Abstract
In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision. To effectively schedule production and maintenance actions, it is vital to model the wear process of cutting tools and predict their remaining useful life (RUL). However, it is difficult to determine the RUL of cutting tools with cutting precision as a failure criterion, as cutting precision is not directly measurable. This paper proposed a RUL prediction method for a cutting tool, developed based on a degradation model, with the roughness of the cutting surface as a failure criterion. The surface roughness was linked to the wearing process of a cutting tool through a random threshold, and accounts for the impact of the dynamic working environment and variable materials of working pieces. The wear process is modeled using a random-effects inverse Gaussian (IG) process. The degradation rate is assumed to be unit-specific, considering the dynamic wear mechanism and a heterogeneous population. To adaptively update the model parameters for online RUL prediction, an expectation–maximization (EM) algorithm has been developed. The proposed method is illustrated using an example study. The experiments were performed on specimens of 7109 aluminum alloy by milling in the normalized state. The results reveal that the proposed method effectively evaluates the RUL of cutting tools according to the specified surface roughness, therefore improving cutting quality and efficiency.
Highlights
Tool wear is widely considered to be stochastic and challenging to predict
According to the input data used in the performance degradation model, remaining useful life (RUL) prediction methods can be classified into three categories: time series models, artificial intelligence models, and stochastic process models
We studied the degradation modeling and RUL prediction of cutting tools based on a cutting precision criterion
Summary
Tool wear is widely considered to be stochastic and challenging to predict. This is primarily due to unit-to-unit performance variations and process variations. The time series models are suitable for mass production, with abundant historical degeneration data, while the artificial intelligence methods are appropriate for dealing with massive and complicated process data. This paper proposes a dynamic evaluation method for RUL prediction, links the surface roughness to the wear of the tool, and the surface roughness criterion is modeled by a random threshold for the degradation state of the tool. Considering the quality variation of the tool, the degradation rate of the inverse Gaussian process is modelled as a random effect to improve the performance of the model. The relationship between the surface roughness and degradation in terms of wearing is defined, and the RUL evaluation model with a random failure threshold is proposed.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
