Machine Learning Framework for Optimization the Process Structure Property Chain in Material Engineering

Abstract

Enhancing the process-structure-property (PSP) loop plays an important role in the field of materials engineering for creating materials with specific characteristics which enhances manufacturing process efficiency. Standard approaches towards developing materials primarily depend according to experimentation evaluation and error, which might be economical & time-saving. Systematically building predictive models for complicated material networks merged with Machine Learning (ML) has shown significant potential in automating and speeding up the improvement in material operations and features with the rise of data-driven innovations. The goal of this study is to construct a model for machine learning designed to enhance material engineering's Process-Structure Property interactions. Different machine learning approaches such as reinforcement learning, deep learning & supervised learning are implemented in the technique to simulate the PSP loop. The models are trained using an enormous array comprising microstructural attributes, process parameters and properties of the material. The architecture integrates data extraction, data preparation & model evaluation protocols to ensure accurate predictions. Material qualities for polymers, metals & ceramics were accurately anticipated using an ML-based optimization methodology. It required quite less time and resources to produce materials compared with earlier strategies. Additionally, the structure proposed appropriate conditions for processing by increasing the material's durability as well as decreasing flaws. The use of machine learning may transform material creation and manufacturing by adapting high-performance developing materials faster and inexpensive.

Keywords
Machine Learning, Material engineering, PSP loop, Data-driven innovations, Reinforcement learning, deep learning and supervised learning.