Abstract

“Unlike images and videos, materials and manufacturing suffer from a paucity of data. Exploring opportunities in the emerging field of data-driven materials design, this talk examines how AI is accelerating material discovery and engineering applications. Investigating whether AI can replace extensive testing and computations or even uncover novel alloy compositions, we are exploring both its potential and its limitations. While AI may reduce complexity in numerical modelling, it sometimes violates physical laws, raising concerns about its reliability. Addressing the challenge of real-world measurements, we question whether AI can effectively distinguish noise from meaningful data. Recognising AI’s promise in advancing materials discovery and manufacturing, we are re-evaluating material testing in manufacturing science, refining data collection strategies, and critically examining the direct applicability of neural networks from computer vision or large language models.”

Dr Angkur Shaikeea has been an Assistant Professor in Industrial Scale-up at the Institute for Manufacturing (IfM) for more than one year (Dr Angkur Jyoti Dipanka Shaikeea – Institute for Manufacturing (IfM)). Before joining IfM, he served as the Ashby Postdoctoral Research Fellow, a position he held subsequent to completing his doctoral studies as the Cambridge India Ramanujan Scholar. His doctoral research was centered on the design of failure-resilient metamaterials. During his post-doctoral tenure, Angkur contributed to development of X-tomography methods for material testing and design. Currently, his professional focus lies on industrial scale-up, complemented by a profound passion for advancing research in mechanics of materials. In his past-times, Angkur enjoys music, poetry and photography.