Enhancing Magnesium Alloy Machining Analysis through Artificial Intelligence and Multimedia Technology: A Biotechnological Approach

Abstract

Traditional methods for analysing the mechanical properties of magnesium alloys frequently encounter issues such as significant errors in ultimate tensile strength and unstable mechanical characteristics, complicating the selection of materials and comparability of results. Addressing these challenges, this study introduces an innovative analysis model that integrates an artificial intelligence optimization tracking algorithm with multimedia technology. This model utilizes an enhanced Johnson–Cook (J-A) model and a newly developed amorphous alloy magnetostriction model, optimized using the Particle Swarm Optimization algorithm to refine the calculation of model parameters. Our approach begins with a comprehensive evaluation of the structural and microstructural stability affecting the ultimate tensile strength of magnesium alloys under standard mechanical property analysis frameworks. This evaluation confirms the reliability and rationality of our model design and provides a solid theoretical foundation for further optimization. Subsequently, we employ a finite element simulation strategy to assess the mechanical performance of magnesium alloys under various modelled conditions, determining the optimal parameter combinations. Integrating this with a database of known magnesium alloys and their microstructures, we design targeted experiments to collect data using advanced multimedia technology. This data collection facilitates a detailed quantitative analysis of the mechanical properties and yield strength of processed magnesium alloys, highlighting significant improvements in precision and reliability over traditional methods.