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...

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Published in:	Materials Vol. 17; no. 18; p. 4544
Main Authors:	Aljabali, Bader Alwomi, Shelton, Joseph, Desai, Salil
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 16.09.2024 MDPI
Subjects:	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
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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
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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
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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
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Snippet	Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications....
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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
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