Abstract

The process of producing a three dimensional product layer by layer is called additive manufacturing. This process not  the same as a traditional machining procedure. Chips are taken out of the raw material to create a component in traditional machining. three dimensional printing is another name for additive manufacturing. The benefit of three dimensional printing is that it can create any complex three dimensional item. The culinary, chemical, aerospace, and healthcare industries, among others, have all used three dimensional printing. Many researchers have been focusing on additive manufacturing. This study aims to investigate the potential applications of artificial intelligence in additive manufacturing. Additive manufacturing made possible by artificial intelligence significantly lowers costs. Even though there are a lot of researchers working in the field of artificial intelligence, few of them have paid close attention to how artificial intelligence may be applied in additive manufacturing. The results of recent studies take on particular importance in this regard. Both academicians and practitioners who are interested in conducting additional research can benefit from the future directions that the research findings from the current study offer. Artificial intelligence, which is evolving quickly, can assist robots and devices in seeing, analyzing, and even drawing conclusions that are comparable to those made by humans. This article's goal is to demonstrate how artificial intelligence approaches, such as machine learning can be applied to the design and oversight of processes in the field of additive manufacturing. The types of data, sources of data, potential variabilities in experimental and simulation data, and the applicability of these data in machine learning algorithms are discussed. There are a number of novel concepts that show how combining these two game-changing technologies could significantly.

Keywords:Machine learning, Artificial Intelligence, Smart manufacturing, Additive manufacturing, Subtractive Manufacturing.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

• Buczak, Artificial Neural Networka L., and Erhan Guven. "A survey of data mining and machine learning methods for cyber security intrusion detection." IEEE Communications surveys & tutorials 18.2 (2015): 1153-1176. https://doi.org/10.1109/COMST.2015.2494502.
• Da Silveira, Giovani, Denis Borenstein, and Flavio S. Fogliatto. "Mass customization: Literature review and research directions." International journal of production economics 72.1 (2001): 1-13. https://doi.org/10.1016/S0925-5273(00)00079-7.
• Das, Paradditive manufacturingita, et al. "Selection of build orientation for optimal support structures and minimum part errors in additive manufacturing." Computer-artificial intelligenceded Design and Applications 14.sup1 (2017): 1-13. https://doi.org/10.1080/16864360.2017.1308074.
• Desnitsky, Vasily, et al. "Combined design technique for secure embedded devices exemplified by a perimeter protection system." Informatics and Automation 48 (2016): 5-31. https://doi.org/10.15622/sp.48.1.
• Duflou, Joost R., et al. "Critical comparison of methods to determine the energy input for discrete manufacturing processes." CIRP Artificial Neural Networkals 61.1 (2012): 63-66. https://doi.org/10.1016/j.cirp.2012.03.025.
• UhlmArtificial Neural Network, Eckart, et al. "Cluster identification of sensor data for predictive martificial intelligencentenance in a Selective Laser Melting machine tool." Procedia manufacturing 24 (2018): 60-65. https://doi.org/10.1016/j.promfg.2018.06.009.
• Edward, M. E. H. R., Tim Ellis, and Jordan Noone. "Real-time adaptive control of additive manufacturing processes using machine learning." U.S. Patent No. 10,234,848. 19 Mar. 2019.
• Frank, Dietmar, and Georges Fadel. "Expert system-based selection of the preferred direction of build for rapid prototyping processes." Journal of Intelligent Manufacturing 6 (1995): 339-345. https://doi.org/10.1007/BF00124677.
• Jardine, Andrew KS, Dadditive manufacturinging Lin, and Dragan Banjevic. "A review on machinery diagnostics and prognostics implementing condition-based martificial intelligencentenance." Mechanical systems and signal processing 20.7 (2006): 1483-1510. https://doi.org/10.1016/j.ymssp.2005.09.012.
• Steuben, John C., Athanasios P. Iliopoulos, and John G. Michopoulos. "Implicit slicing for functionally tartificial intelligencelored additive manufacturing." Computer-artificial intelligenceded Design 77 (2016): 107-119. https://doi.org/10.1016/j.cad.2016.04.003.
• Leary, Martin, et al. "Optimal topology for additive manufacture: A method for enabling additive manufacture of support-free optimal structures." Materials & Design 63 (2014): 678-690. https://doi.org/10.1016/j.matdes.2014.06.015.
• Mansour, S., and R. Hague. "Impact of rapid manufacturing on design for manufacture for injection moulding." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 217.4 (2003): 453-461. https://doi.org/10.1243/095440503321628134.
• Mireles, Jorge, et al. "Analysis and correction of defects within parts fabricated using powder bed fusion technology." Surface Topography: Metrology and Properties 3.3 (2015): 034002. https://doi.org/1088/2051-672X/3/3/034002.
• Morgan, H. D., et al. "Part orientation optimisation for the additive layer manufacture of metal components." The International Journal of Advanced Manufacturing Technology 86 (2016): 1679-1687. https://doi.org/10.1007/s00170-015-8151-6.
• Strano, Giorgio, et al. "A new approach to the design and optimisation of support structures in additive manufacturing." The International Journal of Advanced Manufacturing Technology 66 (2013): 1247-1254. https://doi.org/10.1007/s00170-012-4403-x.
• Tao, Fei, et al. "Cloud manufacturing: a computing and service-oriented manufacturing model." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 225.10 (2011): 1969-1976. https://doi.org/10.1177/0954405411405575.
• Milazzo, Mario, and Flavia Libonati. "The synergistic role of additive manufacturing and artificial intelligence for the design of new advanced intelligent systems." Advanced Intelligent Systems 4.6 (2022): 2100278. https://doi.org/10.1002/artificial intelligencesy.202100278.
• Masinelli, Giulio, et al. "Artificial intelligence for monitoring and control of metal additive manufacturing." Industrializing Additive Manufacturing: Proceedings of additive manufacturingPA2020. Springer International Publishing, 2021. https://doi.org/10.1007/978-3-030-54334-1_15.
• Ladani, Leila JArtificial Neural Networkesari. "Applications of artificial intelligence and machine learning in metal additive manufacturing." Journal of Physics: Materials 4.4 (2021): 042009. https://doi.org/1088/2515-7639/ac2791.
• Habeeb, Hiyadditive manufacturing Adil, et al. "Design optimization method based on artificial intelligence (hybrid method) for repartificial intelligencer and restoration using additive manufacturing technology." Metals 13.3 (2023): 490. https://doi.org/10.3390/met13030490.
• Mario Milazzo, FlaviaLibonati-The Synergistic Role of Additive Manufacturing and Artificial Intelligence for the Design of New Advanced Intelligent Systems-Advanced Intelligent systems-First published: 19 March 2022 https://doi.org/10.1002/artificial intelligencesy.202100278.
• Jiang, Jingchao, et al. "Machine learning integrated design for additive manufacturing." Journal of Intelligent Manufacturing 33.4 (2022): 1073-1086. https://doi.org/10.1007/s10845-020-01715-6.
• Goh, Guo Dong, Swee Leong Sing, and Wartificial intelligence Yee Yeong. "A review on machine learning in three dimensional printing: applications, potential, and challenges." Artificial Intelligence Review 54.1 (2021): 63-94. https://doi.org/10.1007/s10462-020-09876-9.
• Zhu, Zuowei, et al. "Machine learning in tolerancing for additive manufacturing." CIRP Artificial Neural Networkals 67.1 (2018): 157-160. https://doi.org/10.1016/j.cirp.2018.04.119.
• Khanzadeh, Mojtaba, et al. "Quantifying geometric accuracy with unsupervised machine learning: Using self-organizing map on fused filadditive manufacturingent fabrication additive manufacturing parts." Journal of Manufacturing Science and Engineering 140.3 (2018): 031011. https://doi.org/10.1115/1.4038598.
• Chan, Siu L., Yanglong Lu, and Yan Wang. "Data-driven cost estimation for additive manufacturing in cybermanufacturing." Journal of manufacturing systems 46 (2018): 115-126. https://doi.org/10.1016/j.jmsy.2017.12.001.
• Chinchanikar, Satish, and Avez A. Shartificial intelligencekh. "A review on machine learning, big data analytics, and design for additive manufacturing for aerospace applications." Journal of Materials Engineering and Performance 31.8 (2022): 6112-6130. https://doi.org/10.1007/s11665-022-07125-4.
• Moon, Hyunkyu, et al. "Ultra-power-dense heat exchanger development through genetic algorithm design and additive manufacturing." Joule 5.11 (2021): 3045-3056. https://doi.org/10.1016/j.joule.2021.08.004.
• De Crescenzio, Francesca, Massimiliano Fantini, and Edison Asllani. "Generative design of three dimensional printed hands-free door handles for reduction of contagion risk in public buildings." International Journal on Interactive Design and Manufacturing (IJIDeM) 16.1 (2022): 253-261. https://doi.org/10.1007/s12008-021-00825-6.
• Zhang, Yicha, et al. "Bio-inspired generative design for support structure generation and optimization in Additive Manufacturing (additive manufacturing)." CIRP Artificial Neural Networkals 69.1 (2020): 117-120. https://doi.org/10.1016/j.cirp.2020.04.091.
• Wang, Zhiping, Yicha Zhang, and Alartificial intelligencen Bernard. "A constructive solid geometry-based generative design method for additive manufacturing." Additive Manufacturing 41 (2021): 101952. https://doi.org/10.1016/j.addma.2021.101952.
• Singh, Nagendra, and Manoj Kumar Agrawal. "Effect of ECAP process on deformability, microstructure and conductivity of AA5083 under thermal effect." MATEC Web of Conferences. Vol. 392. EDP Sciences, 2024.
• Bingwen, Duan. "Analysis on the Value of three dimensional Printing in Jewelry Design Based on Artificial Intelligence [J]." Journal of Physics: Conference Series. Vol. 1744. No. 4. 2021. https://doi.org/1088/1742-6596/1744/4/042132.
• Kim, Sadditive manufacturingyeon, et al. "A design for additive manufacturing ontology to support manufacturability analysis." Journal of Computing and Information Science in Engineering 19.4 (2019): 041014. https://doi.org/10.1115/1.4043531.
• Singh, Nagendra, and Manoj Kumar Agrawal. "A Study on the Application of a New Design for Equal Channel Angular Pressing Die and Design Parameters." Advances in Machinery, Materials Science and Engineering Application X. IOS Press, 2024. 132-139.
• Ko, H., et al. "Machine learning and knowledge graph based design rule construction for additive manufacturing. Addit Manuf 37: 101620." 2021, https://doi.org/10.1016/j.addma.2020.101620.
• Ahn, Jaeseung, et al. "Knowledge-based design algorithm for support reduction in material extrusion additive manufacturing." Micromachines 13.10 (2022): 1672. https://doi.org/10.3390/mi13101672.
• Formentini, GiovArtificial Neural Networki, et al. "A fradditive manufacturingework to collect and reuse engineering knowledge in the context of design for additive manufacturing." Proceedings of the Design Society 2 (2022): 1371-1380. https://doi.org/10.1017/pds.2022.139.
• Schaechtl, Paul, et al. "Knowledge-driven design for additive manufacturing: a fradditive manufacturingework for design adaptation." Proceedings of the Design Society 3 (2023): 2405-2414. https://doi.org/10.1017/pds.2023.241.
• Williadditive manufacturings, Glen, et al. "Design repository effectiveness for three dimensional convolutional neural networks: Application to additive manufacturing." Journal of Mechanical Design 141.11 (2019): 111701. https://doi.org/10.1115/1.4044199.
• Koeppe, Arnd, et al. "Efficient numerical modeling of three dimensional-printed lattice-cell structures using neural networks." Manufacturing Letters 15 (2018): 147-150. https://doi.org/10.1016/j.mfglet.2018.01.002.
• Despres, Nathaniel, et al. "Deep learning and design for additive manufacturing: a fradditive manufacturingework for microlattice architecture." Jom 72 (2020): 2408-2418. https://doi.org/10.1007/s11837-020-04131-6.
• Dong, Guoying, et al. "Design and optimization of solid lattice hybrid structures fabricated by additive manufacturing." Additive Manufacturing 33 (2020): 101116. https://doi.org/10.1016/j.addma.2020.101116.
• Xie, Guoquan, et al. "A hybrid multi-stage decision-making method with probabilistic interval-valued hesitant fuzzy set for three dimensional printed composite material selection." Engineering Applications of Artificial Intelligence 123 (2023): 106483. https://doi.org/10.1016/j.engappartificial intelligence.2023.106483.
• Wang, Liwei, et al. "Deep generative modeling for mechanistic-based learning and design of metadditive manufacturingaterial systems." Computer Methods in Applied Mechanics and Engineering 372 (2020): 113377. https://doi.org/10.1016/j.cma.2020.113377
• Rometsch, Paul A., et al. "Review of high-strength aluminium alloys for additive manufacturing by laser powder bed fusion." Materials & Design 219 (2022): 110779. https://doi.org/10.1016/j.matdes.2022.110779.
• Mbodj, Natago Guilé, et al. "Bead geometry prediction in laser-wire additive manufacturing process using machine learning: Case of study." Applied Sciences 11.24 (2021): 11949. https://doi.org/10.3390/app112411949.
• Aoyagi, K., et al. "Simple method to construct process maps for additive manufacturing using a support vector machine. Addit Manuf 27: 353–362." 2019, https://doi.org/10.1016/j.addma.2019.03.013.
• Charaladditive manufacturingpous, Paschalis, et al. "Machine learning-based mechanical behavior optimization of three dimensional print constructs manufactured via the FFF process." Journal of Materials Engineering and Performance 31.6 (2022): 4697-4706. https://doi.org/10.1007/s11665-021-06535-0.

Abstract

The process of producing a three dimensional product layer by layer is called additive manufacturing. This process not  the same as a traditional machining procedure. Chips are taken out of the raw material to create a component in traditional machining. three dimensional printing is another name for additive manufacturing. The benefit of three dimensional printing is that it can create any complex three dimensional item. The culinary, chemical, aerospace, and healthcare industries, among others, have all used three dimensional printing. Many researchers have been focusing on additive manufacturing. This study aims to investigate the potential applications of artificial intelligence in additive manufacturing. Additive manufacturing made possible by artificial intelligence significantly lowers costs. Even though there are a lot of researchers working in the field of artificial intelligence, few of them have paid close attention to how artificial intelligence may be applied in additive manufacturing. The results of recent studies take on particular importance in this regard. Both academicians and practitioners who are interested in conducting additional research can benefit from the future directions that the research findings from the current study offer. Artificial intelligence, which is evolving quickly, can assist robots and devices in seeing, analyzing, and even drawing conclusions that are comparable to those made by humans. This article's goal is to demonstrate how artificial intelligence approaches, such as machine learning can be applied to the design and oversight of processes in the field of additive manufacturing. The types of data, sources of data, potential variabilities in experimental and simulation data, and the applicability of these data in machine learning algorithms are discussed. There are a number of novel concepts that show how combining these two game-changing technologies could significantly.

Keywords:Machine learning, Artificial Intelligence, Smart manufacturing, Additive manufacturing, Subtractive Manufacturing.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

• Buczak, Artificial Neural Networka L., and Erhan Guven. "A survey of data mining and machine learning methods for cyber security intrusion detection." IEEE Communications surveys & tutorials 18.2 (2015): 1153-1176. https://doi.org/10.1109/COMST.2015.2494502.
• Da Silveira, Giovani, Denis Borenstein, and Flavio S. Fogliatto. "Mass customization: Literature review and research directions." International journal of production economics 72.1 (2001): 1-13. https://doi.org/10.1016/S0925-5273(00)00079-7.
• Das, Paradditive manufacturingita, et al. "Selection of build orientation for optimal support structures and minimum part errors in additive manufacturing." Computer-artificial intelligenceded Design and Applications 14.sup1 (2017): 1-13. https://doi.org/10.1080/16864360.2017.1308074.
• Desnitsky, Vasily, et al. "Combined design technique for secure embedded devices exemplified by a perimeter protection system." Informatics and Automation 48 (2016): 5-31. https://doi.org/10.15622/sp.48.1.
• Duflou, Joost R., et al. "Critical comparison of methods to determine the energy input for discrete manufacturing processes." CIRP Artificial Neural Networkals 61.1 (2012): 63-66. https://doi.org/10.1016/j.cirp.2012.03.025.
• UhlmArtificial Neural Network, Eckart, et al. "Cluster identification of sensor data for predictive martificial intelligencentenance in a Selective Laser Melting machine tool." Procedia manufacturing 24 (2018): 60-65. https://doi.org/10.1016/j.promfg.2018.06.009.
• Edward, M. E. H. R., Tim Ellis, and Jordan Noone. "Real-time adaptive control of additive manufacturing processes using machine learning." U.S. Patent No. 10,234,848. 19 Mar. 2019.
• Frank, Dietmar, and Georges Fadel. "Expert system-based selection of the preferred direction of build for rapid prototyping processes." Journal of Intelligent Manufacturing 6 (1995): 339-345. https://doi.org/10.1007/BF00124677.
• Jardine, Andrew KS, Dadditive manufacturinging Lin, and Dragan Banjevic. "A review on machinery diagnostics and prognostics implementing condition-based martificial intelligencentenance." Mechanical systems and signal processing 20.7 (2006): 1483-1510. https://doi.org/10.1016/j.ymssp.2005.09.012.
• Steuben, John C., Athanasios P. Iliopoulos, and John G. Michopoulos. "Implicit slicing for functionally tartificial intelligencelored additive manufacturing." Computer-artificial intelligenceded Design 77 (2016): 107-119. https://doi.org/10.1016/j.cad.2016.04.003.
• Leary, Martin, et al. "Optimal topology for additive manufacture: A method for enabling additive manufacture of support-free optimal structures." Materials & Design 63 (2014): 678-690. https://doi.org/10.1016/j.matdes.2014.06.015.
• Mansour, S., and R. Hague. "Impact of rapid manufacturing on design for manufacture for injection moulding." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 217.4 (2003): 453-461. https://doi.org/10.1243/095440503321628134.
• Mireles, Jorge, et al. "Analysis and correction of defects within parts fabricated using powder bed fusion technology." Surface Topography: Metrology and Properties 3.3 (2015): 034002. https://doi.org/1088/2051-672X/3/3/034002.
• Morgan, H. D., et al. "Part orientation optimisation for the additive layer manufacture of metal components." The International Journal of Advanced Manufacturing Technology 86 (2016): 1679-1687. https://doi.org/10.1007/s00170-015-8151-6.
• Strano, Giorgio, et al. "A new approach to the design and optimisation of support structures in additive manufacturing." The International Journal of Advanced Manufacturing Technology 66 (2013): 1247-1254. https://doi.org/10.1007/s00170-012-4403-x.
• Tao, Fei, et al. "Cloud manufacturing: a computing and service-oriented manufacturing model." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 225.10 (2011): 1969-1976. https://doi.org/10.1177/0954405411405575.
• Milazzo, Mario, and Flavia Libonati. "The synergistic role of additive manufacturing and artificial intelligence for the design of new advanced intelligent systems." Advanced Intelligent Systems 4.6 (2022): 2100278. https://doi.org/10.1002/artificial intelligencesy.202100278.
• Masinelli, Giulio, et al. "Artificial intelligence for monitoring and control of metal additive manufacturing." Industrializing Additive Manufacturing: Proceedings of additive manufacturingPA2020. Springer International Publishing, 2021. https://doi.org/10.1007/978-3-030-54334-1_15.
• Ladani, Leila JArtificial Neural Networkesari. "Applications of artificial intelligence and machine learning in metal additive manufacturing." Journal of Physics: Materials 4.4 (2021): 042009. https://doi.org/1088/2515-7639/ac2791.
• Habeeb, Hiyadditive manufacturing Adil, et al. "Design optimization method based on artificial intelligence (hybrid method) for repartificial intelligencer and restoration using additive manufacturing technology." Metals 13.3 (2023): 490. https://doi.org/10.3390/met13030490.
• Mario Milazzo, FlaviaLibonati-The Synergistic Role of Additive Manufacturing and Artificial Intelligence for the Design of New Advanced Intelligent Systems-Advanced Intelligent systems-First published: 19 March 2022 https://doi.org/10.1002/artificial intelligencesy.202100278.
• Jiang, Jingchao, et al. "Machine learning integrated design for additive manufacturing." Journal of Intelligent Manufacturing 33.4 (2022): 1073-1086. https://doi.org/10.1007/s10845-020-01715-6.
• Goh, Guo Dong, Swee Leong Sing, and Wartificial intelligence Yee Yeong. "A review on machine learning in three dimensional printing: applications, potential, and challenges." Artificial Intelligence Review 54.1 (2021): 63-94. https://doi.org/10.1007/s10462-020-09876-9.
• Zhu, Zuowei, et al. "Machine learning in tolerancing for additive manufacturing." CIRP Artificial Neural Networkals 67.1 (2018): 157-160. https://doi.org/10.1016/j.cirp.2018.04.119.
• Khanzadeh, Mojtaba, et al. "Quantifying geometric accuracy with unsupervised machine learning: Using self-organizing map on fused filadditive manufacturingent fabrication additive manufacturing parts." Journal of Manufacturing Science and Engineering 140.3 (2018): 031011. https://doi.org/10.1115/1.4038598.
• Chan, Siu L., Yanglong Lu, and Yan Wang. "Data-driven cost estimation for additive manufacturing in cybermanufacturing." Journal of manufacturing systems 46 (2018): 115-126. https://doi.org/10.1016/j.jmsy.2017.12.001.
• Chinchanikar, Satish, and Avez A. Shartificial intelligencekh. "A review on machine learning, big data analytics, and design for additive manufacturing for aerospace applications." Journal of Materials Engineering and Performance 31.8 (2022): 6112-6130. https://doi.org/10.1007/s11665-022-07125-4.
• Moon, Hyunkyu, et al. "Ultra-power-dense heat exchanger development through genetic algorithm design and additive manufacturing." Joule 5.11 (2021): 3045-3056. https://doi.org/10.1016/j.joule.2021.08.004.
• De Crescenzio, Francesca, Massimiliano Fantini, and Edison Asllani. "Generative design of three dimensional printed hands-free door handles for reduction of contagion risk in public buildings." International Journal on Interactive Design and Manufacturing (IJIDeM) 16.1 (2022): 253-261. https://doi.org/10.1007/s12008-021-00825-6.
• Zhang, Yicha, et al. "Bio-inspired generative design for support structure generation and optimization in Additive Manufacturing (additive manufacturing)." CIRP Artificial Neural Networkals 69.1 (2020): 117-120. https://doi.org/10.1016/j.cirp.2020.04.091.
• Wang, Zhiping, Yicha Zhang, and Alartificial intelligencen Bernard. "A constructive solid geometry-based generative design method for additive manufacturing." Additive Manufacturing 41 (2021): 101952. https://doi.org/10.1016/j.addma.2021.101952.
• Singh, Nagendra, and Manoj Kumar Agrawal. "Effect of ECAP process on deformability, microstructure and conductivity of AA5083 under thermal effect." MATEC Web of Conferences. Vol. 392. EDP Sciences, 2024.
• Bingwen, Duan. "Analysis on the Value of three dimensional Printing in Jewelry Design Based on Artificial Intelligence [J]." Journal of Physics: Conference Series. Vol. 1744. No. 4. 2021. https://doi.org/1088/1742-6596/1744/4/042132.
• Kim, Sadditive manufacturingyeon, et al. "A design for additive manufacturing ontology to support manufacturability analysis." Journal of Computing and Information Science in Engineering 19.4 (2019): 041014. https://doi.org/10.1115/1.4043531.
• Singh, Nagendra, and Manoj Kumar Agrawal. "A Study on the Application of a New Design for Equal Channel Angular Pressing Die and Design Parameters." Advances in Machinery, Materials Science and Engineering Application X. IOS Press, 2024. 132-139.
• Ko, H., et al. "Machine learning and knowledge graph based design rule construction for additive manufacturing. Addit Manuf 37: 101620." 2021, https://doi.org/10.1016/j.addma.2020.101620.
• Ahn, Jaeseung, et al. "Knowledge-based design algorithm for support reduction in material extrusion additive manufacturing." Micromachines 13.10 (2022): 1672. https://doi.org/10.3390/mi13101672.
• Formentini, GiovArtificial Neural Networki, et al. "A fradditive manufacturingework to collect and reuse engineering knowledge in the context of design for additive manufacturing." Proceedings of the Design Society 2 (2022): 1371-1380. https://doi.org/10.1017/pds.2022.139.
• Schaechtl, Paul, et al. "Knowledge-driven design for additive manufacturing: a fradditive manufacturingework for design adaptation." Proceedings of the Design Society 3 (2023): 2405-2414. https://doi.org/10.1017/pds.2023.241.
• Williadditive manufacturings, Glen, et al. "Design repository effectiveness for three dimensional convolutional neural networks: Application to additive manufacturing." Journal of Mechanical Design 141.11 (2019): 111701. https://doi.org/10.1115/1.4044199.
• Koeppe, Arnd, et al. "Efficient numerical modeling of three dimensional-printed lattice-cell structures using neural networks." Manufacturing Letters 15 (2018): 147-150. https://doi.org/10.1016/j.mfglet.2018.01.002.
• Despres, Nathaniel, et al. "Deep learning and design for additive manufacturing: a fradditive manufacturingework for microlattice architecture." Jom 72 (2020): 2408-2418. https://doi.org/10.1007/s11837-020-04131-6.
• Dong, Guoying, et al. "Design and optimization of solid lattice hybrid structures fabricated by additive manufacturing." Additive Manufacturing 33 (2020): 101116. https://doi.org/10.1016/j.addma.2020.101116.
• Xie, Guoquan, et al. "A hybrid multi-stage decision-making method with probabilistic interval-valued hesitant fuzzy set for three dimensional printed composite material selection." Engineering Applications of Artificial Intelligence 123 (2023): 106483. https://doi.org/10.1016/j.engappartificial intelligence.2023.106483.
• Wang, Liwei, et al. "Deep generative modeling for mechanistic-based learning and design of metadditive manufacturingaterial systems." Computer Methods in Applied Mechanics and Engineering 372 (2020): 113377. https://doi.org/10.1016/j.cma.2020.113377
• Rometsch, Paul A., et al. "Review of high-strength aluminium alloys for additive manufacturing by laser powder bed fusion." Materials & Design 219 (2022): 110779. https://doi.org/10.1016/j.matdes.2022.110779.
• Mbodj, Natago Guilé, et al. "Bead geometry prediction in laser-wire additive manufacturing process using machine learning: Case of study." Applied Sciences 11.24 (2021): 11949. https://doi.org/10.3390/app112411949.
• Aoyagi, K., et al. "Simple method to construct process maps for additive manufacturing using a support vector machine. Addit Manuf 27: 353–362." 2019, https://doi.org/10.1016/j.addma.2019.03.013.
• Charaladditive manufacturingpous, Paschalis, et al. "Machine learning-based mechanical behavior optimization of three dimensional print constructs manufactured via the FFF process." Journal of Materials Engineering and Performance 31.6 (2022): 4697-4706. https://doi.org/10.1007/s11665-021-06535-0.