Control of structure, properties, and defects in additive manufacturing

00:00:00 Introduction
00:01:43 Metal additive manufacturing and its uniqueness and opportunities
00:06:01 Control of structure, properties, and defects
00:08:17 Why we need digital tools?
00:12:41 What is a mechanistic model?
00:23:24 Control of grain structure
00:29:18 Control of properties
00:36:51 Control of lack of fusion voids
00:44:09 Control of residual stresses and distortion
01:01:20 Control of solidification cracking
01:03:52 Control of composition change
01:09:33 Control of surface roughness
01:13:55 Major contributions
01:17:00 Quantum computing: a big opportunity

This talk was presented by Dr. Tuhin Mukherjee at a workshop on Advances in Additive Manufacturing (3D Printing) at IIT Jodhpur on 8th December 2022.

Unlike welding and casting technologies that matured largely through many decades of trial-and-error testing, additive manufacturing is uniquely positioned to benefit from the powerful emerging digital tools such as mechanistic modeling and machine learning. Grain structure, mechanical properties, and defects such as cracking, lack of fusion, distortion, residual stress, and surface roughness are difficult to control using empirical testing. The expensive machines and feedstocks and the wide range of values of additive manufacturing process variables make a large volume of physical testing expensive and time-consuming. In contrast, experiments supported by mechanistic modeling and machine learning can control structure, properties, and defects based on scientific principles. The synergy between physical and virtual experiments improves scientific rigor, accelerates the pace of development, and makes manufacturing cost competitive. Research in the virtual world using emerging digital tools has the potential to make the world a more inclusive and welcoming place for all.

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