PSI - Issue 47

Available online at www.sciencedirect.com Structural Integrity Procedia 00 (2022) 000 – 000 Available online at www.sciencedirect.com ^ĐŝĞŶĐĞ ŝƌĞĐƚ

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Procedia Structural Integrity 47 (2023) 56–69

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the IGF27 chairpersons Abstract The fatigue behavior of Additive Manufacturing (AM) parts is influenced by manufacturing defects, whose dimensions are primarily determined by the parameters of the AM process, which, in turn, also affect the resulting microstructure, together with heat treatments. This study employs Machine Learning (ML) techniques to forecast the fatigue response of AM parts from the AM process variables and the heat treatment characteristics. Feed-forward neural networks (FFNN) and physics-informed neural network (PINN) models are formulated and verified employing published datasets on AM Ti6Al4V alloy. The results demonstrate that physics-based ML approaches are effective in forecasting the fatigue response of AM components. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the IGF27 chairpersons Keywords: Type your keywords here, separated by semicolons ; 1. Introduction The design of metal components to be produced with Additive manufacturing (AM) technology gives space to innovative structural solutions, reducing the waste of material and allowing for the tuning of the material properties with the use, for example, of lattice structures. However, AM parts produced with Selective Laser Melting (SLM) process are characterized by numerous and large defects originating from the manufacturing, like pores, lack of fusion, 27th International Conference on Fracture and Structural Integrity (IGF27) Data-driven method to assess the influence of process parameters on the fatigue response of additively manufactured Ti6Al4V Alberto Ciampaglia a, *, Andrea Tridello a , Filippo Berto b , Davide Paolino a a Department of Mechanical and Aerospace Engineering, Politecnico di Torino, C.so Duca degli Abruzzi 24, 101219, Torino, Italy b Department of Chemical Engineering Materials Environment, Sapienza University of Rome, Via Eudossiana 18, 00184, Roma, Italy

* Corresponding author. E-mail address: alberto.ciampaglia@polito.it

2452-3216 © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the IGF27 chairpersons

2452-3216 © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the IGF27 chairpersons 10.1016/j.prostr.2023.06.041