Microstructural material design of pearlitic steel lamella for desired mechanical properties

Designing materials for a targeted mechanical property is a complex problem due to its structure and property relationship nature, traditionally relying on experimental methods. Computational approaches offer a promising alternative, but they are often computationally intensive. This limits their applicability for solving the goal-based structure design problem. This study focuses on the design of microstructure to achieve specific mechanical properties by combining generative adversarial networks, deep neural networks, and genetic algorithm optimization techniques. In this work to demonstrate the effectiveness of the proposed approach we select a widely used pearlitic steel lamella microstructure where the phase fraction of ferrite and cementite and the lamella distribution significantly influence the strength and ductility. A method for designing the pearlite microstructure features for the targeted mechanical properties, such as yield strength (sigma y), ultimate strength (sigma ut), and fracture strain (epsilon f) is developed by applying inverse microstructure design techniques to achieve the targeted mechanical properties, specifically in fully pearlitic steel materials. The findings hold great promise, particularly when coupled with additive manufacturing, in offering an efficient and versatile framework for structural materials design for targeted material applications.