et al., Motion feature based melt pool monitoring for selective laser melting process. J. Mater. Process. Technol. 303, 117523 (2022)CrossRef
64.
B.D. Fulcher, M.A. Little, N.S. Jones, Highly comparative time-series analysis: the empirical structure of time series and their methods. J. R. Soc. Interface 10(83), 20130048 (2013)CrossRef
65.
G. Fahim, K. Amin, S. Zarif, Single-view 3D reconstruction: a survey of deep learning methods. Comput. Graph. 94, 164–190 (2021)CrossRef
66.
M. Montazeri et al., In-process monitoring of porosity in additive manufacturing using optical emission spectroscopy. IISE Trans. 52(5), 500–515 (2020)CrossRef
67.
T. Mativo, C. Fritz, I. Fidan, Cyber acoustic analysis of additively manufactured objects. Int. J. Adv. Manuf. Technol. 96(1), 581–586 (2018)CrossRef
68.
K. Wasmer et al., In situ quality monitoring in AM using acoustic emission: a reinforcement learning approach. J. Mater. Eng. Perform. 28(2), 666–672 (2019)CrossRef
69.
R.M. Yazdi, F. Imani, H. Yang, A hybrid deep learning model of process-build interactions in additive manufacturing. J. Manuf. Syst. 57, 460–468 (2020)
70.
B. Yuan et al., Semi-supervised convolutional neural networks for in-situ video monitoring of selective laser melting, in 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE, 2019)
71.
X. Guan et al., 3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework. BMC Med. Imag. 22(1), 1–18 (2022)CrossRef
72.
73.
Y. Zhang, Y.F. Zhao, A web-based automated manufacturability analyzer and recommender for additive manufacturing (MAR-AM) via a hybrid machine learning model. Expert Syst. Appl. 199, 117189 (2022)CrossRef
74.
J. Li, B.M. Chen, G.H. Lee, So-net: self-organizing network for point cloud analysis, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
75.
J. Masci et al., Geodesic convolutional neural networks on Riemannian manifolds, in Proceedings of the IEEE International Conference on Computer Vision Workshops (2015)
76.
Z. Wu et al., 3D shapenets: a deep representation for volumetric shapes, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
77.
I. Goodfellow, Y. Bengio, A. Courville, Deep Learning (MIT Press, 2016)
78.
Y. Bengio, A. Courville, P. Vincent, Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)CrossRef
79.
R. Salakhutdinov, G. Hinton, Learning a nonlinear embedding by preserving class neighbourhood structure, in Artificial Intelligence and Statistics (PMLR, 2007)
80.
Y. Zhang et al., In situ monitoring plasma arc additive manufacturing process with a fully convolutional network. Int. J. Adv. Manuf. Technol. 120(3), 2247–2257 (2022)MathSciNetCrossRef
81.
Y. Zhang et al., Powder-bed fusion process monitoring by machine vision with hybrid convolutional neural networks. IEEE Trans. Industr. Inf. 16(9), 5769–5779 (2019)CrossRef
82.
L. Scime, J. Beuth, A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process. Addit. Manuf. 24, 273–286 (2018)
83.
J. An, S. Cho, Variational autoencoder based anomaly detection using reconstruction probability. Spec. Lect. IE 2(1), 1–18 (2015)
84.
S. Fathizadan, F. Ju, Y. Lu, Deep representation learning for process variation management in laser powder bed fusion. Addit. Manuf. 42, 101961 (2021)
85.
C. Murphy et al., Using autoencoded voxel patterns to predict part mass, required support material, and build time, in 2018 International Solid Freeform Fabrication Symposium (University of Texas at Austin, 2018)
86.
X. Li et al., A transfer learning approach for microstructure reconstruction and structure-property predictions. Sci. Rep. 8(1), 1–13 (2018)
87.
T.J. Hagedorn, S. Krishnamurty, I.R. Grosse, A knowledge-based method for innovative design for additive manufacturing supported by modular ontologies. J. Comput. Inf. Sci. Eng. 18(2) (2018)
88.
B.-M. Roh et al., Ontology-based process map for metal additive manufacturing. J. Mater. Eng. Perform. 30(12), 8784–8797 (2021)CrossRef
89.
Y. Xiong et al., A knowledge-based process planning framework for wire arc additive manufacturing. Adv. Eng. Inform. 45, 101135 (2020)CrossRef
90.
D.B. Kim, An approach for composing predictive models from disparate knowledge sources in smart manufacturing environments. J. Intell. Manuf. 30(4), 1999–2012 (2019)CrossRef
91.
H. Zhang et al., A knowledge transfer framework to support rapid process modeling in aerosol jet printing. Adv. Eng. Inform. 48, 101264 (2021)CrossRef
92.
H. Park et al., Collaborative knowledge management to identify data analytics opportunities in additive manufacturing. J. Intell. Manuf., 1–24 (2021)
93.
M. Mozaffar et al., Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: current state and perspectives. J. Mater. Process. Technol. 302, 117485 (2021)
94.
G.I. Barenblatt, Scaling, vol. 34 (Cambridge University Press, 2003)
95.
S.C. Feng et al., Toward knowledge management for smart manufacturing. J. Comput. Inf. Sci. Eng. 17(3) (2017)
96.
M. Dinar, D.W. Rosen, A design for additive manufacturing ontology. J. Comput. Inf. Sci. Eng. 17(2) (2017)
97.
A.E. Gongora et al., A Bayesian experimental autonomous researcher for mechanical design. Sci. Adv. 6(15), eaaz1708 (2020)
98.
R. Li et al., Geometrical defect detection on additive manufacturing parts with curvature feature and machine learning. Int. J. Adv. Manuf. Technol. 120(5), 3719–3729 (2022)MathSciNetCrossRef
99.
H. Ko et al., Machine learning and knowledge graph based design rule construction for additive manufacturing. Addit. Manuf. 37, 101620 (2021)
100.
Y. Mallet, O. Vel, D. Coomans, Integrated feature extraction using adaptive wavelets, in Feature Extraction, Construction and Selection (Springer, 1998), pp. 175–189
101.
Y. Yang, M. He, L. Li, Power consumption estimation for mask image projection stereolithography additive manufacturing using machine learning based approach. J. Clean. Prod. 251, 119710 (2020)CrossRef
102.
W. Xing et al., Using convolutional neural networks to classify melt pools in a pulsed selective laser melting process. J. Manuf. Process. 74, 486–499 (2022)CrossRef
103.
R.R. Selvaraju et al., Grad-CAM: visual explanations from deep networks via gradient-based localization, in Proceedings of the IEEE International Conference on Computer Vision (2017)
104.
H. Lee et al., Deep learning for in-situ powder stream fault detection in directed energy deposition process. J. Manuf. Syst. 62, 575–587 (2022)CrossRef
Metadaten
Titel
Feature Engineering in Additive Manufacturing
verfasst von
Mutahar Safdar
Guy Lamouche
Padma Polash Paul
Gentry Wood
Yaoyao Fiona Zhao
Copyright-Jahr
2023
Buch
Engineering of Additive Manufacturing Features for Data-Driven Solutions

Print ISBN: 978-3-031-32153-5

Electronic ISBN: 978-3-031-32154-2

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-3-031-32154-2_2

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