AI-Driven Material Selection.

Material choice is a vital aspect of engineering design, and it directly impacts the performance, longevity, and cost of products in various industries.For improving decision-making in this area, artificial intelligence methods are utilized to provide recommendations on appropriate materials against both quantitative and qualitative inputs from users.The system combines machine learning models with natural language processing for understanding the requirements of the user without compromising the precise design purpose or data details.With a distributed learning environment, a universal model is trained using structured datasets of mechanical properties like yield strength, tensile strength, elongation, hardness, elastic modulus, shear modulus, Poisson's ratio, and density.Random Forest and natural language processing are used in combination, and Support Vector Regression and natural language processing are used to assess and provide material recommendations based on input patterns.The system maintains user privacy while allowing effective material prediction in various application contexts.This method enhances material selection accessibility and reliability, fostering well-informed design decisions without compromising data confidentiality and interpretability through natural language incorporation.