The 21st century has witnessed a rapid convergence of manufacturing technology, computer science and information technology. This has led to a paradigm of 4.0. The hitherto known developments in metallurgical and materials practices are largely driven by application of fundamental knowledge through experiments and experiences. However, the mounting demands of high performance products and environmental security calls for the ‘right first time’ manufacturing in contrast to the traditional trial and error approach. In this context, a priori capability, for prediction and optimization of materials, process and product variables, is becoming the enabling factor. In recent time, research in material science is increasingly embarrassing the computational techniques in development of exotic materials with greater reliability and precision. The present study is aimed at exploring the computer vision and machine learning techniques in different application areas in materials science.

21世纪见证了制造技术，计算机科学和信息技术的快速融合。这导致了4.0的范例。冶金和材料实践中迄今已知的发展很大程度上是通过实验和经验应用基础知识来推动的。但是，与传统的试错法相比，高性能产品和环境安全的不断增长的需求要求“正确的首次”制造。在这种情况下，用于材料，工艺和产品变量的预测和优化的先验能力正成为促成因素。近年来，材料科学的研究越来越难于在具有更高可靠性和精度的奇异材料开发中使用计算技术。