Abstract
Early faults of product have plagued many machine tool companies, especially middle-small enterprises limited by capital. To eliminate the early faults and improve the reliability of computerized numerical control (CNC) machine tools, we propose a systematic early fault elimination method based on the after-sale data (customer field data) of CNC machine tools. The proposed method includes four steps: fault data collection; a four-parameter non-homogenous Poisson process (NHPP) model; a mixed fault analysis method that is the combination of fault tree analysis (FTA) method and fault mode, effects, and criticality analysis (FMECA) method; and early fault elimination measures. The first step is the basis of the analysis. The second step is used to determine the early faults from the fault data. The third step is to analyze the early faults determined. Risk priority number (RPN) is calculated to assess the criticality of the fault causes determined by the FTA. The elimination measures are taken in the end. We apply our method to a machine tool company in China, and the results prove the practicality and effectiveness of the proposed method.
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Funding
An earlier partial version of this paper was presented at the 2018 International Conference on Intelligent Manufacturing and Internet of Things (IMIOT 2018) and International Conference on Intelligent Computing for Sustainable Energy and Environment (ICSEE 2018) in September 21–23 in Chongqing, China. We specifically thank our cooperative corporation (Baoji Machine Tool Group Co., Ltd. in China) for giving the support of the research. This work is sponsored in part by the National Natural Science Foundation of China under Grants 51835001 and 51705048, in part by the National Major Scientific and Technological Special Project for “High-grade CNC and Basic Manufacturing Equipment”, China under Grant 2018ZX04032-001.
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Zhang, X., Li, Y., Zhang, G. et al. An early fault elimination method of computerized numerical control machine tools. Int J Adv Manuf Technol 106, 5049–5059 (2020). https://doi.org/10.1007/s00170-020-04956-0
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DOI: https://doi.org/10.1007/s00170-020-04956-0