Generative AI and digital twin integrated intelligent process planning：A conceptual framework

Abstract

Abstract Process planning serves as a critical link between design and manufacturing, exerting a pivotal influence on the quality and efficiency of production. However, current intelligent process planning systems, like computer-aided process planning (CAPP), still contend with the challenge of realizing comprehensive automation in process decision-making. These obstacles chiefly involve, though are not confined to, issues like limited intelligence, poor flexibility, low reliability, and high usage thresholds. Generative artificial intelligence (AI) has attained noteworthy accomplishments in natural language processing (NLP), offering new perspectives to address these challenges. This paper summarizes the limitations of current intelligent process planning methods and explores the potential of integrating generative AI into process planning. With synergistically incorporating digital twins, this paper introduces a conceptual framework termed generative AI and digital twin-enabling intelligent process planning (GIPP). The paper elaborates on two supporting methodologies: process generative pre-trained transformer (ProcessGPT) modelling and digital twin-based process verification method. Moreover, a prototype system is established to introduce the implementation and machining execution mechanism of GIPP for milling a specific thin-walled component. Three potential application scenarios and a comparative analysis are employed to elucidate the practicality of GIPP, providing new insights for intelligent process planning.