Investigation of metal to composite joining by generative rivets technology

Abstract

This research explores the integration of drip-casting and laser additive manufacturing (LAM) techniques to develop generative rivets, a novel approach aimed at enhancing the joining of metal and composite materials. Utilizing high-energy density laser beams, this study investigates three iterative process enhancements, each designed to refine the production quality and application efficacy of generative rivets, culminating in the refinement from scanning strategies to empirical equations. The research predominantly assesses the industrial performance of these rivets, with a focus on residual stress measurements and mechanical performance. Employing Finite Element Analysis (FEA), this study simulates the generative process of laser rivets, comparing simulated results with empirical data obtained by X-ray Diffraction (XRD) to validate the consistency in residual stress distribution and load-bearing capacity. These comparisons have demonstrated significant alignment between predicted outcomes and experimental results, confirming the viability of the proposed manufacturing processes for industrial applications. The findings indicate that generative rivets offer substantial advantages in terms of efficiency, adaptability, and integration with machine learning-based manufacturing systems, aligning well with current technological trends toward automation and smart manufacturing. The research contributes to the field of advanced manufacturing by providing a robust alternative to traditional riveting methods, potentially transforming practices in the aerospace and automotive industries where high-strength and durable material joints are paramount.