Abstract
Tool wear monitoring is necessary for cost reduction and productivity improvement in the machining industry. Machine learning has been proven to be an effective means of tool wear monitoring. Feature engineering is the core of the machining learning model. In complex parts milling, cutting conditions are time-varying due to the variable engagement between cutting tool and the complex geometric features of the workpiece. In such cases, the features for accurate tool wear monitoring are tricky to select. Besides, usually few sensors are available in an actual machining situation. This causes a high correlation between the hand-designed features, leading to the low accuracy and weak generalization ability of the machine learning model. This paper presents a tool wear monitoring method for complex part milling based on deep learning. The features are pre-selected based on cutting force model and wavelet packet decomposition. The pre-selected cutting forces, cutting vibration and cutting condition features are input to a deep autoencoder for dimension reduction. Then, a deep multi-layer perceptron is developed to estimate the tool wear. The dataset is obtained with a carefully designed varying cutting depth milling experiment. The proposed method works well, with an error of 8.2% on testing samples, which shows an obvious advantage over the classic machine learning method.
Highlights
Tool wear is a cost driver in machining that affects quality and productivity and adds unscheduled downtime for tool changes and the reworking of damaged parts
Tool wear monitoring methods can be categorized into two main groups: direct and indirect methods [1]
Indirect methods are the most widely used in tool wear monitoring because they are easy to conduct in real time and can obtain acceptable accuracy by using a proper monitoring signals and modeling method
Summary
Tool wear is a cost driver in machining that affects quality and productivity and adds unscheduled downtime for tool changes and the reworking of damaged parts. Accurate tool wear monitoring is necessary to avoid these unnecessary costs. Tool wear monitoring methods can be categorized into two main groups: direct and indirect methods [1]. Direct methods measure the actual wear value with optical, laser or ultrasonic devices. Direct methods measure tool wear precisely, they are difficult to implement in real-time machining because, in most cases, the tool wear area is unreachable due to the occlusion of workpiece structure and flood coolant. Indirect methods monitor in-process physical parameters to evaluate wear state, such as force, vibration, acoustic emission, current, power and temperature signals [2]. Indirect methods are the most widely used in tool wear monitoring because they are easy to conduct in real time and can obtain acceptable accuracy by using a proper monitoring signals and modeling method
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