An automatic generation approach of process model based on feature knowledge and geometric modeling

被引:0
作者
Wang, Pengyu [1 ]
Liu, Weichao [2 ]
You, Youpeng [3 ]
Qian, Shuang [4 ]
机构
[1] Nanjing Univ Posts & Telecommun, Coll Automat & Coll Artificial Intelligence, Nanjing 210023, Peoples R China
[2] Nanjing Inst Technol, Sch Mech Engn, Nanjing 211167, Peoples R China
[3] Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing 210016, Peoples R China
[4] Shanghai Xinyuelianhui Elect Technol Co LTD, Shanghai 200233, Peoples R China
关键词
Computer-aided application; Manufacturing information integration; Process model; Feature knowledge; 3D MODEL; CAD; PARTS;
D O I
10.1016/j.aei.2024.102881
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Traditional process planning applies knowledge to convert design information within the part model into process information. This information is then displayed by manually drawing two-dimensional (2D) process cards from multiple views, illustrating the geometric design and the process details. However, this method has revealed several issues, including poor efficiency, information misinterpretation, and the potential for manual drawing errors. This traditional approach hampers the integration of manufacturing information across engineering software. It exacerbates the digital divide between computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM). To address these issues, this study proposes an automated approach for creating three-dimensional (3D) process models based on feature knowledge and geometric modeling. The 3-axis milling features are recognized as the machining objects from the boundary representation (B-Rep) part model. Geometric and parameter knowledge for modeling is derived from the machining step. In the proposed approach, two geometric modeling methods are investigated to construct feature state volumes (FSVs). The first method utilizes FSV modeling to simulate the shape of unmachined features before rough machining, while the second method utilizes FSV modeling to construct the shape of machined features before finishing machining. The case studies illustrate that the proposed approach can construct FSVs for various machining states and autonomously generate process models. In digital manufacturing, this approach assists process planners in intuitively evaluating the reasonableness of process routes. Additionally, it provides the essential driving geometry required for the autonomous generation of tool paths.
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页数:23
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