Abstract
Condition monitoring of the cutting process is a core function of autonomous machining and its success strongly relies on sensed data. Despite the enormous amount of research conducted so far into condition monitoring of the cutting process, there are still limitations given the complexity underlining tool wear; hence, a clearer understanding of sensed data and its dynamical behavior is fundamental to sustain the development of more robust condition monitoring systems. The dependence of these systems on acquired data is critical and determines the success of such systems. In this study, data is acquired from an experimental setup using some of the commonly used sensors for condition monitoring, reproducing realistic cutting operations, and then analyzed upon their deterministic nature using different techniques, such as the Lyapunov exponent, mutual information, attractor dimension, and recurrence plots. The overall results demonstrate the existence of low dimensional chaos in both new and worn tools, defining a deterministic nature of cutting dynamics and, hence, broadening the available approaches to tool wear monitoring based on the theory of chaos. In addition, recurrence plots depict a clear relationship to tool condition and may be quantified considering a two-dimensional structural measure, such as the semivariance. This exploratory study unveils the potential of non-linear dynamics indicators in validating information strength potentiating other uses and applications.
Highlights
Since the late 1990s, we have witnessed a change from the old practice of changing tools automatically to the feasibility of instituting tool change procedures based on monitoring the amount of wear on the cutting tool-edges through the implementation of adaptive tool inspection mechanisms [1,2]
Based on the earlier description, average mutual information and false nearest neighbors were determined in order to identify the switching dynamics in the time series pertaining to the different sensors
This study explores the deterministic nature of sensed information for condition monitoring providing qualitative and quantitative evidence of their impact
Summary
Since the late 1990s, we have witnessed a change from the old practice of changing tools automatically to the feasibility of instituting tool change procedures based on monitoring the amount of wear on the cutting tool-edges through the implementation of adaptive tool inspection mechanisms [1,2]. Literature reports numerous proposals for the architecture of condition monitoring systems for online supervision and control. Few of the architectures have gained sufficient acceptance or otherwise proved to be feasible for most machining processes/conditions. One important strategy to support this goal is sensor-based and real-time control of key characteristics of both machines and products, throughout the manufacturing process [3,4,5]. The development of such systems considers the traditional ability of the operator to determine the condition of the tool based on his/her experience and senses, e.g., vision and hearing. Sensor-based information and its deterministic nature is, of vital importance towards the development of reliable condition monitoring systems
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