A Review on Machine Learning, Big Data Analytics, and Design for Additive Manufacturing for Aerospace Applications
Source abstract
Abstract
Additive manufacturing (AM) has emerged as a promising technology to cater to the increasing demand for the fabrication of multi-functional, multi-material, and complex parts. AM is revolutionizing production and product development in the aerospace, automotive, and medical fields. However, mismatch in material properties, pervasive imperfections in the printed part, and lack of build consistency are crucial concerns. Higher accuracy in AM processes primarily depends on controlling various aspects of the process. In the last few years, machine learning, data analytics, and design for additive manufacturing have been the most extensively used techniques to address the vital concerns of additive manufacturing. Despite well-known techniques, very few studies reported applications of these techniques for aerospace. Specifically, this study comprehensively reviews recent advancements in the design for additive manufacturing (DfAM) and applications of machine learning and big data analytics to address the prime concerns of AM. The DfAM emphasizes issues and opportunities for topology optimization and methods for generative design for weight reduction and manufacturing of products with high resolution. Simulation and modeling techniques that are being used to improve geometric quality and process analysis are discussed to enable its potential for different applications. Further, automation of AM process using the Internet of things and knowledge-based systematic process planning is discussed to address key issues in process planning of multiple parts. Finally, the current challenges and scope for algorithmically driven AM processes are summarized with the trends of automation in AM to ensure greater efficiency and a better lifecycle of AM products in the era of industry 4.0.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Additive Manufacturing Materials and Processes · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
Show source-provided references
1. M.K. Thompson, G. Moroni, T. Vaneker, G. Fadel et al., Design for Additive Manufacturing: Trends, opportunities, Considerations, and Constraints, CIRP Ann. Manuf. Technol., 2016, 65, p 737–760.10.1016/j.cirp.2016.05.004 ↗
2. T.T. Wohlers, R.I. Campbell and T. Caffrey, 3D Printing and Additive Manufacturing State of the Industry: Annual Worldwide Progress Report, Wohlers Associates: Fort Collins, CO, USA, 2016.
3. J.C. Najmon, S. Raeisi and A. Tovar, Review of additive manufacturing technologies and applications in the aerospace industry, Additive Manufacturing for the Aerospace Industry. F. Froes, R. Boyer Ed., Elsevier Inc., Amsterdam, The Netherlands, 2019, p 7–3110.1016/B978-0-12-814062-8.00002-9 ↗
4. K. Ransikarbum, R. Leksomboon, Analytic Hierarchy Process Approach for Healthcare Educational Media Selection: Additive Manufacturing Inspired Study. In: 2021 IEEE 8th International Conference on Industrial Engineering and Applications (ICIEA), IEEE, 2021, p 154–15810.1109/ICIEA52957.2021.9436803 ↗
5. K. Ransikarbum, R. Leksomboon, C. Yingviwatanapong, T. Wajanavisit, N. Bijaphala, Additive manufacturing-based healthcare 3d model for education: literature review and a feasibility study. In: International Conference on Research, Invention, and Innovation Congress (RI2C), Bangkok, Thailand, IEEE, 201910.1109/RI2C48728.2019.8999943 ↗
6. P. Khamhong, C. Yingviwatanapong, K. Ransikarbum, Fuzzy analytic hierarchy process (FAHP)-based criteria analysis for 3d printer selection in additive manufacturing. In: International Conference on Research, Invention, and Innovation Congress (RI2C), Bangkok, Thailand, IEEE, 201910.1109/RI2C48728.2019.8999950 ↗
7. L. Meng, W. Zhang, D. Quan et al., From Topology Optimization Design to Additive Manufacturing: Today’s Success and Tomorrow’s Roadmap, Arch Computat Methods Eng., 2020, 27, p 805–830. https://doi.org/10.1007/s11831-019-09331-110.1007/s11831-019-09331-1 ↗
8. K. Ransikarbum, R. Pitakaso and N. Kim, A decision-support model for additive manufacturing scheduling using an integrative analytic hierarchy process and multi-objective optimization, Appl. Sci., 2020, 10(15), p 5159.10.3390/app10155159 ↗
9. K. Ransikarbum, S. Ha, J. Ma and N. Kim, Multi-Objective Optimization Analysis for Part-to-Printer Assignment in a Network of 3D Fused Deposition Modeling, J. Manuf. Syst., 2017, 43, p 35–46.10.1016/j.jmsy.2017.02.012 ↗
10. S.M. Yusuf, S. Cutler and N. Gao, Review: The Impact of Metal Additive Manufacturing on the Aerospace Industry, Metals, 2019, 9, p 1286. https://doi.org/10.3390/met912128610.3390/met9121286 ↗
11. S. Singamneni, L.V. Yifan, A. Hewitt, R. Chalk, W. Thomas et al., Additive Manufacturing for the Aircraft Industry: A Review, J Aeronaut Aerospace Eng., 2019, 8, p 214. https://doi.org/10.4172/2329-6542.100021410.4172/2329-6542.1000214 ↗
12. J.K. Liu, A.T. Gaynor, S.K. Chen et al., Current and future Trends in Topology Optimization for Additive Manufacturing, Struct Mutidiscip Optim, 2018, 57(6), p 2457–2483.10.1007/s00158-018-1994-3 ↗
13. F.H. Froes, R. Boyer and B. Dutta, Additive Manufacturing for Aerospace Applications-Part II, Adv. Mater. Processes, 2017, 175(6), p 18–23.
14. G. Shi et al., An Aerospace Bracket Designed by Thermo-Elastic Topology Optimization and Manufactured by Additive Manufacturing, Chin J Aeronaut, 2019 https://doi.org/10.1016/j.cja.2019.09.00610.1016/j.cja.2019.09.006 ↗
15. Y. Zhang, B. Peng, X. Zhou, C. Xiang, D. Wang, A Deep Convolutional Neural Network For Topology Optimization With Strong Generalization Ability. arXiv preprint arXiv:1901.07761 2019 Jan 23.
16. K. Ransikarbum, R. Pitakaso, N. Kim and J. Ma, Multi-Criteria Decision Analysis Framework for Part Orientation Analysis in Additive Manufacturing, J. Comput. Des. Eng., 2021, 8(4), p 1141–1157.
17. K. Ransikarbum and P. Khamhong, Integrated Fuzzy Analytic Hierarchy Process and Technique for Order of Preference by Similarity to Ideal Solution for Additive Manufacturing Printer Selection, J. Mater. Eng. Perform, 2021, 30(9), p 6481–6492.10.1007/s11665-021-05816-y ↗
18. S. Ha, K. Ransikarbum, H. Han, D. Kwon, N. Kim and H. Kim, A Dimensional Compensation Algorithm for Vertical Bending Deformation of 3D Printed Parts in Selective Laser Sintering, Rapid Prototyp J., 2018, 24(6), p 955–963.10.1108/RPJ-12-2016-0202 ↗
19. L.J. Love, B.K. Post, V. Kunc and A.C. Roschli, Commercialization of Big Area Additive Manufacturing. CRADA NFE-16-06083 Final Report (2020). https://doi.org/10.2172/160667610.2172/1606676 ↗
20. N. Aleisa, K. Renaud, Privacy of the Internet of Things: a systematic literature review (extended discussion). arXiv preprint arXiv:1611.03340. 2016 Sep 13.10.24251/HICSS.2017.717 ↗
21. R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction, 2nd ed. The MIT Press Cambridge, MA, USA, 2018, p 9780262039246
22. L. Meng, B. McWilliams, W. Jarosinski, H.Y. Park, Y.G. Jung, J. Lee and J. Zhang, Machine Learning in Additive Manufacturing: A Review, Jom., 2020, 72(6), p 2363–2377.10.1007/s11837-020-04155-y ↗
23. S. Hoyer, J. Sohl-Dickstein, S. Greydanus, Neural Reparameterization Improves Structural Optimization. arXiv preprint arXiv:1909.04240. 2019.
24. S. McBane and Y. Choi, Component-Wise Reduced Order Model Lattice-Type Structure Design, Comput. Methods Appl. Mech. Eng., 2021, 1(381), 113813.10.1016/j.cma.2021.113813 ↗
25. E. Ross, & D. Hambleton, Using Graph Neural Networks to Approximate Mechanical Response on 3D Lattice Structures, In: Proceedings of AAG2020-Advances in Architectural Geometry, 2021, 24, p 466-485
26. S. Chowdhury, & S. Anand, Artificial Neural Network Based Geometric Compensation for Thermal Deformation in Additive Manufacturing Processes. In: International Manufacturing Science and Engineering Conference American Society of Mechanical Engineers, 2016 49910, p V003T08A00610.1115/MSEC2016-8784 ↗
27. G.X. Gu, C.-T. Chen, D.J. Richmond and M.J. Buehler, Bioinspired Hierarchical Composite Design Using Machine Learning: Simulation, Additive Manufacturing, and Experiment, Mater. Horizons, 2018, 5(5), p 939–945. https://doi.org/10.1039/C8MH00653A10.1039/C8MH00653A ↗
28. X. Yao, S.K. Moon and G. Bi, A Hybrid Machine Learning Approach for Additive Manufacturing Design Feature Recommendation, Rapid Prototyp. J., 2017, 23(6), p 983–997. https://doi.org/10.1108/RPJ-03-2016-004110.1108/RPJ-03-2016-0041 ↗
29. S.L. Chan, Y. Lu and Y. Wang, Data-Driven Cost Estimation for Additive Manufacturing in Cybermanufacturing, J. Manuf. Syst., 2018, 46, p 115–126.10.1016/j.jmsy.2017.12.001 ↗
30. C. Wang, X.P. Tan, S.B. Tor and C.S. Lim, Machine Learning in Additive Manufacturing: State-of-the-Art and Perspectives, Addit. Manuf., 2020, 36, 101538.
31. F. Caiazzo and A. Caggiano, Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning, Materials, 2018, 11, p 444.10.3390/ma11030444 ↗
32. Y. Li, Y. Sun, Q. Han, G. Zhang and I. Horváth, Enhanced Beads Overlapping Model for Wire and Arc Additive Manufacturing of Multi-Layer Multi-Bead Metallic Parts, J. Mater. Process. Technol., 2018, 252, p 838–848.10.1016/j.jmatprotec.2017.10.017 ↗
33. M. Mozaffar, A. Paul, R. Al-Bahrani, S. Wolff, A. Choudhary, A. Agrawal, K. Ehmann and J. Cao, Data-Driven Prediction of the High-Dimensional Thermal History in Directed Energy Deposition Processes Via Recurrent Neural Networks, Manuf. Lett., 2018, 18, p 35–39.10.1016/j.mfglet.2018.10.002 ↗
34. C. Silbernagel, A. Aremu and I. Ashcroft, Using Machine Learning to Aid in the Parameter Optimisation Process for Metal-Based Additive Manufacturing, Rapid Prototyp. J., 2019, 26, p 625–637.10.1108/RPJ-08-2019-0213 ↗
35. B. Kappes, S. Moorthy, D. Drake, H. Geerlings, & A. Stebner, Machine Learning to Optimize Additive Manufacturing Parameters for Laser Powder Bed Fusion of Inconel 718, In: Proceedings of the 9th international symposium on superalloy 718 & derivatives Energy, aerospace, and industrial applications, Springer, Cham, 2018, p 595-61010.1007/978-3-319-89480-5_39 ↗
36. A. Garg and J.S.L. Lam, Measurement of Environmental Aspect of 3-D Printing Process Using Soft Computing Methods, Meas. J. Int. Meas. Confed., 2015, 75, p 210–217.10.1016/j.measurement.2015.04.016 ↗
37. A. Garg, J.S.L. Lam and M.M. Savalani, A New Computational Intelligence Approach in Formulation of Functional Relationship of Open Porosity of the Additive Manufacturing Process, Int. J. Adv. Manuf. Technol., 2015, 80, p 555–565.10.1007/s00170-015-6989-2 ↗
38. R. Liu, S. Liu and X. Zhang, A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing, Int. J. Adv. Manuf. Technol., 2021, 113, p 1943–1958.10.1007/s00170-021-06640-3 ↗
39. A. Fathi and A. Mozaffari, Vector Optimization of Laser Solid Freeform Fabrication System Using a Hierarchical Mutable Smart Bee-Fuzzy Inference System and Hybrid NSGA-II/Self-Organizing Map, J. Intell. Manuf., 2014, 25(4), p 775–795.10.1007/s10845-012-0718-6 ↗
40. Z. Yang, D. Eddy, S. Krishnamurty, I. Grosse, P. Denno, P.W. Witherell and F. Lopez, Dynamic Metamodeling for Predictive Analytics in Advanced Manufacturing, Smart Sustain. Manuf. Syst., 2018, 2(1), p 20170013. https://doi.org/10.1520/SSMS2017001310.1520/SSMS20170013 ↗
41. S. Ziegler, S. Struller, P. Scherwitz and G. Reinhart, Approach for a Production Control System Within Metal-Based Additive Manufacturing, Procedia CIRP, 2021, 99, p 39–44. https://doi.org/10.1016/j.procir.2021.03.00710.1016/j.procir.2021.03.007 ↗
42. X. Xiao and S. Joshi, Process Planning for Five-Axis Support Free Additive Manufacturing, Addit. Manuf., 2020, 36, 101569. https://doi.org/10.1016/j.addma.2020.10156910.1016/j.addma.2020.101569 ↗
43. N. Chen and M. Frank, Process Planning for Hybrid Additive and Subtractive Manufacturing to Integrate Machining and Directed Energy Deposition, Procedia Manuf., 2019, 34, p 205–213. https://doi.org/10.1016/j.promfg.2019.06.14010.1016/j.promfg.2019.06.140 ↗
44. L. Chen, T.Y. Lau and K. Tang, Manufacturability Analysis and Process Planning for Additive and Subtractive Hybrid Manufacturing of Quasi-Rotational Parts with Columnar Features, Comput. Aided Des., 2020, 118, 102759. https://doi.org/10.1016/j.cad.2019.10275910.1016/j.cad.2019.102759 ↗
45. A. Rossi and M. Lanzetta, Integration of Hybrid Additive/Subtractive Manufacturing Planning and Scheduling by Metaheuristics, Comput. Ind. Eng., 2020, 2020(144), 106428. https://doi.org/10.1016/j.cie.2020.10642810.1016/j.cie.2020.106428 ↗
46. W. Baumung and V.V. Fomin, Optimization Model to Extend Existing Production Planning and Control Systems for the Use of Additive Manufacturing Technologies in the Industrial Production, Procedia Manuf., 2018, 24, p 222–228. https://doi.org/10.1016/j.promfg.2018.06.03510.1016/j.promfg.2018.06.035 ↗
47. S.K. Everton, M. Hirsch, P. Stravroulakis, R.K. Leach and A.T. Clare, Review of In-Situ Process Monitoring and in-Situ Metrology for Metal Additive Manufacturing, Mater. Des., 2016, 95, p 431–445.10.1016/j.matdes.2016.01.099 ↗
48. F. Imani, A. Gaikwad, M. Montazeri, P. Rao, H. Yang, & E. Reutzel, . Layerwise in-Process Quality Monitoring in Laser Powder Bed Fusion. In: International Manufacturing Science and Engineering Conference, Vol 51357, American Society of Mechanical Engineers, 2018, p V001T01A03810.1115/MSEC2018-6477 ↗
49. L. Li, R. McGuan, R. Isaac, P. Kavehpour and R. Candler, Improving Precision of Material Extrusion 3D Printing by In-Situ Monitoring & Predicting 3D Geometric Deviation Using Conditional Adversarial Networks, Addit. Manuf., 2021, 38, 101695.
50. S.A. Shevchik, C. Kenel, C. Leinenbach and K. Wasmer, Acoustic Emission for In Situ Quality Monitoring in Additive Manufacturing Using Spectral Convolutional Neural Networks, Addit. Manuf., 2018, 21, p 598–604.
51. C. Liu, D. Roberson, & Z. Kong, (2017) Textural Analysis-Based Online Closed-Loop Quality Control for Additive Manufacturing Processes. In: IIE Annual Conference. Proceedings Institute of Industrial and Systems Engineers (IISE), pp 1127-1132
52. B. Yao, F. Imani and H. Yang, Markov Decision Process for Image-Guided Additive Manufacturing, IEEE Robot. Autom. Lett., 2018, 3(4), p 2792–2798.10.1109/LRA.2018.2839973 ↗
53. Qi. Tian, S. Guo, E. Melder, L. Bian and W. Guo, Deep Learning-Based Data Fusion Method for In Situ Porosity Detection in Laser-Based Additive Manufacturing, J. Manuf. Sci. Eng., 2021 https://doi.org/10.1115/1.404895710.1115/1.4048957 ↗
54. D. Rengasamy, H.P. Morvan and G.P. Figueredo, Deep Learning Approaches to Aircraft Maintenance, Repair and Overhaul: A Review, IEEE ITSC, 2018 https://doi.org/10.1109/ITSC.2018.856950210.1109/ITSC.2018.8569502 ↗
55. Z. Gao, C. Ma, D. Song and Y. Liu, Deep Quantum Inspired Neural Network with Application to Aircraft Fuel System Fault Diagnosis, Neurocomputing, 2017, 238, p 13–23. https://doi.org/10.1016/j.neucom.2017.01.03210.1016/j.neucom.2017.01.032 ↗
56. A.ElSaid, B. Wild, F.E. Jamiy, J. Higgins, &T. Desell Optimizing LSTM RNNs Using ACO to Predict Turbine Engine Vibration. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion 2017 p 21-2210.1145/3067695.3082045 ↗
57. X. Li et al., Remaining Useful Life Estimation in Prognostics Using Deep Convolution Neural Networks, Rel Eng & Sys Safety, 2017, 172, p 12.
58. M. Yuan et al., Fault Diagnosis and Remaining Useful Life Estimation of Aero Engine Using LSTM Neural Network, IEEE Int Conf on Aircraft Utility Systems, 2016, p 135–14010.1109/AUS.2016.7748035 ↗
59. Z. Gao, C. Ma, Y. Luo, RUL Prediction for IMA Based on Deep Regression Method, In: 2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA), 2017, p 25–3110.1109/IWCIA.2017.8203556 ↗
60. C. Zhang et al., Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics, IEEE Tran on Neural Nets Learn Syst., 2016, 1, p 1–13.
61. R. McCann, M.A. Obeidi, C. Hughes, E. McCarthy et al., In-Situ Sensing, Process Monitoring and Machine Control in Laser Powder Bed Fusion: A Review, Addit. Manuf., 2021, 1(45), 102058.
62. N. Senin and R. Leach, Information-Rich Surface Metrology, Procedia CIRP, 2018, 75, p 19–26. https://doi.org/10.1016/j.procir.2018.05.00310.1016/j.procir.2018.05.003 ↗
63. M. SamieTootooni, A. Dsouza, R. Donovan, P.K. Rao and Z.J.P. KongBorgesen, Classifying the Dimensional Variation in Additive Manufactured Parts From Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches, J. Manuf. Sci. Eng., 2017 https://doi.org/10.1115/1.403664110.1115/1.4036641 ↗
64. J. Liu, C. Liu, Y. Bai, P. Rao, C.B. Williams and Z. Kong, Layer-Wise Spatial Modeling of Porosity in Additive Manufacturing, IISE Trans, 2019, 51(2), p 109–123.10.1080/24725854.2018.1478169 ↗
65. S. Schmid, J. Krabusch, T. Schromm et al., A New Approach for Automated Measuring of the Melt Pool Geometry in Laser-Powder Bed Fusion, Prog. Addit. Manuf., 2021, 6, p 269–279. https://doi.org/10.1007/s40964-021-00173-710.1007/s40964-021-00173-7 ↗
66. J. Francis and L. Bian, Deep Learning for Distortion Prediction in Laser-Based Additive Manufacturing using Big Data, Manuf. Lett., 2019, 20, p 10–14. https://doi.org/10.1016/j.mfglet.2019.02.00110.1016/j.mfglet.2019.02.001 ↗
67. A. Yaseer and H. Chen, Machine Learning Based Layer Roughness Modeling in Robotic Additive Manufacturing, J. Manuf. Process., 2021, 1(70), p 543–552.10.1016/j.jmapro.2021.08.056 ↗
68. W. Raghupathi and V. Raghupathi, Big Data Analytics in Healthcare: Promise and Potential, Health Inf. Sci. Syst., 2014, 2, p 3. https://doi.org/10.1186/2047-2501-2-310.1186/2047-2501-2-3 ↗
69. J. Butt, Exploring the Interrelationship Between Additive Manufacturing and Industry 4.0, Designs, 2020, 4(2), p 13. https://doi.org/10.3390/designs402001310.3390/designs4020013 ↗
70. K. Bi, D. Lin, Y. Liao et al., Additive Manufacturing Embraces Big Data, Prog Addit Manuf, 2021, 6, p 181–197. https://doi.org/10.1007/s40964-021-00172-810.1007/s40964-021-00172-8 ↗
71. A. Majeed, Y. Zhang, S. Ren, J. Lv, T. Peng, S. Waqar and E. Yin, A Big Data-Driven Framework for Sustainable and Smart Additive Manufacturing, Robot. Computer-Integrated Manuf., 2021, 67, 102026. https://doi.org/10.1016/j.rcim.2020.10202610.1016/j.rcim.2020.102026 ↗
72. Y. Wang, P. Zheng, T. Peng, H. Yang and J. Zou, Smart Additive Manufacturing: Current Artificial Intelligence-Enabled Methods and Future Perspectives, Sci. China Technol. Sci., 2020, 63(9), p 1600–1611. https://doi.org/10.1007/s11431-020-1581-210.1007/s11431-020-1581-2 ↗
73. R. Ashima, A. Haleem, S. Bahl, M. Javaid, S.K. Mahla and S. Singh, Automation and Manufacturing of Smart Materials in Additive Manufacturing Technologies Using Internet of Things Towards the Adoption of Industry 4.0, Mater. Today Proc., 2021, 45, p 5081–5088. https://doi.org/10.1016/j.matpr.2021.01.58310.1016/j.matpr.2021.01.583 ↗
74. A. Kumar, Methods and Materials for Smart Manufacturing: Additive Manufacturing, Internet of Things, Flexible Sensors and Soft Robotics, Manuf. Lett., 2018, 15, p 122–125. https://doi.org/10.1016/j.mfglet.2017.12.01410.1016/j.mfglet.2017.12.014 ↗
75. R. Agrawal and S. Vinodh, Prioritizing Drivers of Industry 4.0 Enabling Additive Manufacturing: A Case Study, Industry 4.0 and Advanced Manufacturing: Proceedings of I-4AM 2019. A. Chakrabarti, M. Arora Ed., Springer Singapore, Singapore, 2021, p 25–34. https://doi.org/10.1007/978-981-15-5689-0_310.1007/978-981-15-5689-0_3 ↗
76. A.A. Shapiro, J.P. Borgonia, Q.N. Chen et al., Additive Manufacturing for Aerospace Flight Applications, J. Spacecraft Rockets, 2016, 53(5), p 952–959.10.2514/1.A33544 ↗
77. G. J. Schiller, Additive Manufacturing for Aerospace. In: 2015 IEEE Aerospace Conference, IEEE, 2015 p 1-810.1109/AERO.2015.7118958 ↗
78. F.H. Froes, R. Boyer and B. Dutta, Additive Manufacturing for Aerospace Applications-Part I, Adv. Mater. Proc., 2017, 175(5), p 36–41.
79. J.H. Zhu, W.H. Zhang and L. Xia, Topology Optimization in Aircraft and Aerospace Structures Design, Arch. Comput. Method E, 2016, 23(4), p 595–622. https://doi.org/10.1007/s11831-015-9151-210.1007/s11831-015-9151-2 ↗
80. M. Orme, I. Madera, M. Gschweitl and M. Ferrari, Topology Optimization for Additive Manufacturing as an Enabler for Light Weight Flight Hardware, Designs, 2018, 2, p 51. https://doi.org/10.3390/designs204005110.3390/designs2040051 ↗
81. M. Kamal and G. Rizza, Design for Metal Additive Manufacturing for Aerospace Applications, In Additive Manufacturing for the Aerospace Industry, Elsevier, Amsterdam, 2019, p 67–8610.1016/B978-0-12-814062-8.00005-4 ↗
82. E. P. Trudel, M. El. Sayed, L. Kok, & E. Provost, Multiscale Design Optimization of Additively Manufactured Aerospace Structures Employing Functionally Graded Lattice Materials, In: AIAA Scitech 2019 Forum, 2019, p 042010.2514/6.2019-0420 ↗
83. L. Berrocal, R. Fernández, S. González et al., Topology Optimization and Additive Manufacturing for Aerospace Components, Prog. Addit. Manuf., 2019, 4, p 83–95. https://doi.org/10.1007/s40964-018-0061-310.1007/s40964-018-0061-3 ↗
84. Y.S. Han, B. Xu, L. Zhao and Y.M. Xie, Topology Optimization of Continuum Structures Under Hybrid Additive-Subtractive Manufacturing Constraints, Struct. Multidiscip. Optim., 2019, 60(6), p 2571–2595. https://doi.org/10.1007/s00158-019-02334-310.1007/s00158-019-02334-3 ↗
85. Z.H. Jihong, Z.H. Han, W.A. Chuang, Z.H. Lu, Y.U. Shangqin and W. Zhang, A Review Of Topology Optimization for Additive Manufacturing: Status and Challenges, Chin. J. Aeronaut., 2021, 34(1), p 91–110.10.1016/j.cja.2020.09.020 ↗
86. M. Schnitger, An Introduction to Generative Design. Cadalyst. Longitude Media, 2018
87. S. A. Bagassi, F. Lucchi, F. De Crescenzio, & F. Persiani, Generative Design: Advanced Design Optimization Processes for Aeronautical Applications. In: Proceedings of the 30th Congress of the International Council of the Aeronautical Sciences, Daejeon, Korea, 2016, p 25-30
88. I. Ntintakis and G.E. Stavroulakis, Progress and Recent Trends in Generative Design, MATEC Web Conf., 2020, 318, p 01006. https://doi.org/10.1051/matecconf/20203180100610.1051/matecconf/202031801006 ↗
89. S.H.I. Guanghui, G.U.A.N. Chengqi, Q.U.A.N. Dongliang, W.U. Dongtao, T.A.N.G. Lei and G.A.O. Tong, An Aerospace Bracket Designed by Thermo-Elastic Topology Optimization and Manufactured by Additive Manufacturing, Chin. J. Aeronaut., 2020, 33(4), p 1252–1259.10.1016/j.cja.2019.09.006 ↗
90. T. Briard, F. Segonds and N. Zamariola, G-DfAM: A Methodological Proposal of Generative Design for Additive Manufacturing in the Automotive Industry, Int. J. Interact. Des. Manuf. (IJIDeM), 2020, 14(3), p 875–886.10.1007/s12008-020-00669-6 ↗
91. C.C. Tutum, S. Chockchowwat, E. Vouga, R. Miikkulainen, Functional Generative Design: an Evolutionary Approach to 3D-Printing. In: Proceedings of the Genetic and Evolutionary Computation Conference, 2018, p 1379–138610.1145/3205455.3205635 ↗
92. W.E. Frazier, Direct Digital Manufacturing of Metallic Components: Vision and Roadmap. In: 21st, international solid freeform fabrication symposium, University of Texas, Austin, 2010, p 717–732
93. A. Uriondo, M. Esperon-Miguez and S. Perinpanayagam, The Present and Future of Additive Manufacturing in the Aerospace Sector: A Review of Important Aspects, Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng., 2015, 229(11), p 2132–2147.10.1177/0954410014568797 ↗
94. G. Allevi, M. Cibeca, R. Fioretti, R. Marsili, R. Montanini and G. Rossi, Qualification of Additively Manufactured Aerospace Brackets: A Comparison Between Thermoelastic Stress Analysis and Theoretical Results, Measurement, 2018, 126, p 252–258. https://doi.org/10.1016/j.measurement.2018.05.06810.1016/j.measurement.2018.05.068 ↗
95. H. Hosseinzadeh, Metal Additive Manufacturing of Carbon Steel with Direct Laser Deposition: Computer Simulation, Prog. Addit. Manuf., 2021, 6, p 217–229. https://doi.org/10.1007/s40964-020-00160-410.1007/s40964-020-00160-4 ↗
96. E.A. Bonifaz and J.S. Palomeque, A Mechanical Model in Wire + Arc Additive Manufacturing Process, Prog. Addit. Manuf., 2020, 5, p 163–169. https://doi.org/10.1007/s40964-020-00112-y10.1007/s40964-020-00112-y ↗
97. S. Bhandari and R. Lopez-Anido, Finite Element Modeling of 3D-Printed Part with Cellular Internal Structure Using Homogenized Properties, Prog. Addit. Manuf., 2019, 4, p 143–154. https://doi.org/10.1007/s40964-018-0070-210.1007/s40964-018-0070-2 ↗
98. S.H. Jeong, E.G. Park, J.W. Kang et al., Thermal Analysis for Simulation of Metal Additive Manufacturing Process Considering Temperature-and History-Dependent Material Properties, Int. J. Aeronaut. Space Sci., 2021, 22, p 52–63. https://doi.org/10.1007/s42405-020-00283-610.1007/s42405-020-00283-6 ↗
99. T. Chen and Y.C. Wang, An Advanced Fuzzy Approach for Modeling the Yield Improvement of Making Aircraft Parts Using 3D Printing, Int. J. Adv. Manuf. Technol., 2019, 105, p 4085–4095. https://doi.org/10.1007/s00170-019-03295-z10.1007/s00170-019-03295-z ↗
100. T.I. Zohdi, Modeling and Simulation of Cooling-Induced Residual Stresses in Heated Particulate Mixture Depositions in Additive Manufacturing, Comput. Mech., 2015, 56, p 613–630. https://doi.org/10.1007/s00466-015-1191-910.1007/s00466-015-1191-9 ↗