Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Materials Ročník 17; číslo 18; s. 4544
Hlavní autoři: Aljabali, Bader Alwomi, Shelton, Joseph, Desai, Salil
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 16.09.2024
MDPI
Témata:
3D printing
Additive manufacturing
Algorithms
Automation
Classification
Datasets
Decision making
Design
Designers
Dimensional analysis
Genetic algorithms
Industrial applications
Industrial development
Industry 4.0
Knowledge
Knowledge bases (artificial intelligence)
Manufacturability
Mathematical optimization
Neural networks
Optimization
Particle swarm optimization
Process planning
Process selection
Rapid prototyping
Technology application
Three dimensional printing
Taiwan
genetic algorithm
expert system
design for additive manufacturing
Industry 4.0
ISSN:1996-1944, 1996-1944
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.
AbstractList Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.
Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.
Audience Academic
Author Desai, Salil
Aljabali, Bader Alwomi
Shelton, Joseph
AuthorAffiliation 3 Center for Excellence in Product Design and Advanced Manufacturing, North Carolina A & T State University, Greensboro, NC 27411, USA
2 Department of Computer Science, College of Engineering and Technology, Virginia State University, Petersburg, VA 23806, USA; jshelton@vsu.edu
1 Department of Industrial & Systems Engineering, College of Engineering, North Carolina A & T State University, Greensboro, NC 27411, USA; bmalwoim@aggies.ncat.edu
AuthorAffiliation_xml – name: 2 Department of Computer Science, College of Engineering and Technology, Virginia State University, Petersburg, VA 23806, USA; jshelton@vsu.edu
– name: 1 Department of Industrial & Systems Engineering, College of Engineering, North Carolina A & T State University, Greensboro, NC 27411, USA; bmalwoim@aggies.ncat.edu
– name: 3 Center for Excellence in Product Design and Advanced Manufacturing, North Carolina A & T State University, Greensboro, NC 27411, USA
Author_xml – sequence: 1
  givenname: Bader Alwomi
  surname: Aljabali
  fullname: Aljabali, Bader Alwomi
– sequence: 2
  givenname: Joseph
  orcidid: 0000-0001-9371-7403
  surname: Shelton
  fullname: Shelton, Joseph
– sequence: 3
  givenname: Salil
  orcidid: 0000-0002-6116-2105
  surname: Desai
  fullname: Desai, Salil
BackLink https://www.ncbi.nlm.nih.gov/pubmed/39336285$$D View this record in MEDLINE/PubMed
BookMark eNptklFvFCEQx4mpsfXsix_AkPhiTPaEBXaXJ3O2WpvUaNK-Ew6GK80uVGCb3LeX82qtjfDABH7zZyb_eYkOQgyA0GtKloxJ8mHStKcDF5w_Q0dUyq6hkvODR_EhOs75htTFGB1a-QIdMslY1w7iCG3OIEDxBq_GTUy-XE_NJ53B4lNddHOa_B0E_CNFAznjSxjBFB8DvtzmAhN2MeGVtb5UDH_TYXbalDn5sME-4PNg51zSFvMleYWeOz1mOL4_F-jqy-erk6_Nxfez85PVRWM470rDoDcDJ6Z1vGPW2XVvBaNSgF3zzhDHtdN6rXttuQNmpSC2k0PHBBMDE5ot0Me97O28nsAaCCXpUd0mP-m0VVF79e9L8NdqE-8UpZwx1rZV4d29Qoo_Z8hFTT4bGEcdIM5ZMUqJJLIXsqJvn6A3cU6htvebEnKQbEct99RGj6B8cLF-bOq2MHlTzXS-3q8Gukvpq0kL9OZxDw_F_zGtAu_3gEkx5wTuAaFE7YZC_R2KCpMnsPFF70ysZfjxfym_AIrtuLM
CitedBy_id crossref_primary_10_3390_machines13010029
crossref_primary_10_3390_ma18020408
crossref_primary_10_3390_technologies13030094
crossref_primary_10_3390_systems13090793
Cites_doi 10.1016/S0004-3702(03)00012-2
10.3390/s24154864
10.1016/j.ijmachtools.2005.11.008
10.3390/machines11010095
10.1007/s12008-022-00956-4
10.1016/j.promfg.2020.10.104
10.1007/s40964-022-00351-1
10.1007/s00170-018-2034-6
10.3844/ajeassp.2017.264.271
10.1002/adma.202200512
10.1002/adma.201301036
10.1109/COASE.2008.4626477
10.1016/S0736-5845(02)00017-0
10.1007/s00170-016-9548-6
10.1007/s10845-018-1412-0
10.3390/polym15112519
10.1016/j.engappai.2009.04.005
10.1108/RPJ-10-2018-0262
10.1109/ACCESS.2024.3395444
10.1109/SMC.2013.7
10.1108/17563781311301490
10.1080/00207543.2018.1516905
10.1002/9783527835478
10.1016/j.cad.2018.12.007
10.1007/978-3-319-04313-5_11
10.1007/s12008-021-00786-w
10.1007/s00170-020-05884-9
10.1115/1.4035787
10.1002/1097-4636(200009)53:5<525::AID-JBM12>3.0.CO;2-1
10.1007/s00170-012-4425-4
10.1115/1.4042084
10.1016/j.future.2003.11.024
10.1115/ISFA2012-7256
10.1021/acs.analchem.0c03299
10.1080/01431160600746456
10.1016/j.jechem.2023.01.037
10.1002/adfm.202107671
10.2351/1.4885235
10.1201/9781003279501
10.1016/j.promfg.2019.06.208
10.1007/s10462-020-09876-9
10.1109/ICRA.2019.8793989
10.1109/CEC.2000.870381
10.1016/j.autcon.2011.06.010
10.1007/s10639-021-10733-7
10.3390/ma17122822
10.1179/1743280411Y.0000000014
10.1109/72.265956
10.3390/mi15050636
10.1007/s00170-019-04596-z
10.1016/j.cirp.2018.04.119
ContentType Journal Article
Copyright COPYRIGHT 2024 MDPI AG
2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
2024 by the authors. 2024
Copyright_xml – notice: COPYRIGHT 2024 MDPI AG
– notice: 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
– notice: 2024 by the authors. 2024
DBID AAYXX
CITATION
NPM
7SR
8FD
8FE
8FG
ABJCF
ABUWG
AFKRA
AZQEC
BENPR
BGLVJ
CCPQU
D1I
DWQXO
HCIFZ
JG9
KB.
PDBOC
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
7X8
5PM
DOI 10.3390/ma17184544
DatabaseName CrossRef
PubMed
Engineered Materials Abstracts
Technology Research Database
ProQuest SciTech Collection
ProQuest Technology Collection
Materials Science & Engineering Collection
ProQuest Central (Alumni)
ProQuest Central
ProQuest Central Essentials
ProQuest Central (subscription)
Technology Collection
ProQuest One Community College
ProQuest Materials Science Collection
ProQuest Central
SciTech Premium Collection
Materials Research Database
Materials Science Database
Materials Science Collection
ProQuest Databases
ProQuest One Academic
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
MEDLINE - Academic
PubMed Central (Full Participant titles)
DatabaseTitle CrossRef
PubMed
Publicly Available Content Database
Materials Research Database
Technology Collection
Technology Research Database
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
Materials Science Collection
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Central China
ProQuest Central
ProQuest One Applied & Life Sciences
Engineered Materials Abstracts
ProQuest Central Korea
Materials Science Database
ProQuest Central (New)
ProQuest Materials Science Collection
ProQuest One Academic Eastern Edition
ProQuest Technology Collection
ProQuest SciTech Collection
ProQuest One Academic UKI Edition
Materials Science & Engineering Collection
ProQuest One Academic
ProQuest One Academic (New)
MEDLINE - Academic
DatabaseTitleList CrossRef
Publicly Available Content Database
MEDLINE - Academic
PubMed
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: KB.
  name: Materials Science Database
  url: http://search.proquest.com/materialsscijournals
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1996-1944
ExternalDocumentID PMC11433322
A811105700
39336285
10_3390_ma17184544
Genre Journal Article
GeographicLocations Taiwan
GeographicLocations_xml – name: Taiwan
GrantInformation_xml – fundername: National Science Foundation
  grantid: #2100850
– fundername: National Science Foundation
  grantid: #2100739
– fundername: Department of Defense
– fundername: National Science Foundation (NSF CMMI) Awards
  grantid: #2100739; #2100850; #2434487; 2200538
GroupedDBID 29M
2WC
2XV
53G
5GY
5VS
8FE
8FG
AADQD
AAFWJ
AAHBH
AAYXX
ABDBF
ABJCF
ACUHS
ADBBV
ADMLS
AENEX
AFFHD
AFKRA
AFZYC
ALMA_UNASSIGNED_HOLDINGS
AOIJS
BCNDV
BENPR
BGLVJ
CCPQU
CITATION
CZ9
D1I
E3Z
EBS
ESX
FRP
GX1
HCIFZ
HH5
HYE
I-F
IAO
ITC
KB.
KC.
KQ8
MK~
MODMG
M~E
OK1
OVT
P2P
PDBOC
PGMZT
PHGZM
PHGZT
PIMPY
PQGLB
PROAC
RPM
TR2
TUS
NPM
7SR
8FD
ABUWG
AZQEC
DWQXO
JG9
PKEHL
PQEST
PQQKQ
PQUKI
PRINS
7X8
PUEGO
5PM
ID FETCH-LOGICAL-c446t-3e7c840c2f463dfdb7d53195edb46c0f4afaaba7ad4fe3d950d69863535835a3
IEDL.DBID PIMPY
ISICitedReferencesCount 6
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001323173400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1996-1944
IngestDate Tue Nov 04 02:05:35 EST 2025
Fri Sep 05 06:50:02 EDT 2025
Fri Jul 25 11:21:44 EDT 2025
Tue Nov 04 18:17:46 EST 2025
Mon Jul 21 05:56:46 EDT 2025
Sat Nov 29 07:17:04 EST 2025
Tue Nov 18 20:39:31 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 18
Keywords genetic algorithm
expert system
design for additive manufacturing
Industry 4.0
Language English
License Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c446t-3e7c840c2f463dfdb7d53195edb46c0f4afaaba7ad4fe3d950d69863535835a3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ORCID 0000-0001-9371-7403
0000-0002-6116-2105
OpenAccessLink https://www.proquest.com/publiccontent/docview/3110598939?pq-origsite=%requestingapplication%
PMID 39336285
PQID 3110598939
PQPubID 2032366
ParticipantIDs pubmedcentral_primary_oai_pubmedcentral_nih_gov_11433322
proquest_miscellaneous_3110909759
proquest_journals_3110598939
gale_infotracacademiconefile_A811105700
pubmed_primary_39336285
crossref_primary_10_3390_ma17184544
crossref_citationtrail_10_3390_ma17184544
PublicationCentury 2000
PublicationDate 20240916
PublicationDateYYYYMMDD 2024-09-16
PublicationDate_xml – month: 9
  year: 2024
  text: 20240916
  day: 16
PublicationDecade 2020
PublicationPlace Switzerland
PublicationPlace_xml – name: Switzerland
– name: Basel
PublicationTitle Materials
PublicationTitleAlternate Materials (Basel)
PublicationYear 2024
Publisher MDPI AG
MDPI
Publisher_xml – name: MDPI AG
– name: MDPI
References Sun (ref_14) 2013; 25
Fidelis (ref_42) 2000; Volume 1
Aminzadeh (ref_64) 2019; 30
Williams (ref_29) 2019; 28
ref_58
ref_57
ref_56
ref_54
ref_53
Pearson (ref_27) 2022; 27
ref_52
Baigarina (ref_18) 2023; 8
Budinoff (ref_25) 2021; 15
ref_51
Waxman (ref_23) 2002; Volume 1
Parupelli (ref_22) 2020; 110
Desai (ref_63) 2018; 97
Liu (ref_44) 2004; 20
Wu (ref_66) 2017; 90
Lu (ref_45) 2007; 28
Hoenig (ref_71) 2024; 12
Desai (ref_12) 2013; 64
Masood (ref_31) 2002; 18
ref_69
ref_68
ref_67
Goh (ref_32) 2021; 54
ref_21
ref_20
Prashar (ref_7) 2023; 17
ref_28
Lim (ref_17) 2012; 21
Alford (ref_50) 2013; 6
Ahsan (ref_46) 2016; 11
Mehrpouya (ref_70) 2019; 105
Mani (ref_60) 2017; 58110
Shimbo (ref_33) 2003; 146
ref_36
Fogel (ref_39) 1994; 5
Parvanda (ref_3) 2023; 8
Curodeau (ref_9) 2000; 53
Perkins (ref_10) 2014; 102
ref_38
Kranz (ref_55) 2015; 27
Zhu (ref_65) 2018; 67
Parupelli (ref_6) 2017; 10
Deka (ref_48) 2019; 34
Nagarajan (ref_34) 2019; 141
Vaissier (ref_49) 2019; 110
Khorrami (ref_13) 2022; 34
Dinar (ref_62) 2017; 17
ref_47
Rayna (ref_1) 2014; Volume 261
Mubarak (ref_16) 2023; 81
Rojek (ref_30) 2021; 357
Tully (ref_24) 2020; 92
ref_43
ref_41
Gu (ref_19) 2012; 57
ref_40
Salehi (ref_59) 2009; 22
ref_2
Wang (ref_26) 2019; 57
Dwivedi (ref_35) 2006; 46
Fountas (ref_37) 2020; 51
ref_8
Wiberg (ref_61) 2019; 25
ref_5
ref_4
Desai (ref_15) 2014; 8
Ali (ref_11) 2022; 32
References_xml – volume: 8
  start-page: 477
  year: 2014
  ident: ref_15
  article-title: Direct Write Manufacturing of Solid Oxide Fuel Cells for Green Energy
  publication-title: J. Environ. Res. Dev.
– volume: 146
  start-page: 1
  year: 2003
  ident: ref_33
  article-title: Controlling the Learning Process of Real-Time Heuristic Search
  publication-title: Artif. Intell.
  doi: 10.1016/S0004-3702(03)00012-2
– ident: ref_5
– ident: ref_67
  doi: 10.3390/s24154864
– ident: ref_51
– volume: Volume 1
  start-page: 562
  year: 2002
  ident: ref_23
  article-title: Information Fusion for Image Analysis: Geospatial Foundations for Higher-Level Fusion
  publication-title: Proceedings of the 5th International Conference on Information Fusion, FUSION 2002
– volume: 46
  start-page: 1811
  year: 2006
  ident: ref_35
  article-title: An Expert System for Generation of Machine Inputs for Laser-Based Multi-Directional Metal Deposition
  publication-title: Int. J. Mach. Tools Manuf.
  doi: 10.1016/j.ijmachtools.2005.11.008
– ident: ref_38
  doi: 10.3390/machines11010095
– volume: 17
  start-page: 2221
  year: 2023
  ident: ref_7
  article-title: Additive Manufacturing: Expanding 3D Printing Horizon in Industry 4.0
  publication-title: Int. J. Interact. Des. Manuf.
  doi: 10.1007/s12008-022-00956-4
– volume: 51
  start-page: 740
  year: 2020
  ident: ref_37
  article-title: Single and Multi-Objective Optimization of FDM-Based Additive Manufacturing Using Metaheuristic Algorithms
  publication-title: Procedia Manuf.
  doi: 10.1016/j.promfg.2020.10.104
– volume: 102
  start-page: 4290
  year: 2014
  ident: ref_10
  article-title: Direct Writing of Bio-Functional Coatings for Cardiovascular Applications
  publication-title: J. Biomed. Mater. Res. Part. A
– ident: ref_58
– volume: 8
  start-page: 587
  year: 2023
  ident: ref_3
  article-title: Trends, Opportunities, and Challenges in the Integration of the Additive Manufacturing with Industry 4.0
  publication-title: Prog. Addit. Manuf.
  doi: 10.1007/s40964-022-00351-1
– volume: 97
  start-page: 1719
  year: 2018
  ident: ref_63
  article-title: Cyber-Enabled Concurrent Material and Process Selection in a Flexible Design for Manufacture Paradigm
  publication-title: Int. J. Adv. Manuf. Technol.
  doi: 10.1007/s00170-018-2034-6
– volume: 10
  start-page: 264
  year: 2017
  ident: ref_6
  article-title: Understanding Hybrid Additive Manufacturing of Functional Devices
  publication-title: Am. J. Eng. Appl. Sci.
  doi: 10.3844/ajeassp.2017.264.271
– volume: 34
  start-page: 2200512
  year: 2022
  ident: ref_13
  article-title: Multiphoton Lithography of Organic Semiconductor Devices for 3D Printing of Flexible Electronic Circuits, Biosensors, and Bioelectronics
  publication-title: Adv. Mater.
  doi: 10.1002/adma.202200512
– ident: ref_56
– volume: 25
  start-page: 4539
  year: 2013
  ident: ref_14
  article-title: 3D Printing of Interdigitated Li-Ion Microbattery Architectures
  publication-title: Adv. Mater.
  doi: 10.1002/adma.201301036
– ident: ref_36
  doi: 10.1109/COASE.2008.4626477
– ident: ref_52
– volume: 18
  start-page: 267
  year: 2002
  ident: ref_31
  article-title: A Rule Based Expert System for Rapid Prototyping System Selection
  publication-title: Robot. Comput.-Integr. Manuf.
  doi: 10.1016/S0736-5845(02)00017-0
– volume: 90
  start-page: 2027
  year: 2017
  ident: ref_66
  article-title: Real-Time FDM Machine Condition Monitoring and Diagnosis Based on Acoustic Emission and Hidden Semi-Markov Model
  publication-title: Int. J. Adv. Manuf. Technol.
  doi: 10.1007/s00170-016-9548-6
– volume: 30
  start-page: 2505
  year: 2019
  ident: ref_64
  article-title: Online Quality Inspection Using Bayesian Classification in Powder-Bed Additive Manufacturing from High-Resolution Visual Camera Images
  publication-title: J. Intell. Manuf.
  doi: 10.1007/s10845-018-1412-0
– ident: ref_41
– volume: 11
  start-page: 85
  year: 2016
  ident: ref_46
  article-title: AM Optimization Framework for Part and Process Attributes through Geometric Analysis
  publication-title: Addit. Manuf.
– ident: ref_21
  doi: 10.3390/polym15112519
– volume: 22
  start-page: 1179
  year: 2009
  ident: ref_59
  article-title: Application of Genetic Algorithm to Computer-Aided Process Planning in Preliminary and Detailed Planning
  publication-title: Eng. Appl. Artif. Intell.
  doi: 10.1016/j.engappai.2009.04.005
– volume: 25
  start-page: 1080
  year: 2019
  ident: ref_61
  article-title: Design for Additive Manufacturing—A Review of Available Design Methods and Software
  publication-title: Rapid Prototyp. J.
  doi: 10.1108/RPJ-10-2018-0262
– volume: 58110
  start-page: V001T02A035
  year: 2017
  ident: ref_60
  article-title: Design Rules for Additive Manufacturing: A Categorization
  publication-title: Proceedings of the Volume 1: 37th Computers and Information in Engineering Conference
– volume: 28
  start-page: 676
  year: 2019
  ident: ref_29
  article-title: Additive Manufacturing Standards for Space Resource Utilization
  publication-title: Addit. Manuf.
– volume: 12
  start-page: 73113
  year: 2024
  ident: ref_71
  article-title: Explainable AI for Cyber-Physical Systems: Issues and Challenges
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2024.3395444
– ident: ref_43
  doi: 10.1109/SMC.2013.7
– volume: 6
  start-page: 4
  year: 2013
  ident: ref_50
  article-title: Genetic and Evolutionary Biometrics: Exploring Value Preference Space for Hybrid Feature Weighting and Selection
  publication-title: Int. J. Intell. Comput. Cybern.
  doi: 10.1108/17563781311301490
– ident: ref_28
– ident: ref_53
– volume: 57
  start-page: 3975
  year: 2019
  ident: ref_26
  article-title: IoT-Enabled Cloud-Based Additive Manufacturing Platform to Support Rapid Product Development
  publication-title: Int. J. Prod. Res.
  doi: 10.1080/00207543.2018.1516905
– ident: ref_4
  doi: 10.1002/9783527835478
– volume: 110
  start-page: 11
  year: 2019
  ident: ref_49
  article-title: Genetic-Algorithm Based Framework for Lattice Support Structure Optimization in Additive Manufacturing
  publication-title: Comput.-Aided Des.
  doi: 10.1016/j.cad.2018.12.007
– volume: 357
  start-page: 04001
  year: 2021
  ident: ref_30
  article-title: Intelligent System Supporting Technological Process Planning for Machining and 3D Printing
  publication-title: Bull. Pol. Acad. Sci. Tech. Sci.
– volume: Volume 261
  start-page: 119
  year: 2014
  ident: ref_1
  article-title: The Impact of 3D Printing Technologies on Business Model Innovation
  publication-title: Advances in Intelligent Systems and Computing
  doi: 10.1007/978-3-319-04313-5_11
– volume: 15
  start-page: 613
  year: 2021
  ident: ref_25
  article-title: Will It Print: A Manufacturability Toolbox for 3D Printing
  publication-title: Int. J. Interact. Des. Manuf.
  doi: 10.1007/s12008-021-00786-w
– volume: 110
  start-page: 543
  year: 2020
  ident: ref_22
  article-title: Hybrid Additive Manufacturing (3D Printing) and Characterization of Functionally Gradient Materials via in Situ Laser Curing
  publication-title: Int. J. Adv. Manuf. Technol.
  doi: 10.1007/s00170-020-05884-9
– ident: ref_40
– volume: 17
  start-page: 021013
  year: 2017
  ident: ref_62
  article-title: A Design for Additive Manufacturing Ontology
  publication-title: J. Comput. Inf. Sci. Eng.
  doi: 10.1115/1.4035787
– volume: 53
  start-page: 525
  year: 2000
  ident: ref_9
  article-title: Design and Fabrication of Cast Orthopedic Implants with Freeform Surface Textures from 3-D Printed Ceramic Shell
  publication-title: J. Biomed. Mater. Res.
  doi: 10.1002/1097-4636(200009)53:5<525::AID-JBM12>3.0.CO;2-1
– volume: 64
  start-page: 537
  year: 2013
  ident: ref_12
  article-title: Direct Writing of Nanomaterials for Flexible Thin-Film Transistors (fTFTs)
  publication-title: Int. J. Adv. Manuf. Technol.
  doi: 10.1007/s00170-012-4425-4
– volume: 141
  start-page: 021705
  year: 2019
  ident: ref_34
  article-title: Knowledge-Based Design of Artificial Neural Network Topology for Additive Manufacturing Process Modeling: A New Approach and Case Study for Fused Deposition Modeling
  publication-title: J. Mech. Des.
  doi: 10.1115/1.4042084
– volume: 20
  start-page: 1119
  year: 2004
  ident: ref_44
  article-title: Evolving Neural Network Using Real Coded Genetic Algorithm (GA) for Multispectral Image Classification
  publication-title: Future Gener. Comput. Syst.
  doi: 10.1016/j.future.2003.11.024
– ident: ref_20
  doi: 10.1115/ISFA2012-7256
– volume: 8
  start-page: 1393
  year: 2023
  ident: ref_18
  article-title: Construction 3D Printing: A Critical Review and Future Research Directions
  publication-title: Prog. Addit. Manuf.
– volume: 92
  start-page: 14853
  year: 2020
  ident: ref_24
  article-title: A Scientist’s Guide to Buying a 3D Printer: How to Choose the Right Printer for Your Laboratory
  publication-title: Anal. Chem.
  doi: 10.1021/acs.analchem.0c03299
– volume: 28
  start-page: 823
  year: 2007
  ident: ref_45
  article-title: A Survey of Image Classification Methods and Techniques for Improving Classification Performance
  publication-title: Int. J. Remote Sens.
  doi: 10.1080/01431160600746456
– volume: 81
  start-page: 272
  year: 2023
  ident: ref_16
  article-title: Recent Advances in 3D Printed Electrode Materials for Electrochemical Energy Storage Devices
  publication-title: J. Energy Chem.
  doi: 10.1016/j.jechem.2023.01.037
– volume: 32
  start-page: 2107671
  year: 2022
  ident: ref_11
  article-title: Recent Advances in 3D Printing of Biomedical Sensing Devices
  publication-title: Adv. Funct. Mater.
  doi: 10.1002/adfm.202107671
– ident: ref_54
– volume: 27
  start-page: S14001
  year: 2015
  ident: ref_55
  article-title: Design Guidelines for Laser Additive Manufacturing of Lightweight Structures in TiAl6V4
  publication-title: J. Laser Appl.
  doi: 10.2351/1.4885235
– ident: ref_8
  doi: 10.1201/9781003279501
– ident: ref_2
– volume: 34
  start-page: 764
  year: 2019
  ident: ref_48
  article-title: Part Separation Technique for Assembly-Based Design in Additive Manufacturing using Genetic Algorithm
  publication-title: Procedia Manuf.
  doi: 10.1016/j.promfg.2019.06.208
– volume: 54
  start-page: 63
  year: 2021
  ident: ref_32
  article-title: A Review on Machine Learning in 3D Printing: Applications, Potential, and Challenges
  publication-title: Artif. Intell. Rev.
  doi: 10.1007/s10462-020-09876-9
– ident: ref_47
  doi: 10.1109/ICRA.2019.8793989
– volume: Volume 1
  start-page: 805
  year: 2000
  ident: ref_42
  article-title: Discovering Comprehensible Classification Rules with a Genetic Algorithm
  publication-title: Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)
  doi: 10.1109/CEC.2000.870381
– volume: 21
  start-page: 262
  year: 2012
  ident: ref_17
  article-title: Developments in Construction-Scale Additive Manufacturing Processes
  publication-title: Autom. Constr.
  doi: 10.1016/j.autcon.2011.06.010
– volume: 27
  start-page: 3037
  year: 2022
  ident: ref_27
  article-title: 3D Printing as an Educational Technology: Theoretical Perspectives, Learning Outcomes, and Recommendations for Practice
  publication-title: Educ. Inf. Technol.
  doi: 10.1007/s10639-021-10733-7
– ident: ref_69
  doi: 10.3390/ma17122822
– volume: 57
  start-page: 133
  year: 2012
  ident: ref_19
  article-title: Laser Additive Manufacturing of Metallic Components: Materials, Processes and Mechanisms
  publication-title: Int. Mater. Rev.
  doi: 10.1179/1743280411Y.0000000014
– volume: 5
  start-page: 3
  year: 1994
  ident: ref_39
  article-title: An Introduction to Simulated Evolutionary Optimization
  publication-title: IEEE Trans. Neural Netw.
  doi: 10.1109/72.265956
– ident: ref_57
– ident: ref_68
  doi: 10.3390/mi15050636
– volume: 105
  start-page: 4691
  year: 2019
  ident: ref_70
  article-title: A Prediction Model for Finding the Optimal Laser Parameters in Additive Manufacturing of NiTi Shape Memory Alloy
  publication-title: Int. J. Adv. Manuf. Technol.
  doi: 10.1007/s00170-019-04596-z
– volume: 67
  start-page: 157
  year: 2018
  ident: ref_65
  article-title: Machine Learning in Tolerancing for Additive Manufacturing
  publication-title: CIRP Ann.
  doi: 10.1016/j.cirp.2018.04.119
SSID ssj0000331829
Score 2.4238443
Snippet Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications....
SourceID pubmedcentral
proquest
gale
pubmed
crossref
SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
StartPage 4544
SubjectTerms 3D printing
Additive manufacturing
Algorithms
Automation
Classification
Datasets
Decision making
Design
Designers
Dimensional analysis
Genetic algorithms
Industrial applications
Industrial development
Industry 4.0
Knowledge
Knowledge bases (artificial intelligence)
Manufacturability
Mathematical optimization
Neural networks
Optimization
Particle swarm optimization
Process planning
Process selection
Rapid prototyping
Technology application
Three dimensional printing
Title Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0
URI https://www.ncbi.nlm.nih.gov/pubmed/39336285
https://www.proquest.com/docview/3110598939
https://www.proquest.com/docview/3110909759
https://pubmed.ncbi.nlm.nih.gov/PMC11433322
Volume 17
WOSCitedRecordID wos001323173400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 1996-1944
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000331829
  issn: 1996-1944
  databaseCode: M~E
  dateStart: 20080101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
– providerCode: PRVPQU
  databaseName: Materials Science Database
  customDbUrl:
  eissn: 1996-1944
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000331829
  issn: 1996-1944
  databaseCode: KB.
  dateStart: 20080101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/materialsscijournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central
  customDbUrl:
  eissn: 1996-1944
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000331829
  issn: 1996-1944
  databaseCode: BENPR
  dateStart: 20080101
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Publicly Available Content Database
  customDbUrl:
  eissn: 1996-1944
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000331829
  issn: 1996-1944
  databaseCode: PIMPY
  dateStart: 20080101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Nb9MwFH9iLQc4bHwTNiojkBCHrEnsxvFpatkmEFpVwQ7lFDmxs1Xa0q1NkfjveS9xs3ZCnDgmfrIcve_4vd8D-BDVdztR4muRSV-gC_M1dSyTb7Y6zIyNTT1sQo7HyXSqJq49eunKKtc2sTbUDdoz1W2jEe6beU5_zPs8pLgAfa06urn1aYYU3bW6gRo70CXgraAD3cnXs8nP9p9LwFGCI9WglHLM9vvXOkTjLJB6yy_dt84b7mm7dHLDF53u_d-veAK7LiZlw0aInsIDWz6DxxtIhc_hguCpcZkNry5wh-ry2h-h_zPsWFfaP16QzWSu6YD9qGfrIMNZg4fOMDBmQ2PqMiV2pssVtVPU_ZFsVjI3PeQ3E4fBCzg_PTn__MV3Mxr8HBPJyudW5pgj5lEhYm4Kk0lDWj2wJhNxHhRCF1pnWmojCsuNGgQmVglGOXyAsZ_mL6FTzkv7GlihlRV5YaQQEhliUXgMBW8Rvk1sWHjwac2gNHf45TRG4yrFPIaYmd4x04P3Le1Ng9rxV6qPxOeUVBl3yrXrSMDzEChWOkxCYpAMAg8O1uxMnY4v0zvuefCuXUbtpCsXXdr5qqFRgZIDpHnVSE57IK44pwZWD5ItmWoJCPl7e6WcXdYI4JjEco6m-M2_z7UPjyKMwai8JYwPoFMtVvYtPMx_VbPlogc7cpr0oDs6GU--49O30WHPacwfSUIl4A
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFIlyKM-C2wKLACEOprZ3_dhDhQKhatQmikQO7clae9dtpNYpiQPqj-I_MuNXE4S49cDVO7LW9rfzzXh3vgF465V7O15kK5GEtkAKsxVVLBM3G-Um2gS6bDYRDofRyYkcrcGvphaGjlU2PrF01Hqa0j_yPe5SJIDsKj9dfbepaxTtrjYtNCpYHJnrn5iyzff7Pfy-7zzv4Ov4y6FddxWwU0x9CpubMMWsJvUyEXCd6STUhEPf6EQEqZMJlSmVqFBpkRmupe_oQEbIy9zHaEVxvO0dWBf4hE4H1kf9wei0_anjcFwinqxkUDmXzt6lctH7C1-IFeL70_0v8d_q2cwlsjt48J-9poewWUfVrFstg0ewZvLHcH9Ja_EJnJHANg6z7sUZTrg4v7Q_I4Nr1lOFsnsz8vqsLptg38ruQAhZVim6MwztWVfr8qAVG6h8QQUhZYUnm-Ss7n9yzcRH5ymMb-NBt6CTT3PzHFimpBFppkMhQvziBuGvKfz08Gpk3MyCDw0C4rRWYKdGIBcxZmKElvgGLRa8aW2vKt2Rv1q9JyDF5IzwTqmqaypwPiTrFXcjl_AQOo4Fuw1e4tpLzeMbsFjwuh1G_0KbRio300VlIx0Z-mjzrIJmOyEuOacSXAuiFdC2BqRdvjqST85LDXNMwzlHMtn-97xewb3D8eA4Pu4Pj3Zgw8OIkg7ruMEudIrZwryAu-mPYjKfvayXIoP4llH9GwUidNk
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLULlwLsQKGAECHEIm8ROHB8Q2rJdURVWK-iht8iJnXalNlt2s6D-NP4dM3l1FyFuPXCNR5YTf55vJp4HwKugutsJYleLVLoCKczVlLFM3Gy1nxobmarZhByP46MjNdmAX20uDIVVtjqxUtRmltE_8j73yRJAdlX9vAmLmAxHH86_u9RBim5a23YaNUQO7MVPdN8W7_eHuNevg2C0d_jxk9t0GHAzdINKl1uZoYeTBbmIuMlNKg1hMrQmFVHm5ULnWqdaaiNyy40KPROpGDmah2i5aI7TXoNNydHn6cHm7t548rX7weNxPC6Bqkuicq68_pn2kQlEKMQaCf5JBStcuB6nuUJ8o9v_8Se7A7caa5sN6uNxFzZscQ9urtRgvA_HVHgbh9ng9BgXXJ6cubvI7IYNdand4ZzYgDXpFOxb1TUIoczqSu8MTX42MKYKwGJfdLGkRJEq85NNC9b0Rblg4p33AA6v4kW3oVfMCvsIWK6VFVlupBASd9_isTBklgb4NLZ-7sDbFg1J1lRmpwYhpwl6aISc5BI5DrzsZM_reiR_lXpDoEpISeFMmW5yLXA9VO4rGcQ-YUN6ngM7LXaSRnstkkvgOPCiG0a9Q5dJurCzZS2jPCVDlHlYw7RbEFecU2quA_EagDsBqmm-PlJMT6ra5uiec44k8_jf63oONxDKyef98cET2ArQ0KQYHj_agV45X9qncD37UU4X82fNqWSQXDGofwPT2H1z
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Genetic+Algorithm-Based+Data-Driven+Process+Selection+System+for+Additive+Manufacturing+in+Industry+4.0&rft.jtitle=Materials&rft.au=Aljabali%2C+Bader+Alwomi&rft.au=Shelton%2C+Joseph&rft.au=Desai%2C+Salil&rft.date=2024-09-16&rft.issn=1996-1944&rft.eissn=1996-1944&rft.volume=17&rft.issue=18&rft.spage=4544&rft_id=info:doi/10.3390%2Fma17184544&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_ma17184544
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1996-1944&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1996-1944&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1996-1944&client=summon