Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Buchtitelbild

2023 | OriginalPaper | Buchkapitel

1. Introduction

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

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This chapter introduces additive manufacturing (AM) in terms of its merits and maturity. The status of data-driven AM research is evaluated in terms of existing reviews. The significance of feature engineering in AM is explained. Finally, the methodology used to collect literature is explained toward the end.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Nächstes Kapitel Feature Engineering in Additive Manufacturing
Fußnoten
1
MJ and SL are other AM techniques which could be used to manufacture metallic parts.
 
2
Big data is usually categorized based on its volume, velocity, variability, variety, and value.
 
3
The featurization levels have been explained in the subsequent text.
 
4
Synonymous of “variable” or “attribute”.
 
Literatur
1.
I. Gibson et al., Additive Manufacturing Technologies, vol. 17 (Springer, 2021)
2.
M. Mehrpouya et al., The potential of additive manufacturing in the smart factory industrial 4.0: a review. Appl. Sci. 9(18), 3865 (2019)
3.
W.E. Frazier, Metal additive manufacturing: a review. J. Mater. Eng. Perform. 23(6), 1917–1928 (2014)CrossRef
4.
N. Johnson et al., Invited review: machine learning for materials developments in metals additive manufacturing. Addit. Manuf. 36, 101641 (2020)
5.
ASTM, Additive Manufacturing—General Principles—Fundamentals and Vocabulary (American Society of Testing Materials, 2022)
6.
B. Blakey-Milner et al., Metal additive manufacturing in aerospace: a review. Mater. Des. 209, 110008 (2021)CrossRef
7.
8.
A. Paolini, S. Kollmannsberger, E. Rank, Additive manufacturing in construction: a review on processes, applications, and digital planning methods. Addit. Manuf. 30, 100894 (2019)
9.
C. Sun et al., Additive manufacturing for energy: a review. Appl. Energy 282, 116041 (2021)CrossRef
10.
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)
11.
A. Oleff et al., Process monitoring for material extrusion additive manufacturing: a state-of-the-art review. Progr. Addit. Manuf., 1–26 (2021)
12.
Z. Luo, Y. Zhao, A survey of finite element analysis of temperature and thermal stress fields in powder bed fusion additive manufacturing. Addit. Manuf. 21, 318–332 (2018)
13.
X. Guan, Y.F. Zhao, Modeling of the laser powder–based directed energy deposition process for additive manufacturing: a review. Int. J. Adv. Manuf. Technol. 107(5), 1959–1982 (2020)CrossRef
14.
M.M. Francois et al., Modeling of additive manufacturing processes for metals: challenges and opportunities. Curr. Opin. Solid State Mater. Sci. 21(4), 198–206 (2017)CrossRef
15.
J. Qin et al., Research and application of machine learning for additive manufacturing. Addit. Manuf. 52, 102691 (2022)
16.
C.M. Bishop, N.M. Nasrabadi, Pattern Recognition and Machine Learning, vol. 4 (Springer, 2006)
17.
Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015)
18.
W.Y. Wang et al., Integrated computational materials engineering for advanced materials: a brief review. Comput. Mater. Sci. 158, 42–48 (2019)CrossRef
19.
S. Sagiroglu, D. Sinanc, Big data: a review, in 2013 International Conference on Collaboration Technologies and Systems (CTS) (IEEE, 2013)
20.
D. Kochkov et al., Machine learning–accelerated computational fluid dynamics. Proc. Natl. Acad. Sci. 118(21), e2101784118 (2021)MathSciNetCrossRef
21.
A. Phua, C. Davies, G. Delaney, A digital twin hierarchy for metal additive manufacturing. Comput. Ind. 140, 103667 (2022)CrossRef
22.
Q. Zhu, Z. Liu, J. Yan, Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks. Comput. Mech. 67(2), 619–635 (2021)MathSciNetMATHCrossRef
23.
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)
24.
S.J. Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)CrossRef
25.
C. Muthiah et al., Application of machine learning in fused deposition modeling: a review, in AIP Conference Proceedings (AIP Publishing LLC, 2022)
26.
X. Lin et al., Metal-based additive manufacturing condition monitoring methods: from measurement to control. ISA Trans. 120, 147–166 (2022)CrossRef
27.
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
28.
M. Valizadeh, S.J. Wolff, Convolutional Neural Network applications in additive manufacturing: a review. Adv. Ind. Manuf. Eng. 4, 100072 (2022)
29.
C. Tian et al., Data-driven approaches toward smarter additive manufacturing. Adv. Intell. Syst. 3(12), 2100014 (2021)CrossRef
30.
L. Meng et al., Machine learning in additive manufacturing: a review. JOM 72(6), 2363–2377 (2020)CrossRef
31.
Z. Wang et al., Data-driven modeling of process, structure and property in additive manufacturing: a review and future directions. J. Manuf. Process. 77, 13–31 (2022)CrossRef
32.
X. Qi et al., Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives. Engineering 5(4), 721–729 (2019)CrossRef
33.
M. Joshi et al., Applications of supervised machine learning algorithms in additive manufacturing: a review, in 2019 International Solid Freeform Fabrication Symposium (University of Texas at Austin, 2019)
34.
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., 1–36 (2022)
35.
P. Sreeraj, S.K. Mishra, P.K. Singh, A review on non-destructive evaluation and characterization of additively manufactured components. Progr. Addit. Manuf., 1–24 (2021)
36.
Y. Fu et al., Machine learning algorithms for defect detection in metal laser-based additive manufacturing: a review. J. Manuf. Process. 75, 693–710 (2022)CrossRef
37.
S. Guo et al., Machine learning for metal additive manufacturing: towards a physics-informed data-driven paradigm. J. Manuf. Syst. 62, 145–163 (2022)CrossRef
38.
Y. Zhang, W. Yan, Applications of machine learning in metal powder-bed fusion in-process monitoring and control: status and challenges. J. Intell. Manuf., 1–24 (2022)
39.
M.D. Xames, F.K. Torsha, F. Sarwar, A systematic literature review on recent trends of machine learning applications in additive manufacturing. J. Intell. Manuf., 1–27 (2022)
40.
F.G. Cunha, T.G. Santos, J. Xavier, In situ monitoring of additive manufacturing using digital image correlation: a review. Materials 14(6), 1511 (2021)CrossRef
41.
D. Chen et al., Research on in situ monitoring of selective laser melting: a state of the art review. Int. J. Adv. Manuf. Technol. 113(11), 3121–3138 (2021)CrossRef
42.
X. Chen et al., A review on wire-arc additive manufacturing: typical defects, detection approaches, and multisensor data fusion-based model. Int. J. Adv. Manuf. Technol. 117(3), 707–727 (2021)CrossRef
43.
S. Usha, In situ monitoring of metal additive manufacturing process: a review. Addit. Manuf., 275–299 (2021)
44.
R. McCann et al., In-situ sensing, process monitoring and machine control in laser powder bed fusion: a review. Addit. Manuf. 45, 102058 (2021)
45.
J. Lee et al., Review on quality control methods in metal additive manufacturing. Appl. Sci. 11(4), 1966 (2021)CrossRef
46.
D. Mahmoud et al., Applications of machine learning in process monitoring and controls of L-PBF additive manufacturing: a review. Appl. Sci. 11(24), 11910 (2021)
47.
G.D. Goh, S.L. Sing, W.Y. Yeong, A review on machine learning in 3D printing: applications, potential, and challenges. Artif. Intell. Rev. 54(1), 63–94 (2021)CrossRef
48.
S. Nasiri, M.R. Khosravani, Machine learning in predicting mechanical behavior of additively manufactured parts. J. Mater. Res. Technol. 14, 1137–1153 (2021)
49.
K. Bartsch et al., On the digital twin application and the role of artificial intelligence in additive manufacturing: a systematic review. J. Phys. Mater. 4(3), 032005 (2021)CrossRef
50.
S. Sing et al., Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing. Virtual Phys. Prototyp. 16(3), 372–386 (2021)CrossRef
51.
A. Omairi, Z.H. Ismail, Towards machine learning for error compensation in additive manufacturing. Appl. Sci. 11(5), 2375 (2021)CrossRef
52.
Y. Wang et al., Smart additive manufacturing: current artificial intelligence-enabled methods and future perspectives. Sci. China Technol. Sci. 63(9), 1600–1611 (2020)CrossRef
53.
M.S. Hossain, H. Taheri, In situ process monitoring for additive manufacturing through acoustic techniques. J. Mater. Eng. Perform. 29(10), 6249–6262 (2020)CrossRef
54.
C. Wang et al., Machine learning in additive manufacturing: state-of-the-art and perspectives. Addit. Manuf. 36, 101538 (2020)
55.
A.H. Moltumyr, M.H. Arbo, J.T. Gravdahl, Towards vision-based closed-loop additive manufacturing: a review, in 2020 3rd International Symposium on Small-scale Intelligent Manufacturing Systems (SIMS) (IEEE, 2020)
56.
Z.-J. Tang et al., A review on in situ monitoring technology for directed energy deposition of metals. Int. J. Adv. Manuf. Technol. 108(11), 3437–3463 (2020)CrossRef
57.
L. Zhang et al., Digital twins for additive manufacturing: a state-of-the-art review. Appl. Sci. 10(23), 8350 (2020)CrossRef
58.
P. Charalampous, I. Kostavelis, D. Tzovaras, Non-destructive quality control methods in additive manufacturing: a survey. Rapid Prototyp. J. 26(4), 777–790 (2020)
59.
Z. Jin et al., Machine learning for advanced additive manufacturing. Matter 3(5), 1541–1556 (2020)CrossRef
60.
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
61.
S.S. Razvi et al., A review of machine learning applications in additive manufacturing, in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (American Society of Mechanical Engineers, 2019)
62.
Z. Jiang, J. Wang, Research status of on-line monitoring of laser metal deposition. IOP Conf. Ser. Mater. Sci. Eng. 605(1):012020
63.
D. Huang, H. Li, Review of machine learning applications in powder bed fusion technology for part production. Proc. Int. Conf. Prog. Addit. Manuf. (2018)
64.
M.O. Alabi, K. Nixon, I. Botef, A survey on recent applications of machine learning with big data in additive manufacturing industry. Am. J. Eng. Appl. Sci. 11(3) (2018)
65.
J. Yang et al., Survey on artificial intelligence for additive manufacturing, in 2017 23rd International Conference on Automation and Computing (ICAC) (IEEE, 2017)
66.
Z.Y. Chua, I.H. Ahn, S.K. Moon, Process monitoring and inspection systems in metal additive manufacturing: status and applications. Int. J. Precis. Eng. Manuf. Green Technol. 4(2), 235–245 (2017)CrossRef
67.
W.-W. Liu et al., A review on in-situ monitoring and adaptive control technology for laser cladding remanufacturing. Proc. CIRP 61, 235–240 (2017)CrossRef
68.
M. Mani et al., A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes. Int. J. Prod. Res. 55(5), 1400–1418 (2017)CrossRef
69.
S.K. Everton et al., Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Mater. Des. 95, 431–445 (2016)CrossRef
70.
T.G. Spears, S.A. Gold, In-process sensing in selective laser melting (SLM) additive manufacturing. Integr. Mater. Manuf. Innov. 5(1), 16–40 (2016)CrossRef
71.
E.W. Reutzel, A.R. Nassar, A survey of sensing and control systems for machine and process monitoring of directed-energy, metal-based additive manufacturing. Rapid Prototyp. J. 21(2):159–167 (2015)
72.
G. Tapia, A. Elwany, A review on process monitoring and control in metal-based additive manufacturing. J. Manuf. Sci. Eng. 136(6) (2014)
73.
P. Yadav et al., Inline drift detection using monitoring systems and machine learning in selective laser melting. Adv. Eng. Mater. 22(12), 2000660 (2020)CrossRef
74.
J. Ling et al., Building data-driven models with microstructural images: generalization and interpretability. Mater. Discov. 10, 19–28 (2017)CrossRef
75.
A. Zheng, A. Casari, Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists (O’Reilly Media, Inc., 2018)
76.
G.X. Gu et al., Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater. Horiz. 5(5), 939–945 (2018)CrossRef
77.
L. Wang et al., Data-driven topology optimization with multiclass microstructures using latent variable Gaussian process. J. Mech. Des. 143(3) (2020)
78.
X. Lin et al., Motion feature based melt pool monitoring for selective laser melting process. J. Mater. Process. Technol. 303, 117523 (2022)CrossRef
79.
X. Zhao et al., Automated anomaly detection of laser-based additive manufacturing using melt pool sparse representation and unsupervised learning, in 2021 International Solid Freeform Fabrication Symposium (University of Texas at Austin, 2021)
80.
D. Rose et al., Automated semantic segmentation of NiCrBSi-WC optical microscopy images using convolutional neural networks. Comput. Mater. Sci. 210, 111391 (2022)CrossRef
81.
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
82.
S.L. Chan, Y. Lu, Y. Wang, Data-driven cost estimation for additive manufacturing in cybermanufacturing. J. Manuf. Syst. 46, 115–126 (2018)CrossRef
83.
D.B. Kim et al., Streamlining the additive manufacturing digital spectrum: a systems approach. Addit. Manuf. 5, 20–30 (2015)
84.
M. Schotten et al., A brief history of Scopus: the world’s largest abstract and citation database of scientific literature, in Research Analytics (Auerbach Publications, 2017), pp. 31–58
Metadaten
Titel
Introduction
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_1

Premium Partner

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

    Zur Marktübersicht
    Bildnachweise