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- 저자 : Xitian Tian (검색결과 2 건)
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Xiaolin Shi,Xitian Tian,Gangfeng Wang 한국정밀공학회 2020 International Journal of Precision Engineering and Vol.21 No.7
Considering that each computer-aided design (CAD) system has independent geometric dimensioning and tolerancing modeling method, precision information model generated by different CAD systems is difficult to be shared and reused by the downstream precision analysis system. To tackle the problem that tolerance semantic information cannot be transmitted through data exchange standards, this paper aims to propose a novel semantic tolerance screening approach to represent precision information for assembly precision analysis (APA) in the design stage. Based on semantic correlation between tolerance propagation and accumulation, precision information of multi-parts is preliminarily screened out. Then by utilizing semantic web rule language rules to determine the type and position of tolerance zones, multiple tolerances existing on a precision feature surface are refinedly screened out. Finally, a formal tolerance screening ontology, named ToS-Ontology, is generated for performing APA of complex products. The effectiveness of the proposed approach is demonstrated by a practical example, which is to calculate center distance between two holes to ensure bolts can pass smoothly in the limit case.
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Bo Li,Xitian Tian,Min Zhang 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.9 No.1
The natural energy crisis and the increasingly serious environmental problems have imposed all industries to reduce energy consumption. During milling process, selecting a correct cutting parameters can not only greatly improve production quality and processing efficiency, but also can reduce energy consumption, in addition, tool wear also has a great impact on them. Therefore, a milling power consumption model of CNC machine tools is established based on modern machining theory is established in this article, unlike traditional energy consumption models, our model takes full account of cutting conditions and tool wear. The surface roughness of parts is one of the important indicators to measure the machining quality of machine tools. Therefore, taking milling process as research object, a multi-objective cutting parameters optimization model that takes the machining surface roughness, material removal rate (MRR) and machining energy consumption as the optimization goals was established. Furthermore, an intelligent optimization algorithm was proposed based on improved Teaching–Learning- Based Optimization (TLBO) to solve the model under various limited milling conditions. Finally, comparing experimental results of optimized parameter and empirical parameters, it shows that goals of reducing energy consumption, improving productivity and machining quality can be achieved by optimizing cutting parameters.
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