Machine learning can expedite the process of material design, testing, and manufacturing by eliminating the lengthy trial-and-error methodology.


Copyright: – “How Machine Learning Can Speed Up Material Design, Testing and Manufacturing”


The conventional alloy design process is multistage and thus multivariable, leading to tremendous effort. Further, alloy design is also about meeting a well-defined target of properties like yield strength, ultimate tensile strength, fracture toughness, fatigue life etc. ML method can eliminate long hit-and-trial-based expensive methodology of material design to smart discovery and deployment. A tangible effort is going on across the globe to curate, capture, generate and manage data and to come up with algorithms to predict the next processes and materials.

New Delhi: Materials are the face of our civilization and the techno-social growth is hinged to the ground-breaking discovery of materials and manufacturing methods. We and our surroundings are all made of materials and the next generation technology drives a new generation of materials and manufacturing.

Due to the application of advanced computational methods in engineering, sophisticated synthesis and faster testing methods, development cycle of new materials has come down from a decade to a couple of years. However, we have not reached where we can design digitally and realize a new material in the lab or in industry setting, without foregoing wholesome trials.

New paradigm in material design

Design of new materials is well beyond a simple concept of mixing its constituents. For example, the most used engineering alloys e.g. steel, super alloys and titanium alloys have many constituent elements like iron, carbon, nickel, chromium, titanium, vanadium and so on. One needs to understand the temperatures at which these elements melt and mix, how fast to cool the liquid, how to form into various usable shapes, understand intermediate heating steps, and so on. Iterative design of new alloys by understanding various permutations and combinations of compositions and process variables falls in the scope of metallurgy.[…]

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