Decision-making framework supported by techno-economic analysis of laser powder bed fusion: a novel approach using Retrieved Augmentation Generation (RAG)

Abstract

This paper introduces a comprehensive decision-making framework for Laser Powder Bed Fusion (L-PBF), focusing on material selection challenges. Utilizing a Retrieved Augmentation Generation (RAG) model, the framework enhances user decision-making by providing interactive access to an extensive repository of research articles. The framework’s practical utility is illustrated through successful applications of SS316L, Ti6Al4V, and AlSi10Mg materials in aerospace, automotive and biomedical industries. The cost estimation accuracy is demonstrated through cost comparisons of parts, such as impeller, throttle pedal arm and femoral stem, validated using commercial web-based quotation, revealing cost differences of approximately 21%, 11%, and 22%, respectively. In addition, the framework addresses user inquiries with varying specificity, showcasing the RAG model’s capability to deliver both detailed and generalized responses. When confronted with questions outside the scope of the existing database, the RAG model transparently indicates the unavailability of information, ensuring reliable guidance. By facilitating informed material selection and precise cost predictions, this framework promotes broader adoption and technological advancement in additive manufacturing, fostering innovation and efficiency in manufacturing processes.