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Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Author(s) -
Hassanin Hany,
Alkendi Yusra,
Elsayed Mahmoud,
Essa Khamis,
Zweiri Yahya
Publication year - 2020
Publication title -
advanced engineering materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.938
H-Index - 114
eISSN - 1527-2648
pISSN - 1438-1656
DOI - 10.1002/adem.201901338
Subject(s) - artificial neural network , materials science , lattice (music) , design of experiments , deep learning , artificial intelligence , elastic modulus , modulus , universal testing machine , computer science , mechanical engineering , ultimate tensile strength , structural engineering , machine learning , composite material , mathematics , engineering , acoustics , statistics , physics
Cellular structures are lightweight‐engineered materials that have gained much attention with the development of additive manufacturing technologies. This article introduces a precise approach to predict the mechanical properties of additively manufactured lattice structures using deep‐learning approaches. Diamond‐shaped nodal lattice structures are designed by varying strut length, strut diameter, and strut orientation angle. The samples are manufactured using laser powder bed fusion (LPBF) of Ti−64 alloy and subjected to compression testing to measure the ultimate strength, elastic modulus, and specific strength. Machine learning approaches such as shallow neural network (SNN), deep neural network (DNN), and deep learning neural network (DLNN) are developed and compared to the statistical design of experiment (DoE) approach. The trained DLNN model show the highest performance when compared with DNN, DoE, and SNN with a mean percentage error of 5.26%, 14.60%, and 9.39% for the ultimate strength, elastic modulus, and specific strength, respectively. The DLNN model is used to create process maps, and is further validated. The results show that although deep learning is preferred for big data, the optimized DLNN model outperform the statistical DoE approach and can be a favorable tool for lattice structure prediction with limited data.