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The modeling, analysis, and application of the in-process machining data for CNC machining

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Abstract

As the Computer Numerical Control (CNC) machining is executing, a large volume of in-process machining data is generated simultaneously in the CNC system, such as the interpolation point, actual feed rate, and tracking error. As a comprehensive reflection of entire CNC machining process, those types of data can be utilized to analyze the machining process and evaluate the machining results, especially in case of machining abnormalities, e.g., surface machining defects, excessive machining error, and malfunction of machine tool. In this paper, a novel idea called Chromatographic Point Cloud of Interpolation (CPCI) is defined by firstly building a point cloud from interpolation points and then assigning a color to each point according to a certain in-process machining data (e.g., feed rate). In order that the machining process can be effectively analyzed from the CPCI, a set of methods is proposed to efficiently triangulate the point cloud of the CPCI and quantify the continuity of in-process machining data of the CPCI. Experiments on two surfaces validate the effectiveness and advantage of the proposed triangular surface reconstruction method as well as its application in eliminating the surface defects resulting from the discontinuous feed rate between adjacent Cutter Location (CL) curves.

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Funding

This work is supported by the National Natural Science Foundation of China under grant no. 51575210.

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Huicheng Zhou, Minglang Lang, Pengcheng Hu, Zhiwei Su & Jihong Chen

Authors
  1. Huicheng Zhou
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  2. Minglang Lang
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  3. Pengcheng Hu
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  4. Zhiwei Su
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  5. Jihong Chen
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Corresponding author

Correspondence to Pengcheng Hu.

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Zhou, H., Lang, M., Hu, P. et al. The modeling, analysis, and application of the in-process machining data for CNC machining. Int J Adv Manuf Technol 102, 1051–1066 (2019). https://doi.org/10.1007/s00170-018-2963-0

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  • DOI: https://doi.org/10.1007/s00170-018-2963-0

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