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Process planning is the core of guiding process production and one of the most critical parts to realize intelligent manufacturing of products. In order to realize the monitoring, simulation, prediction and control of the physical space in the intelligent manufacturing mode, the digital twin technology is used to guide the process planning. This research describes a modeling method of digital twin process model (DTPM) for manufacturing process and the contents of DT data, discusses the acquisition method of real-time data and the management method of simulation data. Aiming at the data fusion between physical space and virtual space, a hierarchical model and mapping strategy for multi-source heterogeneous data in machining process are proposed to generate the DT data. Finally, the guidance and the visualization function in process planning of DTPM is analyzed, and the effectiveness of this method is verified by choosing the design process of a key component in a marine diesel engine.

The hard turning process has been widely used in the field of hard material precision machining because of its high efficiency, low processing residual stress, and low environmental pollution. Due to its undesirably processing quality, it is still not a substitute for traditional grinding, so many studies have reported that the process has been optimized. However, there has been little research on the geometry optimization of hard cutting tools, which have a great influence on the traditional machining process. In this paper, two tools with different rake face shapes are designed. The finite element analysis method is used to compare their performance with a conventional plane tool while turning hardened steel. The results show that the cutting performance of the designed tool T1 and T2 (chip morphology, cutting force, and cutting temperature) and the quality of the machined surface are improved compared with the tool. The cutting force decreased by 12.72% and 14.74%, the cutting temperature decreased by 7.56% and 9.01%, respectively, and the surface residual stress decreased by 26.56% and 28.66%.

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