AI-Driven Design of AZ61 Magnesium Alloy for Enhanced Performance in Industrial Automation

In this study, we explore the applicability of computational intelligence approaches i.e., genetic algorithms (GA) to enhance material performance. Central to these methodologies is processes optimization and data-driven models development. In particular, our work gives a detailed overview of the state-of-the-art of materials development through innovative usage of computational intelligence (CI) methods to help them in materials innovation and exploration. In particular, the approach of the study provides a data-driven guide toward desirable properties for AZ61 magnesium alloy. A metaheuristics optimization tool, GA, is used with linear regression to estimate the optimal rolling process parameters. This allows targeted control over hardness and tensile strength, both of which are essential for the application of this magnesium alloy in industrial automation. This not just guarantees the optimum subset of process parameters, but it also provides a framework for material properties improvement. This work demonstrates how AI-Driven material design can reshape industrial performance and innovation, integrating computational intelligence tools that exercise this promise.