Abstract
Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. This is typically accomplished using indirect TCM methods that synthesize the information collected from one or more sensors to estimate tool condition based on machine learning approaches. Among the many sensor types available for conducting TCM, motor current sensors offer numerous advantages, in that they are inexpensive, easily installed, and have no effect on the milling process. Accordingly, this study proposes a new TCM method employing a few appropriate current sensor signal features based on the time, frequency, and time - frequency domains of the signals and an advanced monitoring model based on an improved kernel extreme learning machine (KELM). The selected multi-domain features are strongly correlated with tool wear condition and overcome the loss of useful information related to tool condition when employing a single domain. The improved KELM employs a two-layer network structure and an angle kernel function that includes no hyperparameter, which overcome the drawbacks of KELM in terms of the difficulty of learning the features of complex nonlinear data and avoiding the need for preselecting the kernel function and its hyperparameter. The performance of the proposed method is verified by its application to the benchmark NASA milling dataset and separate TCM experiments in comparison with existing TCM methods. The results indicate that the proposed TCM method achieves excellent monitoring performance using only a few key signal features of current sensors.
Highlights
Milling is a common and efficient machining operation employed in modern industrial manufacturing for fabricating various mechanical parts, such as flat surfaces, grooves, threads, and other complex geometric shapes
Current sensors are deemed most appropriate due to their low cost and simple installation that has no effect on the milling process, while the selected multidomain features, which are strongly correlated with tool wear condition, overcome the drawbacks associated with the use of current signals described in Subsection 2.1 and the loss of useful information related to tool condition when employing a single domain
This study proposed a tool condition monitoring (TCM) method employing a few key current sensor signal features based on the time, frequency, and time-frequency domains of the signals and an advanced monitoring model based on an improved kernel extreme learning machine (KELM) to achieve excellent TCM performance for monitoring milling processes
Summary
Milling is a common and efficient machining operation employed in modern industrial manufacturing for fabricating various mechanical parts, such as flat surfaces, grooves, threads, and other complex geometric shapes. Cutting tools are key components in machine milling operations that are inevitably subject to wear during milling and present conditions that vary over their effective lifetimes [1]. Konstantinos et al [2] and Karandikar et al [3] have determined that cutting tools are typically used for only 50%–80% of their effective lifetimes owing to excessive tool wear and breakage (i.e., tool faults). These tool faults are major causes of unscheduled downtime in milling processes and typically account for 7%–20% of the total downtime [4]. Tool condition monitoring (TCM) has become an essential task in industrial milling processes for scheduling operations based on objective tool condition evaluations [7]
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