APPLICATION OF GENERATIVE DESIGN AND DIGITAL IMAGE CORRELATION FOR PART OPTIMIZATION AND MECHANICAL TESTING

Generative design has emerged as a transformative approach in engineering, enabling the creation of highly optimized components with complex geometries that are well-suited for additive manufacturing (AM). This study examines the integration of generative design, AM and digital image correlation (DIC) for iterative part optimization and mechanical performance validation. By leveraging generative design algorithms, parts can be optimized for weight reduction, material efficiency, and structural performance. The rapid prototyping capabilities of AM facilitate swift fabrication of both original and redesigned components, allowing for direct comparison through experimental testing. DIC, as a noncontact full-field measurement technique, plays a pivotal role in validating numerical models by capturing detailed strain distributions under various load conditions. This paper presents a comprehensive framework that highlights the interdependence of these technologies, reviews challenges and advances, and outlines future directions for refining the mechanical design cycle through enhanced data-driven workflows.