Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches

Journal article


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
AuthorsHassanin, H., Zweiri, Y., Finet, L., Essa, K., Qiu, C. and Attallah, M.
Abstract

Ti-6Al-2Sn-4Zr-6Mo is one of the most important titanium alloys characterised by its high strength, fatigue, and toughness properties, making it a popular material for aerospace and biomedical applications. However, no studies have been reported on processing this alloy using laser powder bed fusion. In this paper, a deep learning neural network (DLNN) was introduced to rationalise and predict the densification and hardness due to Laser Powder Bed Fusion of Ti-6Al-2Sn-4Zr-6Mo alloy. The process optimisation results showed that near-full densification is achieved in Ti-6Al-2Sn-4Zr-6Mo alloy samples fabricated using an energy density of 77–113 J/mm3. Furthermore, the hardness of the builds was found to increase with increasing the laser energy density. Porosity and the hardness measurements were found to be sensitive to the island size, especially at high-energy-density. Hot isostatic pressing (HIP) was able to eliminate the porosity, increase the hardness, and achieve the desirable α and β phases. The developed model was validated and used to produce process maps. The trained deep learning neural network model showed the highest accuracy with a mean percentage error of 3% and 0.2% for the porosity and hardness. The results showed that deep learning neural networks could be an efficient tool for predicting materials properties using small data.

KeywordsDeep learning; Additive manufacturing; Porosity; Powder bed fusion
Year2021
JournalMaterials
Journal citation14 (8), p. 2056
PublisherMDPI
ISSN1996-1944
Digital Object Identifier (DOI)https://doi.org/10.3390/ma14082056
Official URLhttps://www.mdpi.com/1996-1944/14/8/2056
Publication dates
Print19 Apr 2021
Publication process dates
Accepted14 Apr 2021
Deposited22 Apr 2021
Publisher's version
File Access Level
Open
Output statusPublished
References

1. W. Ge, F. Lin, and C. Guo, Materials and Manufacturing Processes, 2018, 33(15), 1708-1713.
2. K. Davidson and S. Singamneni, Materials and Manufacturing Processes, 2016, 31(12), 1543-1555.
3. F. Scherillo, A. Astarita, L. Carrino, C. Pirozzi, U. Prisco, and A. Squillace, Materials and Manufacturing Processes, 2019, 34(2), 201-207.
4. M. Y. Kayacan, K. Özsoy, B. Duman, N. Yilmaz, and M. C. Kayacan, Materials and Manufacturing Processes, 2019, 34(13), 1467-1475.
5. H. Hassanin, F. Modica, M. A. El-Sayed, J. Liu, and K. Essa, Advanced Engineering Materials, 2016, 18(9), 1544-1549.
6. T. Langford, A. Mohammed, K. Essa, A. Elshaer, and H. Hassanin, Applied Sciences, 2021, 11(1), 332.
7. C. R. M. Brambilla, O. L. Okafor-Muo, H. Hassanin, and A. ElShaer, Pharmaceutics, 2021, 13(3), 358.
8. O. L. Okafor-Muo, H. Hassanin, R. Kayyali, and A. ElShaer, Journal of Pharmaceutical Sciences, 2020, 109(12), 3535-3550.
9. H. Hassanin, Y. Alkendi, M. Elsayed, K. Essa, and Y. Zweiri, Advanced Engineering Materials, 2020, 22(3), 1901338.
10. H. Klippstein, H. Hassanin, A. Diaz De Cerio Sanchez, Y. Zweiri, and L. Seneviratne, Advanced Engineering Materials, 2018, 20(9), 1800290.
11. A. Galatas, H. Hassanin, Y. Zweiri, and L. Seneviratne, Polymers, 2018, 10(11), 1262.
12. H. Hassanin, A. Abena, M. A. Elsayed, and K. Essa, Micromachines, 2020, 11(8), 745.
13. M. Schmitt, R. M. Mehta, and I. Y. Kim, Rapid Prototyping Journal, 2020, 26(1), 89-99.
14. A. Mohammed, A. Elshaer, P. Sareh, M. Elsayed, and H. Hassanin, International Journal of Pharmaceutics, 2020, 580, 119245.
15. H. Hassanin and K. Jiang, Microelectronic Engineering, 2010, 87(5), 1617-1619.
16. K. Essa, H. Hassanin, M. M. Attallah, N. J. Adkins, A. J. Musker, G. T. Roberts, N. Tenev, and M. Smith, Applied Catalysis A: General, 2017, 542, 125-135.
17. M. A. El-Sayed, H. Hassanin, and K. Essa, International Journal of Cast Metals Research, 2016, 29(6), 350-354.
18. A. Jiménez, P. Bidare, H. Hassanin, F. Tarlochan, S. Dimov, and K. Essa, The International Journal of Advanced Manufacturing Technology, 2021.
19. K. Essa, R. Khan, H. Hassanin, M. M. Attallah, and R. Reed, The International Journal of Advanced Manufacturing Technology, 2016, 83(9), 1835-1845.
20. A. Sabouri, A. K. Yetisen, R. Sadigzade, H. Hassanin, K. Essa, and H. Butt, Energy & Fuels, 2017, 31(3), 2524-2529.
21. G. B. Olson, Science, 1997, 277(5330), 1237-1242.
22. A. Belhocine and A. Afzal, Australian Journal of Mechanical Engineering, 2020, 1-14.
23. B. Yuan, G. M. Guss, A. C. Wilson, S. P. Hau-Riege, P. J. DePond, S. McMains, M. J. Matthews, and B. Giera, Advanced Materials Technologies, 2018, 3(12), 1800136.
24. D. Weichert, P. Link, A. Stoll, S. Rüping, S. Ihlenfeldt, and S. Wrobel, The International Journal of Advanced Manufacturing Technology, 2019.
25. J. Yang, S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang, Materials, 2020, 13(24), 1-23.
26. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle: 'Greedy layer-wise training of deep networks', Proceedings of the 19th International Conference on Neural Information Processing Systems, Canada, 2006, MIT Press, 153-160.
27. G. E. Hinton and R. R. Salakhutdinov, Science, 2006, 313(5786), 504-507.
28. G. E. Hinton, S. Osindero, and Y.-W. Teh, Neural Computation, 2006, 18(7), 1527-1554.
29. R. Azzam, T. Taha, S. Huang, and Y. Zweiri: 'A deep learning framework for robust semantic SLAM', 2020 Advances in Science and Engineering Technology International Conferences, ASET 2020, 2020.
30. M. Peters, J. Kumpfert, C. H. Ward, and C. Leyens, Advanced Engineering Materials, 2003, 5(6), 419-427.
31. M. H. I. Alluaibi, E. M. Cojocaru, A. Rusea, N. Șerban, G. Coman, and V. D. Cojocaru, Metals, 2020, 10(9), 1-16.
32. K. Kapoor, P. Ravi, D. Naragani, J.-S. Park, J. D. Almer, and M. D. Sangid, Materials Characterization, 2020, 166, 110410.
33. L. Thijs, F. Verhaeghe, T. Craeghs, J. V. Humbeeck, and J. P. Kruth, Acta Materialia, 2010, 58(9), 3303-3312.
34. B. Vandenbroucke and J. P. Kruth, Rapid Prototyping Journal, 2007, 13(4), 196-203.
35. P. Edwards and M. Ramulu, Materials Science and Engineering A, 2014, 598, 327-337.
36. T. D. Dinh, S. Han, V. Yaghoubi, H. Xiang, H. Erdelyi, T. Craeghs, J. Segers, and W. Van Paepegem, International Journal of Fatigue, 2021, 144.
37. H. Bai, H. Deng, L. Chen, X. Liu, X. Qin, D. Zhang, T. Liu, and X. Cui, Metals, 2021, 11(4).
38. Y. K. Kim, S. H. Park, Y. J. Kim, B. Almangour, and K. A. Lee, Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science, 2018, 49(11), 5763-5774.
39. T. Sercombe, N. Jones, R. Day, and A. Kop, Rapid Prototyping Journal, 2008, 14(5), 300-304.
40. Y. I. Ganor, E. Tiferet, S. C. Vogel, D. W. Brown, M. Chonin, A. Pesach, A. Hajaj, A. Garkun, S. Samuha, R. Z. Shneck, and O. Yeheskel, Materials, 2021, 14(3), 1-17.
41. L. C. Zhang, J. D. Miller, and T. B. Sercombe: 'Microstructural manipulation and mechanical properties of Ti-24Nb-4Zr-8Sn alloy manufactured by selective laser melting', Ti 2011 - Proceedings of the 12th World Conference on Titanium, 2012, 1740-1743.
42. L. Zhou, T. Yuan, R. Li, J. Tang, M. Wang, and F. Mei, Materials Science and Engineering A, 2018, 725, 329-340.
43. C. K. C. A. Liu, K.F. Leong, Key Engineering Materials, 2010, 447-448, 780-784.
44. J. Schmidhuber, Neural Networks, 2015, 61, 85-117.
45. J. Brownlee: 'Machine Learning Algorithms from Scratch: With Python'; 2017, Jason Brownlee.
46. N. Read, W. Wang, K. Essa, and M. M. Attallah, Materials & Design (1980-2015), 2015, 65, 417-424.
47. M. Elsayed, M. Ghazy, Y. Youssef, and K. Essa, Rapid Prototyping Journal, 2019, 25(3), 433-447.

Permalink -

https://repository.canterbury.ac.uk/item/8x89v/laser-powder-bed-fusion-of-ti-6al-2sn-4zr-6mo-alloy-and-properties-prediction-using-deep-learning-approaches

Download files


Publisher's version
materials-14-02056-v2.pdf
File access level: Open

  • 88
    total views
  • 43
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

A novel enhanced SOC estimation method for lithium-ion battery cells using cluster-based LSTM models and centroid proximity selection
Al-Alawi, M., Jaddoa, A., Cugley, J. and Hassanin, H. 2024. A novel enhanced SOC estimation method for lithium-ion battery cells using cluster-based LSTM models and centroid proximity selection. Journal of Energy Storage.
Tailoring 3D star-shaped auxetic structures for enhanced mechanical performance
Hassanin, H., Wang, Y., A. Alsaleh, N., Djuansjah, J., El-Sayed, K. and Essa, K. 2024. Tailoring 3D star-shaped auxetic structures for enhanced mechanical performance. Aerospace. 11 (6), p. 428. https://doi.org/10.3390/aerospace11060428
Virtual prototyping of vision-based tactile sensors design for robotic-assisted precision machining
Zaid, I., Sajwani, H., Halwani, M., Hassanin, H., Ayyad, A., AbuAssi, A., Almaskari, F., Abdul Samad, Y, Abusafieh, A. and Zweiri, Y. 2024. Virtual prototyping of vision-based tactile sensors design for robotic-assisted precision machining. Sensors and Actuators A: Physical. 374 (115469). https://doi.org/10.1016/j.sna.2024.115469
Designing lightweight 3D-printable bioinspired structures for enhanced compression and energy absorption properties
Harish, A., A. Alsaleh, N., Ahmadein, M., Elfar, A., Djuansjah, J., Hassanin, H., El-Sayed, M. and Essa, K. 2024. Designing lightweight 3D-printable bioinspired structures for enhanced compression and energy absorption properties. Polymers. 16 (6), p. 729. https://doi.org/10.3390/polym16060729
A novel vision-based multi-functional sensor for normality and position measurements in precise robotic manufacturing
Halwani, M., Ayyad, A., AbuAssi, L., Abdulrahman, Y., Almaskari, F., Hassanin, H., Abusafieh, A. and Zweiri, Y. 2024. A novel vision-based multi-functional sensor for normality and position measurements in precise robotic manufacturing. Precision Engineering. 88, pp. 367-381. https://doi.org/10.1016/j.precisioneng.2024.02.015
Optimisation of a novel hot air contactless single incremental point forming of polymers
Almadani, M., Guner, A., Hassanin, H. and Essa, K. 2024. Optimisation of a novel hot air contactless single incremental point forming of polymers. Journal of Manufacturing Processes. 117, pp. 302-314. https://doi.org/10.1016/j.jmapro.2024.02.042
Advancing safety and efficiency in critical infrastructure with a novel SOC estimation for battery storage systems: A focus on second life batteries
Al-Alawi, M., Cugley, J., Jaddoa, A. and Hassanin, H. 2024. Advancing safety and efficiency in critical infrastructure with a novel SOC estimation for battery storage systems: A focus on second life batteries.
Contactless single point incremental forming: Experimental and numerical simulation
Almadani, M., Guner, A., Hassanin, H., De Lisi, Michele. and Essa, K. 2023. Contactless single point incremental forming: Experimental and numerical simulation. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-023-12401-1
Hot-air contactless single-point incremental forming
Almadani, M., Guner, A., Hassanin, H. and Essa, K. 2023. Hot-air contactless single-point incremental forming. Journal of Manufacturing and Materials Processing. 7 (5), p. 179. https://doi.org/10.3390/jmmp7050179
Optimising surface roughness and density in titanium fabrication via laser powder bed fusion
Hassanin, H., El-Sayed, M., Ahmadein, M., A. Alsaleh, N., Ataya, S., Ahmed, M. and Essa, K. 2023. Optimising surface roughness and density in titanium fabrication via laser powder bed fusion. Micromachines. 14 (8), p. 1642. https://doi.org/10.3390/mi14081642
Hybrid finite element–smoothed particle hydrodynamics modelling for optimizing cutting parameters in CFRP composites
Abena, A., Ataya, S., Hassanin, H., El-Sayed, M., Ahmadein, M., A. Alsaleh, N., Ahmed, M. and Essa, K. 2023. Hybrid finite element–smoothed particle hydrodynamics modelling for optimizing cutting parameters in CFRP composites. Polymers. 15 (13), p. 2789. https://doi.org/10.3390/polym15132789
Embracing sustainable farming: Unleashing the circular economy potential of second-life EV batteries in agricultural applications
Al-Alawi, M., Cugley, J. and Hassanin, H. 2023. Embracing sustainable farming: Unleashing the circular economy potential of second-life EV batteries in agricultural applications.
Entrained defects and mechanical properties of aluminium castings
El-Sayed, M., Essa, K. and Hassanin, H. 2023. Entrained defects and mechanical properties of aluminium castings.
Review on engineering of bone scaffolds using conventional and additive manufacturing technologies
Mohammed, A., Jiménez, A., Bidare, P., Elshaer, A., Memić, A., Hassanin, H. and Essa, K. 2023. Review on engineering of bone scaffolds using conventional and additive manufacturing technologies. 3D Printing and Additive Manufacturing. https://doi.org/10.1089/3dp.2022.0360
Preparation of polylactic acid/calcium peroxide compo-site filaments for fused deposition modelling
Mohammed, A., Kovacev , N., Elshaer, A., Melaibari, A., Iqbal, J., Hassanin, H., Essa, K. and Memić, A. 2023. Preparation of polylactic acid/calcium peroxide compo-site filaments for fused deposition modelling. Polymers. 15 (9), p. 2229. https://doi.org/10.3390/polym15092229
Non-destructive disassembly of interference fit under wear conditions for sustainable remanufacturing
Yeung, H., Ataya, S., Hassanin, H., El-Sayed, M., Ahmadein, M., A. Alsaleh, N., Ahmed, M. and Essa, K. 2023. Non-destructive disassembly of interference fit under wear conditions for sustainable remanufacturing. Machines. 11 (5), p. 538. https://doi.org/10.3390/machines11050538
Fabrication and characterization of oxygen-generating polylactic acid/calcium peroxide composite filaments for bone scaffolds
Mohammed, A., Saeed, A., Elshaer, A., Melaibari, A., Memić, A., Hassanin, H. and Essa, K. 2023. Fabrication and characterization of oxygen-generating polylactic acid/calcium peroxide composite filaments for bone scaffolds. Pharmaceuticals. 16 (4), p. 627. https://doi.org/10.3390/ph16040627
Using second-life batteries and solar power to help farms become energy efficient.
Al-Alawi, M., Cugley, J. and Hassanin, H. 2023. Using second-life batteries and solar power to help farms become energy efficient. Canterbury Christ Church University.
Chip formation and orthogonal cutting optimisation of unidirectional carbon fibre composites
Hassanin, H., Abena, A., Soo, L., Ataya, S., El-Sayed, M., Ahmadein, M., A. Alsaleh, N., Ahmed, M. and Essa, K. 2023. Chip formation and orthogonal cutting optimisation of unidirectional carbon fibre composites. Polymers. 15 (8), p. 1897. https://doi.org/10.3390/polym15081897
Fabrication and Optimisation of Ti-6Al-4V Lattice-Structured Total Shoulder Implants Using Laser Additive Manufacturing
Bittredge, Oliver, Hassanin, H., El-Sayed, M., Eldessouky, Hossam Mohamed, A. Alsaleh, N., Alrasheedi, Nashmi H., Essa, K. and Ahmadein, M. 2022. Fabrication and Optimisation of Ti-6Al-4V Lattice-Structured Total Shoulder Implants Using Laser Additive Manufacturing. Materials (Basel, Switzerland). 15 (9), p. e3095. https://doi.org/10.3390/ma15093095
Influence of Bifilm Defects Generated during Mould Filling on the Tensile Properties of Al−Si−Mg Cast Alloys
El-Sayed, M., Essa, K. and Hassanin, H. 2022. Influence of Bifilm Defects Generated during Mould Filling on the Tensile Properties of Al−Si−Mg Cast Alloys. Metals. 12 (1), p. e160. https://doi.org/10.3390/met12010160
Elastomer-based visuotactile sensor for normality of robotic manufacturing systems
Hassanin, H., Zaid, I., Halwani, M., Ayyad, A., Imam, A., Almaskari, F. and Zweiri, Y. 2022. Elastomer-based visuotactile sensor for normality of robotic manufacturing systems. Polymers. 14 (23), p. 5097. https://doi.org/10.3390/polym14235097
Techno-economic feasibility of retired electric-vehicle batteries repurpose/reuse in second-life applications: A systematic review
Hassanin, H., Al-Alawi, M. and Cugley, J. 2022. Techno-economic feasibility of retired electric-vehicle batteries repurpose/reuse in second-life applications: A systematic review. Energy and Climate Change. 3 (100086). https://doi.org/10.1016/j.egycc.2022.100086
Planning, operation, and design of market-based virtual power plant considering uncertainty
Hassanin, H., Ullah, Z., Arshad, Cugley, J. and Al-Alawi, M. 2022. Planning, operation, and design of market-based virtual power plant considering uncertainty. Energies. 19 (15), p. 7290. https://doi.org/10.3390/en15197290
The epistemic insight digest: Issue : Autumn 2022
Gordon, A., Shalet, D., Simpson, S., Hassanin, H., Lawson, F., Lawson, M., Litchfield, A., Thomas, C., Canetta, E., Manley, K. and Choong, C. Shalet, D. (ed.) 2022. The epistemic insight digest: Issue : Autumn 2022. Canterbury Canterbury Christ Church University.
Modeling, optimization, and analysis of a virtual power plant demand response mechanism for the internal electricity market considering the uncertainty of renewable energy sources
Ullah, Z., Arshad and Hassanin, H. 2022. Modeling, optimization, and analysis of a virtual power plant demand response mechanism for the internal electricity market considering the uncertainty of renewable energy sources. Energies. 15 (14), p. 5296. https://doi.org/doi.org/10.3390/en15145296
Interdisciplinary engineering education - essential for the 21st century
Gordon, A., Simpson, S. and Hassanin, H. 2022. Interdisciplinary engineering education - essential for the 21st century.
Multipoint forming using hole-type rubber punch
Hassanin, H., Tolipov, A., El-Sayed, M., Eldessouky, H., A. Alsaleh, N., Alfozan, A., Essa, K. and Ahmadein, M. 2022. Multipoint forming using hole-type rubber punch. Metals. 12 (3), p. 491. https://doi.org/10.3390/met12030491
Multistage Tool Path Optimisation of Single-Point Incremental Forming Process
Yan, Zhou, Hassanin, H., El-Sayed, M., Eldessouky, Hossam Mohamed, Djuansjah, Joy Rizki Pangestu, A. Alsaleh, N., Essa, K. and Ahmadein, M. 2021. Multistage Tool Path Optimisation of Single-Point Incremental Forming Process. Materials (Basel, Switzerland). 14 (22), p. e6794. https://doi.org/10.3390/ma14226794
Effect of runner thickness and hydrogen content on the mechanical properties of A356 alloy castings
El-Sayed, M., Essa, K. and Hassanin, H. 2021. Effect of runner thickness and hydrogen content on the mechanical properties of A356 alloy castings . International Journal of Metalcasting. https://doi.org/10.1007/s40962-021-00753-x
Parts design and process optimization
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
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
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