Similarity Measuring Approuch for Engineering Materials Selection

TL;DR

This paper proposes a similarity-based model for engineering materials selection, leveraging data mining techniques to efficiently analyze complex material data and improve decision-making in materials design.

Contribution

It introduces a novel similarity measuring approach for materials selection, addressing data organization challenges and enhancing the efficiency of materials discovery processes.

Findings

Effective decision-making in materials design

Improved efficiency in materials selection process

Potential for accelerated materials discovery

Abstract

Advanced engineering materials design involves the exploration of massive multidimensional feature spaces, the correlation of materials properties and the processing parameters derived from disparate sources. The search for alternative materials or processing property strategies, whether through analytical, experimental or simulation approaches, has been a slow and arduous task, punctuated by infrequent and often expected discoveries. A few systematic efforts have been made to analyze the trends in data as a basis for classifications and predictions. This is particularly due to the lack of large amounts of organized data and more importantly the challenging of shifting through them in a timely and efficient manner. The application of recent advances in Data Mining on materials informatics is the state of art of computational and experimental approaches for materials discovery. In this paper similarity based engineering materials selection model is proposed and implemented to select engineering materials based on the composite materials constraints. The result reviewed from this model is sustainable for effective decision making in advanced engineering materials design applications.