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도경민(Gyungmin Toh),김완승(Wanseung Kim),권재수(Jaesoo Gwon),박준홍(Junhong Park) 한국소음진동공학회 2020 한국소음진동공학회 논문집 Vol.30 No.2
This paper presents a novel method for measuring the clamping force using sound that occurs during bolt fastening. The resonance frequency of the bolt increases with the progress of the fastening process. This characteristic change is utilized as the feature analyzed by a convolutional neural network (CNN). The clamping force is measured using a load cell, and is then used during labeling for classification. To measure the radiated noise, a microphone is installed near the fastening part. In addition, a signal-processing method is proposed to apply the measurement to deep-learning classification and perform data augmentation. The CNN architecture was modeled, and the fastening force was determined using the classification method. The estimated value was compared with the actual load cell measurements.