Advancements in Digital Manufacturing for Metal 3D Printing
Received date: 2023-10-18
Revised date: 2023-11-21
Online published: 2023-12-27
Supported by
National Key Research and Development Program of China(2022YFB4600302);National Natural Science Foundation of China(52090041);National Natural Science Foundation of China(52104368)
Digital manufacturing revolutionizes conventional manufacturing processes into digital models, enabling intelligent control over the entire production process to rapidly create products tailored to specific requirements. Metal three-dimensional (3D) printing, a complex physical process, is characterized by strong multiphysics interactions, highly time-varying disturbances, intrinsic nonlinear relationships, and multiple variables and objectives. Achieving full-process digital control in metal 3D printing has the potential to overcome current bottlenecks, such as inconsistent part quality and unstable performance, thereby advancing high-quality 3D printing technology. This work investigates metal 3D printing characteristics and fundamental digital manufacturing principles. It subsequently provides an overview of research progress in the digital manufacturing of metal 3D printing, encompassing three critical aspects: online monitoring of the 3D printing process, digital simulation, and the interaction between physical and information systems. Finally, the work discusses the future research focus of digital manufacturing in metal 3D printing, offering insights into its development prospects.
LIU Zhuangzhuang , DING Minglu , XIE Jianxin . Advancements in Digital Manufacturing for Metal 3D Printing[J]. Acta Metall Sin, 2024 , 60(5) : 569 -584 . DOI: 10.11900/0412.1961.2023.00416
| 1 | Zhou J. Intelligent manufacturing—Main direction of "made in China 2025"[J]. China Mech. Eng., 2015, 26: 2273 |
| 周 济. 智能制造——"中国制造2025"的主攻方向[J]. 中国机械工程, 2015, 26: 2273 | |
| 2 | Kolade O, Owoseni A. Employment 5.0: The work of the future and the future of work[J]. Technol. Soc., 2022, 71: 102086 |
| 3 | Zhou J, Zhou Y H, Wang B C, et al. Human-cyber-physical systems (HCPSs) in the context of new-generation intelligent manufacturing[J]. Engineering, 2019, 5: 624 |
| 4 | Lu Y Q, Zheng H, Chand S, et al. Outlook on human-centric manufacturing towards Industry 5.0[J]. J. Manuf. Syst., 2022, 62: 612 |
| 5 | Wang B C, Tao F, Fang X D, et al. Smart manufacturing and intelligent manufacturing: A comparative review[J]. Engineering, 2021, 7: 738 |
| 6 | Zhou J, Li P G, Zhou Y H, et al. Toward new-generation intelligent manufacturing[J]. Engineering, 2018, 4: 11 |
| 7 | Zang J Y, Wang B C, Meng L, et al. Brief analysis on three basic paradigms of intelligent manufacturing[J]. Strategic Study CAE, 2018, 20(4): 13 |
| 臧冀原, 王柏村, 孟 柳 等. 智能制造的三个基本范式: 从数字化制造, “互联网+”制造到新一代智能制造[J]. 中国工程科学, 2018, 20(4): 13 | |
| 8 | Osterrieder P, Budde L, Friedli T. The smart factory as a key construct of industry 4.0: A systematic literature review[J]. Int. J. Prod. Econ., 2020, 221: 107476 |
| 9 | Tao F, Qi Q L. New IT driven service-oriented smart manufacturing: Framework and characteristics[J]. IEEE Trans. Syst. Man. Cybern. Syst., 2019, 49: 81 |
| 10 | Tao F, Cheng J F, Qi Q L, et al. Digital twin-driven product design, manufacturing and service with big data[J]. Int. J. Adv. Manuf. Technol., 2017, 94: 3563 |
| 11 | Tao F, Zhang M, Cheng J F, et al. Digital twin workshop: A new paradigm for future workshop[J]. Comput. Integr. Manuf. Syst., 2017, 23: 1 |
| 陶 飞, 张 萌, 程江峰 等. 数字孪生车间——一种未来车间运行新模式[J]. 计算机集成制造系统, 2017, 23: 1 | |
| 12 | Liu R H, Zhang N, Wu Y F. On thinking of constructing an industrial internet security protection system[J]. Cyber Secur. Data Governance, 2018, 37(1): 23 |
| 刘仁辉, 张 尼, 吴云峰. 构筑工业互联网安全防护体系 为推动先进制造业发展保驾护航[J]. 信息技术与网络安全, 2018, 37(1): 23 | |
| 13 | Chowdhury S, Yadaiah N, Prakash C, et al. Laser powder bed fusion: A state-of-the-art review of the technology, materials, properties & defects, and numerical modelling[J]. J. Mater. Res. Technol., 2022, 20: 2109 |
| 14 | Tofail S A M, Koumoulos E P, Bandyopadhyay A, et al. Additive manufacturing: Scientific and technological challenges, market uptake and opportunities[J]. Mater Today, 2018, 21: 22 |
| 15 | DebRoy T, Wei H L, Zuback J S, et al. Additive manufacturing of metallic components—Process, structure and properties[J]. Prog. Mater. Sci., 2018, 92: 112 |
| 16 | Hassanin H, Finet L, Cox S C, et al. Tailoring selective laser melting process for titanium drug-delivering implants with releasing micro-channels[J]. Addit. Manuf., 2018, 20: 144 |
| 17 | Pham M S, Dovgyy B, Hooper P A. Twinning induced plasticity in austenitic stainless steel 316L made by additive manufacturing[J]. Mater. Sci. Eng., 2017, A704: 102 |
| 18 | Uriondo A, Esperon-Miguez M, Perinpanayagam S. The present and future of additive manufacturing in the aerospace sector: A review of important aspects[J]. Proc. Inst. Mech. Eng, 2015, 229G: 2132 |
| 19 | Gao W, Zhang Y B, Ramanujan D, et al. The status, challenges, and future of additive manufacturing in engineering[J]. Comput. Aided Des., 2015, 69: 65 |
| 20 | Sun K, Wei T S, Ahn B Y, et al. 3D printing of interdigitated Li-ion microbattery architectures[J]. Adv. Mater., 2013, 25: 4539 |
| 21 | Guo N N, Leu M C. Additive manufacturing: Technology, applications and research needs[J]. Front. Mech. Eng., 2013, 8: 215 |
| 22 | Bartolo P, Kruth J P, Silva J, et al. Biomedical production of implants by additive electro-chemical and physical processes[J]. CIRP Ann., 2012, 61: 635 |
| 23 | Wei C, Li L. Recent progress and scientific challenges in multi-material additive manufacturing via laser-based powder bed fusion[J]. Virtual Phys. Prototyp., 2021, 16: 347 |
| 24 | Torre M, Giannitelli S M, Mauri E, et al. Chapter seven—Additive manufacturing of biomaterials[J]. Adv. Chem. Eng., 2021, 57: 233 |
| 25 | Li N, Liu W, Wang Y, et al. Laser additive manufacturing on metal matrix composites: A review[J]. Chin. J. Mech. Eng., 2021, 34: 38 |
| 26 | Sanjeeviprakash K, Kannan A R, Shanmugam N S. Additive manufacturing of metal-based functionally graded materials: Overview, recent advancements and challenges[J]. J. Braz. Soc. Mech. Sci. Eng., 2023, 45: 241 |
| 27 | Goetz I K, Kaplan M, Hans M, et al. Reactive metal additive manufacturing: Surface near ZrN-metallic glass composite formation and mechanical properties[J]. Addit. Manuf., 2023, 66: 103457 |
| 28 | Guerra C, Ramos-Grez J A, La Fé-Perdomo I, et al. Microstructure and mechanical properties of Cu-11Al-5Ni-4Fe wt% manufactured by LPBF[J]. Metals, 2023, 13: 459 |
| 29 | Chen Y, Jian Z Y, Ren Y M, et al. Influence of TiB2 volume fraction on SiCp/AlSi10Mg composites by LPBF: Microstructure, mechanical, and physical properties[J]. J. Mater. Res. Technol., 2023, 23: 3697 |
| 30 | Li C X, Liu Y, Shu T, et al. Effect of solution heat treatment on microstructure, mechanical and electrochemical properties of hastelloy X fabricated by laser powder bed fusion[J]. J. Mater. Res. Technol., 2023, 24: 1499 |
| 31 | Yang K, Yang X, Zhang Z L, et al. Spall damage in laser-powder-bed-fusion manufactured Ti-6Al-4V: Mechanisms and microstructure effects[J]. J. Alloys Compd., 2023, 947: 169379 |
| 32 | Liu B, Ding Y T, Xu J Y, et al. Outstanding strength-ductility synergy in Inconel 718 superalloy via laser powder bed fusion and thermomechanical treatment[J]. Addit. Manuf., 2023, 67: 103491 |
| 33 | Guan J R, Wang Q P. Laser powder bed fusion of dissimilar metal materials: A review[J]. Materials, 2023, 16: 2757 |
| 34 | Zhang H B, Li J S, Li Y Q. Effect of powder recycling on the organization and mechanical properties of GH4169 alloy by laser metal deposition[J]. Coatings, 2023, 13: 659 |
| 35 | Wu H L, Huang S H, Li Z S, et al. Optimization of microstructure and properties in U75V steel rail cladding layers manufactured by laser melting deposition and laser shock peening[J]. Opt. Laser Technol., 2023, 163: 109436 |
| 36 | Preu? B, Lindner T, Uhlig T, et al. Microstructure evolution and wear resistance of the eutectic high-entropy alloy Al0.3CoCrFeNiNb0.5 produced by laser metal deposition[J]. Coatings, 2023, 13: 585 |
| 37 | Piscopo G, Atzeni E, Saboori A, et al. An overview of the process mechanisms in the laser powder directed energy deposition[J]. Appl. Sci., 2023, 13: 117 |
| 38 | Ostolaza M, Arrizubieta J I, Lamikiz A, et al. Latest developments to manufacture metal matrix composites and functionally graded materials through AM: A state-of-the-art review[J]. Materials, 2023, 16: 1746 |
| 39 | Bikas H, Stavropoulos P, Chryssolouris G. Additive manufacturing methods and modelling approaches: A critical review[J]. Int. J. Adv. Manuf. Technol., 2016, 83: 389 |
| 40 | Moges T, Ameta G, Witherell P. A review of model inaccuracy and parameter uncertainty in laser powder bed fusion models and simulations[J]. J. Manuf. Sci. Eng., 2019, 141: 040801 |
| 41 | Razvi S S, Feng S, Narayanan A, et al. A review of machine learning applications in additive manufacturing[A]. Proceedings of ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 1[C]. Anaheim: ASME, 2019, doi: 10.1115/DETC2019-98145 |
| 42 | Young Z A, Guo Q L, Parab N D, et al. Types of spatter and their features and formation mechanisms in laser powder bed fusion additive manufacturing process[J]. Addit. Manuf., 2020, 36: 101438 |
| 43 | Bahrin M A, Othman M F, Azli N H N, et al. Industry 4.0: A review on industrial automation and robotic[J]. J. Teknol., 2016, 78: 137 |
| 44 | Haddara M, Elragal A. The readiness of ERP systems for the factory of the future[J]. Procedia Comput. Sci., 2015, 64: 721 |
| 45 | Okano M T. IOT and industry 4.0: the industrial new revolution[A]. International Conference on Management and Information Systems[C]. Bangkok: ICMIS, 2017: 75 |
| 46 | Park J, Bae H. Big data and AI for process innovation in the Industry 4.0 era[J]. Appl. Sci., 2022, 12: 6346 |
| 47 | Mourtzis D, Angelopoulos J, Panopoulos N. Smart manufacturing and tactile internet based on 5G in Industry 4.0: Challenges, applications and new trends[J]. Electronics, 2021, 10: 3175 |
| 48 | Scime L, Beuth J. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm[J]. Addit. Manuf., 2018, 19: 114 |
| 49 | Sillani F, MacDonald E, Villela J, et al. In-situ monitoring of powder bed fusion of polymers using laser profilometry[J]. Addit. Manuf., 2022, 59: 103074 |
| 50 | Bartlett J L, Heim F M, Murty Y V, et al. In situ defect detection in selective laser melting via full-field infrared thermography[J]. Addit. Manuf., 2018, 24: 595 |
| 51 | Furumoto T, Ueda T, Alkahari M R, et al. Investigation of laser consolidation process for metal powder by two-color pyrometer and high-speed video camera[J]. CIRP Ann., 2013, 62: 223 |
| 52 | Craeghs T, Bechmann F, Berumen S, et al. Feedback control of layerwise laser melting using optical sensors[J]. Phys. Procedia, 2010, 5: 505 |
| 53 | Ito K, Kusano M, Demura M, et al. Detection and location of microdefects during selective laser melting by wireless acoustic emission measurement[J]. Addit. Manuf., 2021, 40: 101915 |
| 54 | Ren Z S, Gao L, Clark S J, et al. Machine learning-aided real-time detection of keyhole pore generation in laser powder bed fusion[J]. Science, 2023, 379: 89 |
| 55 | Song L J, Huang W K, Han X, et al. Real-time composition monitoring using support vector regression of laser-induced plasma for laser additive manufacturing[J]. IEEE Trans. Ind. Electron., 2017, 64: 633 |
| 56 | Farshidianfar M H, Khajepour A, Gerlich A. Real-time control of microstructure in laser additive manufacturing[J]. Int. J. Adv. Manuf. Technol., 2016, 82: 1173 |
| 57 | Huang Y Z, Ansari M, Asgari H, et al. Rapid prediction of real-time thermal characteristics, solidification parameters and microstructure in laser directed energy deposition (powder-fed additive manufacturing)[J]. J. Mater. Process. Technol., 2019, 274: 116286 |
| 58 | DebRoy T, Mukherjee T, Wei H L, et al. Metallurgy, mechanistic models and machine learning in metal printing[J]. Nat. Rev. Mater., 2021, 6: 48 |
| 59 | Chen H, Zhang Y J, Giam A, et al. Experimental and computational study on thermal and fluid behaviours of powder layer during selective laser melting additive manufacturing[J]. Addit. Manuf., 2022, 52: 102645 |
| 60 | Mukherjee T, Wei H L, De A, et al. Heat and fluid flow in additive manufacturing—Part I: Modeling of powder bed fusion[J]. Comput. Mater. Sci., 2018, 150: 304 |
| 61 | Mukherjee T, Wei H L, De A, et al. Heat and fluid flow in additive manufacturing—Part II: Powder bed fusion of stainless steel, and titanium, nickel and aluminum base alloys[J]. Comput. Mater. Sci., 2018, 150: 369 |
| 62 | Wei H L, Elmer J W, Debroy T. Three-dimensional modeling of grain structure evolution during welding of an aluminum alloy[J]. Acta Mater., 2017, 126: 413 |
| 63 | Zhang Z, Tan Z J, Yao X X, et al. Numerical methods for microstructural evolutions in laser additive manufacturing[J]. Comput. Math. Appl., 2019, 78: 2296 |
| 64 | Li X X, Tan W D. Numerical investigation of effects of nucleation mechanisms on grain structure in metal additive manufacturing[J]. Comput. Mater. Sci., 2018, 153: 159 |
| 65 | Mukherjee T, DebRoy T. Printability of 316 stainless steel[J]. Sci. Technol. Weld. Joining, 2019, 24: 412 |
| 66 | Sch?nzel M, Shakirov D, Ilin A, et al. Coupled thermo-mechanical process simulation method for selective laser melting considering phase transformation steels[J]. Comput. Math. Appl., 2019, 78: 2230 |
| 67 | Chen M M, Shi R H, Liu Z Z, et al. Phase-field simulation of lack-of-fusion defect and grain growth during laser powder bed fusion of Inconel 718[J]. Int. J. Miner. Metall. Mater., 2023, 30: 2224 |
| 68 | Yang Y, Knol M F, van Keulen F, et al. A semi-analytical thermal modelling approach for selective laser melting[J]. Addit. Manuf., 2018, 21: 284 |
| 69 | Yang Y B, Zhou X, Li Q, et al. A computationally efficient thermo-mechanical model for wire arc additive manufacturing[J]. Addit. Manuf., 2021, 46: 102090 |
| 70 | Yang Y, van Keulen F, Ayas C. A computationally efficient thermal model for selective laser melting[J]. Addit. Manuf., 2020, 31: 100955 |
| 71 | Kusano M, Miyazaki S, Watanabe M, et al. Tensile properties prediction by multiple linear regression analysis for selective laser melted and post heat-treated Ti-6Al-4V with microstructural quantification[J]. Mater. Sci. Eng., 2020, 787A: 139549 |
| 72 | Hertlein N, Deshpande S, Venugopal V, et al. Prediction of selective laser melting part quality using hybrid Bayesian network[J]. Addit. Manuf., 2020, 32: 101089 |
| 73 | Zhang J J, Wang P, Gao R X. Deep learning-based tensile strength prediction in fused deposition modeling[J]. Comput. Ind., 2019, 107: 11 |
| 74 | Raj A, Owen C, Stegman B, et al. Predicting mechanical properties from co-axial melt pool monitoring signals in laser powder bed fusion[J]. J. Manuf. Processes, 2023, 101: 181 |
| 75 | Estalaki S M, Lough C S, Landers R G, et al. Predicting defects in laser powder bed fusion using in-situ thermal imaging data and machine learning[J]. Addit. Manuf., 2022, 58: 103008 |
| 76 | Sah A K, Agilan M, Dineshraj S, et al. Machine learning-enabled prediction of density and defects in additively manufactured Inconel 718 alloy[J]. Mater Today Commun, 2022, 30: 103193 |
| 77 | Smoqi Z, Gaikwad A, Bevans B, et al. Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning[J]. J. Mater. Process. Technol., 2022, 304: 117550 |
| 78 | Mondal B, Mukherjee T, DebRoy T. Crack free metal printing using physics informed machine learning[J]. Acta Mater., 2022, 226: 117612 |
| 79 | Liu Q, Wu H K, Paul M J, et al. Machine-learning assisted laser powder bed fusion process optimization for AlSi10Mg: New microstructure description indices and fracture mechanisms[J]. Acta Mater., 2020, 201: 316 |
| 80 | Dastjerdi A A, Movahhedy M R, Akbari J. Optimization of process parameters for reducing warpage in selected laser sintering of polymer parts[J]. Addit. Manuf., 2017, 18: 285 |
| 81 | Yu T Y, Mo X D, Chen M J, et al. Machine-learning-assisted microstructure-property linkages of carbon nanotube-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion[J]. Nanotechnol. Rev., 2021, 10: 1410 |
| 82 | Gan Z T, Li H Y, Wolff S J, et al. Data-driven microstructure and microhardness design in additive manufacturing using a self-organizing map[J]. Engineering, 2019, 5: 730 |
| 83 | Silbernagel C, Aremu A, Ashcroft I. Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing[J]. Rapid Prototyp. J., 2020, 26: 625 |
| 84 | Wang R X, Standfield B, Dou C R, et al. Real-time process monitoring and closed-loop control on laser power via a customized laser powder bed fusion platform[J]. Addit. Manuf., 2023, 66: 103449 |
| 85 | Tamir T S, Xiong G, Fang Q H, et al. Machine-learning-based monitoring and optimization of processing parameters in 3D printing[J]. Int. J. Comput. Integr. Manuf., 2023, 36: 1362 |
| 86 | Koren O, Shamalov A, Perel N. Small files problem resolution via hierarchical clustering algorithm[J]. Big Data, 2023, doi: 10.1089/big.2022.0181 |
| 87 | Kaseb M R, Khafagy M H, Ali I A, et al. An improved technique for increasing availability in big data replication[J]. Future Gener. Comput. Syst., 2019, 91: 493 |
| 88 | Farkas Z, Kacsuk P, Hajnal A. Connecting workflow-oriented science gateways to multi-cloud systems[A]. 7th International Workshop on Science Gateways[C]. Budapest: IEEE, 2015: 40 |
| 89 | Cheng W H, Chiang C I, Yang C T, et al. The Implementation of supporting uniform data distribution with software-dened storage service on heterogeneous cloud storage[A]. 2016 IEEE 22nd International Conference on Parallel and Distributed Systems[C]. Wuhan: IEEE, 2016: 610 |
| 90 | Ou Z H, Hwang Z H, Chen F, et al. Is cloud storage ready? A comprehensive study of IP-based storage systems[A]. 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing[C]. Limassol: IEEE, 2015: 1 |
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