Tool-Condition Diagnosis Model with Shock-Sharpening Algorithm for Drilling Process

Abstract

Fault diagnosis systems are used to improve the productivity and reduce the costs of the manufacturing process. However, the feature variables in existing systems are extracted based on the classification performance of the final model, thereby limiting their applications to models with different conditions. This paper proposes an algorithm to improve the characteristics of feature variables by considering the cutting conditions. Regardless of the frequency band, the noise of the measurement data was reduced through an oversampling method, setting a window length through a cutter sampling frequency, and improving its sensitivity to shock signal. An experiment was subsequently performed to confirm the performance of the model. Using normal and wear tools on AI7075 and SM45C, the diagnosis accuracies were 97.1% and 95.6%, respectively, with a reduction of 85% and 83%, respectively, in the time required to develop a diagnosis model. Therefore, the proposed algorithm reduced the model computation time and developed a model with high accuracy by enhancing the characteristics of the feature variable. The results of this study can contribute significantly to the establishment of a high-precision monitoring system for various processing processes.

Enhancing Diagnosis of Rotating Elements in Roll-to-Roll Manufacturing Systems through Feature Selection Approach Considering Overlapping Data Density and Distance Analysis

“…By focusing on these pertinent features, feature extraction effectively eliminates noise and irrelevant information, enabling more precise and reliable data analysis [ 37 ]. Additionally, since feature extraction is a preliminary step to feature selection, improving the effectiveness can be achieved by extracting pertinent and important features in advance [ 65 ]. Following the filtration of the selected data using the DNF number, a compilation of significant industrial statistical features and time-domain statistical variables [ 66 , 67 ] were extracted, where X is the vector of vibration data, and N is a window size as listed in Table 1 .…”