Parts design and process optimization

Book chapter


Hassanin, Hany, Bidare, Prveen, Zweiri, Yahya and Essa, Khamis 2021. Parts design and process optimization. in: Salunkhe, S., Hussein, H. and Davim, J. (ed.) Applications of Artificial Intelligence in Additive Manufacturing USA IGI Global. pp. 25-49
AuthorsHassanin, Hany, Bidare, Prveen, Zweiri, Yahya and Essa, Khamis
EditorsSalunkhe, S., Hussein, H. and Davim, J.
AbstractArtificial intelligence and additive manufacturing are primary drivers of Industry 4.0, which is reshaping the manufacturing industry. Based on the progressive layer-by-layer principle, additive manufacturing allows for the manufacturing of mechanical parts with a high degree of complexity. In this chapter, a deep learning neural network (DLNN) is introduced to rationalize the effect of cellular structure design factors as well as process variables on physical and mechanical properties utilizing laser powder bed fusion. The models developed were validated and utilized to create process maps. For both design and process optimization, the trained deep learning neural network model showed the highest accuracy. Deep learning neural networks were found to be an effective technique for predicting material properties from limited data sets, as per the findings.
KeywordsDeep learning; Additive manufacturing; Porosity; Powder bed fusion
Page range25-49
Year2021
Book titleApplications of Artificial Intelligence in Additive Manufacturing
PublisherIGI Global
Output statusPublished
Place of publicationUSA
ISBN9781799885160
ISSN2327-0411
2327-042X
Publication dates
PrintDec 2021
Publication process dates
AcceptedNov 2021
Deposited13 Dec 2021
Digital Object Identifier (DOI)https://doi.org/10.4018/978-1-7998-8516-0.ch002
Official URLhttps://www.igi-global.com/chapter/parts-design-and-process-optimization/294047
Related URLhttps://www.igi-global.com/book/applications-artificial-intelligence-additive-manufacturing/271276
References

1. Ge, W.; Lin, F.; Guo, C. Effect of energy input on microstructure and mechanical properties in EBSM Ti6Al4V. Mater. Manuf. Process. 2018, 33, 1708–1713, doi:10.1080/10426914.2015.1048463.
2. Davidson, K.; Singamneni, S. Selective Laser Melting of Duplex Stainless Steel Powders: An Investigation. Mater. Manuf. Process. 2016, 31, 1543–1555, doi:10.1080/10426914.2015.1090605.
3. Scherillo, F.; Astarita, A.; Carrino, L.; Pirozzi, C.; Prisco, U.; Squillace, A. Linear friction welding of Ti-6Al-4V parts produced by electron beam melting. Mater. Manuf. Process. 2019, 34, 201–207, doi:10.1080/10426914.2018.1532086.
4. Kayacan, M.Y.; Özsoy, K.; Duman, B.; Yilmaz, N.; Kayacan, M.C. A study on elimination of failures resulting from layering and internal stresses in Powder Bed Fusion (PBF) additive manufacturing. Mater. Manuf. Process. 2019, 34, 1467–1475, doi:10.1080/10426914.2019.1655151.
5. Hassanin, H.; Modica, F.; El-Sayed, M.A.; Liu, J.; Essa, K. Manufacturing of Ti–6Al–4V Micro-Implantable Parts Using Hybrid Selective Laser Melting and Micro-Electrical Discharge Machining. Adv. Eng. Mater. 2016, 18, 1544–1549, doi:10.1002/adem.201600172.
6. Langford, T.; Mohammed, A.; Essa, K.; Elshaer, A.; Hassanin, H. 4D Printing of Origami Structures for Minimally Invasive Surgeries Using Functional Scaffold. Appl. Sci. 2021, 11, 332.
7. Brambilla, C.R.M.; Okafor-Muo, O.L.; Hassanin, H.; ElShaer, A. 3DP Printing of Oral Solid Formulations: A Systematic Review. Pharmaceutics 2021, 13, 358.
8. Okafor-Muo, O.L.; Hassanin, H.; Kayyali, R.; ElShaer, A. 3D Printing of Solid Oral Dosage Forms: Numerous Challenges With Unique Opportunities. J. Pharm. Sci. 2020, 109, 3535–3550, doi:10.1016/j.xphs.2020.08.029.
9. Hassanin, H.; Alkendi, Y.; Elsayed, M.; Essa, K.; Zweiri, Y. Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches. Adv. Eng. Mater. 2020, 22, 1901338, doi:10.1002/adem.201901338.
10. Klippstein, H.; Hassanin, H.; Diaz De Cerio Sanchez, A.; Zweiri, Y.; Seneviratne, L. Additive Manufacturing of Porous Structures for Unmanned Aerial Vehicles Applications. Adv. Eng. Mater. 2018, 20, 1800290, doi:10.1002/adem.201800290.
11. Galatas, A.; Hassanin, H.; Zweiri, Y.; Seneviratne, L. Additive Manufactured Sandwich Composite/ABS Parts for Unmanned Aerial Vehicle Applications. Polymers 2018, 10, 1262.
12. Hassanin, H.; Abena, A.; Elsayed, M.A.; Essa, K. 4D Printing of NiTi Auxetic Structure with Improved Ballistic Performance. Micromachines 2020, 11, 745.
13. Schmitt, M.; Mehta, R.M.; Kim, I.Y. Additive manufacturing infill optimization for automotive 3D-printed ABS components. Rapid Prototyp. J. 2020, 26, 89–99, doi:10.1108/RPJ-01-2019-0007.
14. Mohammed, A.; Elshaer, A.; Sareh, P.; Elsayed, M.; Hassanin, H. Additive Manufacturing Technologies for Drug Delivery Applications. Int. J. Pharm. 2020, 580, 119245, doi:10.1016/j.ijpharm.2020.119245.
15. Hassanin, H.; Jiang, K. Optimized process for the fabrication of zirconia micro parts. Microelectron. Eng. 2010, 87, 1617–1619, doi:10.1016/j.mee.2009.10.037.
16. Essa, K.; Hassanin, H.; Attallah, M.M.; Adkins, N.J.; Musker, A.J.; Roberts, G.T.; Tenev, N.; Smith, M. Development and testing of an additively manufactured monolithic catalyst bed for HTP thruster applications. Appl. Catal. A: Gen. 2017, 542, 125–135, doi:10.1016/j.apcata.2017.05.019.
17. El-Sayed, M.A.; Hassanin, H.; Essa, K. Effect of casting practice on the reliability of Al cast alloys. Int. J. Cast Met. Res. 2016, 29, 350–354, doi:10.1080/13640461.2016.1145966.
18. Jiménez, A.; Bidare, P.; Hassanin, H.; Tarlochan, F.; Dimov, S.; Essa, K. Powder-based laser hybrid additive manufacturing of metals: a review. Int. J. Adv. Manuf. Technol. 2021, 10.1007/s00170-021-06855-4, doi:10.1007/s00170-021-06855-4.
19. Essa, K.; Khan, R.; Hassanin, H.; Attallah, M.M.; Reed, R. An iterative approach of hot isostatic pressing tooling design for net-shape IN718 superalloy parts. Int. J. Adv. Manuf. Technol. 2016, 83, 1835–1845, doi:10.1007/s00170-015-7603-3.
20. Sabouri, A.; Yetisen, A.K.; Sadigzade, R.; Hassanin, H.; Essa, K.; Butt, H. Three-Dimensional Microstructured Lattices for Oil Sensing. Energy Fuels 2017, 31, 2524–2529, doi:10.1021/acs.energyfuels.6b02850.
21. Olson, G.B. Computational Design of Hierarchically Structured Materials. Science 1997, 277, 1237–1242, doi:10.1126/science.277.5330.1237.
22. Belhocine, A.; Afzal, A. Computational finite element analysis of brake disc rotors employing different materials. Aust. J. Mech. Eng. 2020, 10.1080/14484846.2020.1733175, 1-14, doi:10.1080/14484846.2020.1733175.
23. Yuan, B.; Guss, G.M.; Wilson, A.C.; Hau-Riege, S.P.; DePond, P.J.; McMains, S.; Matthews, M.J.; Giera, B. Machine-Learning-Based Monitoring of Laser Powder Bed Fusion. Adv. Mater. Technol. 2018, 3, 1800136, doi:10.1002/admt.201800136.
24. Weichert, D.; Link, P.; Stoll, A.; Rüping, S.; Ihlenfeldt, S.; Wrobel, S. A review of machine learning for the optimization of production processes. Int. J. Adv. Manuf. Technol. 2019, 10.1007/s00170-019-03988-5, doi:10.1007/s00170-019-03988-5.
25. Yang, J.; Li, S.; Wang, Z.; Dong, H.; Wang, J.; Tang, S. Using deep learning to detect defects in manufacturing: A comprehensive survey and current challenges. Materials 2020, 13, 1–23, doi:10.3390/ma13245755.
26. Bengio, Y.; Lamblin, P.; Popovici, D.; Larochelle, H. Greedy layer-wise training of deep networks. In Proceedings of the 19th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada, 4–7 December 2006; pp. 153-160.
27. Hinton, G.E.; Salakhutdinov, R.R. Reducing the Dimensionality of Data with Neural Networks. Science 2006, 313, 504–507, doi:10.1126/science.1127647.
28. Hinton, G.E.; Osindero, S.; Teh, Y.-W. A Fast Learning Algorithm for Deep Belief Nets. Neural Comput. 2006, 18, 1527–1554, doi:10.1162/neco.2006.18.7.1527 %M 16764513.
29. Azzam, R.; Taha, T.; Huang, S.; Zweiri, Y. A deep learning framework for robust semantic SLAM. In Proceedings of 2020 Advances in Science and Engineering Technology International Conferences, Dubai, United Arab Emirates, 4 February–9 April 2020.
30. Peters, M.; Kumpfert, J.; Ward, C.H.; Leyens, C. Titanium Alloys for Aerospace Applications. Adv. Eng. Mater. 2003, 5, 419–427, doi:10.1002/adem.200310095.
31. Alluaibi, M.H.I.; Cojocaru, E.M.; Rusea, A.; Șerban, N.; Coman, G.; Cojocaru, V.D. Microstructure and mechanical properties evolution during solution and ageing treatment for a hot deformed, above β-transus, ti-6246 alloy. Metals 2020, 10, 1–16, doi:10.3390/met10091114.
32. Kapoor, K.; Ravi, P.; Naragani, D.; Park, J.-S.; Almer, J.D.; Sangid, M.D. Strain rate sensitivity, microstructure variations, and stress-assisted β → α′′ phase transformation investigation on the mechanical behavior of dual-phase titanium alloys. Mater. Charact. 2020, 166, 110410, doi:10.1016/j.matchar.2020.110410.
33. Thijs, L.; Verhaeghe, F.; Craeghs, T.; Humbeeck, J.V.; Kruth, J.P. A study of the microstructural evolution during selective laser melting of Ti-6Al-4V. Acta Mater. 2010, 58, 3303–3312, doi:10.1016/j.actamat.2010.02.004.
34. Vandenbroucke, B.; Kruth, J.P. Selective laser melting of biocompatible metals for rapid manufacturing of medical parts. Rapid Prototyp. J. 2007, 13, 196–203, doi:10.1108/13552540710776142.
35. Edwards, P.; Ramulu, M. Fatigue performance evaluation of selective laser melted Ti-6Al-4V. Mater. Sci. Eng. A 2014, 598, 327–337, doi:10.1016/j.msea.2014.01.041.
36. Dinh, T.D.; Han, S.; Yaghoubi, V.; Xiang, H.; Erdelyi, H.; Craeghs, T.; Segers, J.; Van Paepegem, W. Modeling detrimental effects of high surface roughness on the fatigue behavior of additively manufactured Ti-6Al-4V alloys. Int. J. Fatigue 2021, 144, doi:10.1016/j.ijfatigue.2020.106034.
37. Bai, H.; Deng, H.; Chen, L.; Liu, X.; Qin, X.; Zhang, D.; Liu, T.; Cui, X. Effect of heat treatment on the microstructure and mechanical properties of selective laser-melted Ti64 and Ti-5Al-5Mo-5v-1Cr-1Fe. Metals 2021, 11, doi:10.3390/met11040534.
38. Kim, Y.K.; Park, S.H.; Kim, Y.J.; Almangour, B.; Lee, K.A. Effect of Stress Relieving Heat Treatment on the Microstructure and High-Temperature Compressive Deformation Behavior of Ti-6Al-4V Alloy Manufactured by Selective Laser Melting. Metall. Mater. Trans. A Phys. Metall. Mater. Sci. 2018, 49, 5763–5774, doi:10.1007/s11661-018-4864-0.
39. Sercombe, T.; Jones, N.; Day, R.; Kop, A. Heat treatment of Ti-6Al-7Nb components produced by selective laser melting. Rapid Prototyp. J. 2008, 14, 300–304, doi:10.1108/13552540810907974.
40. Ganor, Y.I.; Tiferet, E.; Vogel, S.C.; Brown, D.W.; Chonin, M.; Pesach, A.; Hajaj, A.; Garkun, A.; Samuha, S.; Shneck, R.Z., et al. Tailoring microstructure and mechanical properties of additively-manufactured ti6al4v using post processing. Materials 2021, 14, 1–17, doi:10.3390/ma14030658.
41. Zhang, L.C.; Miller, J.D.; Sercombe, T.B. Microstructural manipulation and mechanical properties of Ti-24Nb-4Zr-8Sn alloy manufactured by selective laser melting. In Proceedings of Ti 2011 12th World Conference on Titanium, Beijing, China, 19–24 June 2011; pp. 1740–1743.
42. Zhou, L.; Yuan, T.; Li, R.; Tang, J.; Wang, M.; Mei, F. Microstructure and mechanical properties of selective laser melted biomaterial Ti-13Nb-13Zr compared to hot-forging. Mater. Sci. Eng. A 2018, 725, 329–340, doi:10.1016/j.msea.2018.04.001.
43. A. Liu, C.K.C., K.F. Leong. Properties of Test Coupons Fabricated by Selective Laser Melting. Key Eng. Mater. 2010, 447-448, 780-784.
44. Schmidhuber, J. Deep learning in neural networks: An overview. Neural Netw. 2015, 61, 85–117, doi:10.1016/j.neunet.2014.09.003.
45. Brownlee, J. Machine Learning Algorithms from Scratch: With Python; Machine Learning Mastery: San Juan, PR, USA, 2017.
46. Read, N.; Wang, W.; Essa, K.; Attallah, M.M. Selective laser melting of AlSi10Mg alloy: Process optimisation and mechanical properties development. Mater. Design 2015, 65, 417–424, doi:10.1016/j.matdes.2014.09.044.
47. Elsayed, M.; Ghazy, M.; Youssef, Y.; Essa, K. Optimization of SLM process parameters for Ti6Al4V medical implants. Rapid Prototyp. J. 2019, 25, 433–447, doi:10.1108/RPJ-05-2018-0112.

JournalApplications of Artificial Intelligence in Additive Manufacturing
Permalink -

https://repository.canterbury.ac.uk/item/8zw5q/parts-design-and-process-optimization

  • 2
    total views
  • 1
    total downloads
  • 1
    views this month
  • 0
    downloads this month

Export as

Related outputs

Influence of bifilm defects generated during mould filling on the tensile properties of Al–Si–Mg cast alloys
Hassanin, H., El-Sayed, M. and Essa, K. 2022. Influence of bifilm defects generated during mould filling on the tensile properties of Al–Si–Mg cast alloys. Metals.
Effect of runner thickness and hydrogen content on the mechanical properties of A356 alloy castings
Hassanin, H., El-Sayed, M. and Essa, K. 2021. Effect of runner thickness and hydrogen content on the mechanical properties of A356 alloy castings . International Journal of Metalcasting.
Multi stages toolpath optimisation of single point incremental forming process
Hassanin, H., Yan, Z, El-Sayed, M., Eldessouky, H., Djuansjah, J., Alsaleh, N., Essa, K. and Ahmadein, M. 2021. Multi stages toolpath optimisation of single point incremental forming process. Materials. 14 (22), p. 6794. https://doi.org/10.3390/ma14226794
Micro-additive manufacturing technologies of three-dimensional MEMS
Hassanin, H., Sheikholeslami, G., Pooya, S. and Ishaq, R. 2021. Micro-additive manufacturing technologies of three-dimensional MEMS . Advanced Engineering Materials. https://doi.org/10.1002/adem.202100422
Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications
Fan , W., Chen, Y., Li, J., Sun, Y., Feng, F., Hassanin, H. and Sareh, P. 2021. Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications. Structures. 33, pp. 3954-3963. https://doi.org/10.1016/j.istruc.2021.06.110
Porosity, cracks, and mechanical properties of additively manufactured tooling alloys: A review
Bidare, P., Jiménez, A., Hassanin, H. and Essa, K. 2021. Porosity, cracks, and mechanical properties of additively manufactured tooling alloys: A review. Advances in Manufacturing. https://doi.org/10.1007/s40436-021-00365-y
Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches
Hassanin, H., Zweiri, Y., Finet, L., Essa, K., Qiu, C. and Attallah, M. 2021. Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches. Materials. 14 (8), p. 2056. https://doi.org/10.3390/ma14082056
3DP printing of oral solid formulations: a systematic review
Brambilla, C., Okafor-Muo, O., Hassanin, H. and ElShaer, A. 2021. 3DP printing of oral solid formulations: a systematic review. Pharmaceutics. 13 (3), p. 358. https://doi.org/10.3390/pharmaceutics13030358
Powder-based laser hybrid additive manufacturing of metals: A review
Hassanin, H. 2021. Powder-based laser hybrid additive manufacturing of metals: A review. The International Journal of Advanced Manufacturing Technology.
Micro-fabrication of ceramics: additive manufacturing and conventional technologies
Hassanin, H., Essa, K., Elshaer, A., Imbaby, M. and El-Sayed, T. E. 2021. Micro-fabrication of ceramics: additive manufacturing and conventional technologies. Journal of Advanced Ceramics. 10, pp. 1-27. https://doi.org/10.1007/s40145-020-0422-5
4D Printing of origami structures for minimally invasive surgeries using functional scaffold
Langford, T, Mohammed, A., Essa, K., Elshaer, A. and Hassanin, H. 2020. 4D Printing of origami structures for minimally invasive surgeries using functional scaffold. Applied Sciences. 11 (1), p. 332. https://doi.org/10.3390/app11010332
Reconfigurable multipoint forming using waffle-type elastic cushion and variable loading profile
Hassanin, H., Mohammed, M., Abdel-Wahab, A. and Essa, K 2020. Reconfigurable multipoint forming using waffle-type elastic cushion and variable loading profile. Materials.
3D printing of solid oral dosage forms: numerous challenges with unique opportunities
Hassanin, H. 2020. 3D printing of solid oral dosage forms: numerous challenges with unique opportunities. Journal of Pharmaceutical Sciences. https://doi.org/10.1016/j.xphs.2020.08.029
Design optimisation of additively manufactured titanium lattice structures for biomedical implants
El-Sayed, M.A., Essa, K., Ghazy, M. and Hassanin, H. 2020. Design optimisation of additively manufactured titanium lattice structures for biomedical implants. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-020-05982-8
4D Printing of NiTi auxetic structure with improved ballistic performance
Hassanin, H., Abena, A., Elsayed, M.A. and Essa, K. 2020. 4D Printing of NiTi auxetic structure with improved ballistic performance. Micromachines. 11 (8), p. 745. https://doi.org/doi.org/10.3390/mi11080745