A Data and Knowledge-driven framework of the intelligent process design for special-shaped features of complex aviation parts

Abstract

While the safety and reliability of aviation equipment are directly determined by the performance of complex aviation parts, these kinds of components still contain lots of special-shaped features, which refer to the machining features that need to be completed by unique process and non-standard cutting tools. In addition, the traditional process design of special-shaped features of complex aviation parts (SFCAP) heavily relies on manual experience, which leads to low machining efficiency and unstable machining quality. To solve this problem, this paper takes SFCAP as the study object and proposes a framework of intelligent process design, which can effectively guarantee the processing quality to support the performance of complex aviation equipment by realizing active adaptive adjustment, rapid iteration, and upgrading of process planning based on the machining data and relative knowledge. Four key-enabling technologies are set forth to support the design, which include the special-shaped feature-process knowledge mapping model, 3D feature recognition and geometric parameter extraction, knowledge uncertainty-based evaluation, and digital twin-based verification and optimization. What's more, the benefits and challenges of the design are analyzed, and the contribution and deficiencies are given at the end of this paper.