Data-driven Approach to Predict the Static and Fatigue Properties of Additively Manufactured Ti-6Al-4V

Data-driven Approach to Predict the Static and Fatigue Properties of Additively Manufactured Ti-6Al-4V

abstract: Additive manufacturing (AM) has been extensively investigated in recent years to explore its application in a wide range of engineering functionalities, such as mechanical, acoustic, thermal, and electrical properties. The proposed study focuses on the data-driven approach to predict the m...

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Bibliographic Details
Other Authors: Sharma, Antriksh (Author)
Format: Dissertation
Language:English
Published: 2020
Subjects:
Mechanical engineering
Additive Manufacturing
Artificial Neural Networking
Mechanical Properties
Powder Bed Fusion
Ti-6Al-4V
Online Access:http://hdl.handle.net/2286/R.I.62722

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