Abstract
The reliability of cutting tools is critical to machining precision and production efficiency. The conventional statistic-based reliability assessment method aims at providing a general and overall estimation of reliability for a large population of identical units under given and fixed conditions. However, it has limited effectiveness in depicting the operational characteristics of a cutting tool. To overcome this limitation, this paper proposes an approach to assess the operation reliability of cutting tools. A proportional covariate model is introduced to construct the relationship between operation reliability and condition monitoring information. The wavelet packet transform and an improved distance evaluation technique are used to extract sensitive features from vibration signals, and a covariate function is constructed based on the proportional covariate model. Ultimately, the failure rate function of the cutting tool being assessed is calculated using the baseline covariate function obtained from a small sample of historical data. Experimental results and a comparative study show that the proposed method is effective for assessing the operation reliability of cutting tools.
Highlights
Tool failure is a major cause of unscheduled stoppages in the current manufacturing industry and is costly, in terms of time lost, and in terms of capital destroyed [1]
Taking the condition monitoring information of the cutting tool that was analysed into consideration, the approach overcomes the main shortcoming of the conventional reliability assessment method: the inability to properly reflect the characteristics of a given cutting tool
wavelet packet transform (WPT) and an improved distance evaluation technology are employed to extract and select the relevant features that are most sensitive to the degradation of the cutting tool
Summary
Tool failure is a major cause of unscheduled stoppages in the current manufacturing industry and is costly, in terms of time lost, and in terms of capital destroyed [1]. For modern machine tools, approximately 20% of downtime resulting in reduced productivity and economic losses is attributed to cutting tool failure [2]. The traditional tool replacement strategy based on regular time periods can reduce unplanned downtime and production losses to some extent, but tool degradation is a complex process and is affected by sophisticated and various machining environments [3]. There is a need to assess the real-time operation performance of a cutting tool to guarantee high machining quality and avoid unscheduled downtime to achieve higher economic efficiency and fewer disastrous accidents. It is necessary to monitor the running states and assess the performance of cutting tools to create timely replacement policies or extend tool service life
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