Machine learning model to predict tensile properties of annealed Ti6Al4V parts prepared by selective laser melting. (29th September 2022)

• Record Type: Journal Article
• Authors: Yang, Zhaotong; Yang, Mei; Sisson, Richard; Li, Yanhua; Liang, Jianyu
• Is Part Of: AI EDAM. Volume 36(2022)
• Publication Date: 2022-09-29
• Subjects: annealing -- artificial neural network -- selective laser melting -- tensile properties -- Ti-6Al-4V; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060422000117
• Languages: English
• ISSNs: 0890-0604

Abstract

Abstract: In this work, an artificial neural network model is established to understand the relationship among the tensile properties of as-printed Ti6Al4V parts, annealing parameters, and the tensile properties of annealed Ti6Al4V parts. The database was established by collecting published reports on the annealing treatment of selective laser melting (SLM) Ti6Al4V, from 2006 to 2020. Using the established model, it is possible to prescribe annealing parameters and predict properties after annealing for SLM Ti-6Al-4V parts with high confidence. The model shows high accuracy in the prediction of yield strength (YS) and ultimate tensile strength (UTS). It is found that the YS and UTS are sensitive to the annealing parameters, including temperature and holding time. The YS and UTS are also sensitive to initial YS and UTS of as-printed parts. The model suggests that an annealing process of the holding time of fewer than 4 h and the holding temperature lower than 850°C is desirable for as-printed Ti6Al4V parts to reach the YS required by the ASTM standard. By studying the collected data of microstructure and tensile properties of annealed Ti6Al4V, a new Hall-Petch relationship is proposed to correlate grain size and YS for annealed SLM Ti6Al4V parts in this work. The prediction of strain to failure shows lower accuracy compared with the predictions of YS and UTS due to the large scattering of the experimental data collected from the published reports.