The assembly process of large-scale and non-standard industrial equipment poses significant challenges due to its inherent scale-related complexity and proneness to errors, making it difficult to ensure process cost, production cycle, and assembly accuracy. In response to the limitations of traditional ineffective production models, this paper aims to explore and propose a digital twin (DT)-based technology paradigm for the intelligent assembly of large-scale and non-standard industrial equipment, focusing on both the equipment structure and assembly process levels. The paradigm incorporates key technologies that facilitate the integration of virtual and physical information, including the establishment and updating of DT models for assembly structures using actual data, the assessment of structural assemblability based on DT models, the planning and simulation of assembly processes, and the implementation of virtual commissioning technology tailored to the actual assembly process. The effectiveness of the proposed paradigm is demonstrated through a case study involving the actual assembly of a large-scale aerodynamic experimental equipment. The results confirm its ability to provide valuable technical support for the design, evaluation, and optimization of industrial equipment assembly processes. By leveraging the DT-based methodological system proposed in this paper, significant improvements in the transparency and intelligence of industrial equipment production processes can be achieved.

This paper addresses the challenging problem of determining feed direction and tool orientation at a given cutter contact (CC) point in five-axis free-form surface machining with flat-end mills. The objective is to efficiently determine a feed direction and tool orientation that will avoid both local and global tool gouging and yield a near maximum machining strip width at the CC point. Concurrent determination of the optimal feed direction and tool orientation is a very computationally intensive task and searching for the correct solution would involve exhaustive evaluations of the machining strip width at many feed directions and tool orientations. In this paper, the optimal feed directions and analytical solutions for the optimal tool orientations in five-axis flat-end milling of spherical, cylindrical, and toroidal surfaces are identified first. A toroidal surface inscription method is devised to approximate the local surface geometry at a CC point on a free-form surface by an inscribed toroidal surface. Analytical solutions for toroidal surface machining are then employed to position the flat-end mill at the CC point with the tool feeding in the best toroidal surface inscribing direction. Case studies have demonstrated that the proposed method can efficiently determine a feed direction and tool orientation, corresponding to a near maximum machining strip width.