CLASSIFICATION of GRINDING BURNS in BEARINGS with TRANSFER LEARNING
Künye
Ceylan, N., Sezgin Kaçar, Chu, Y., & Alotaibi, N. D. (2023). CLASSIFICATION OF GRINDING BURNS IN BEARINGS WITH TRANSFER LEARNING. Fractals, 31(06). https://doi.org/10.1142/s0218348x23400984 Özet
Grinding is used to improve surface roughness and dimensioning precision in the metal industry. A large amount of heat is released during grinding. Most of this heat is transferred to the workpiece. In this case, a grinding burn may occur on the workpiece. Grinding burn is a significant issue in the production of bearings. If a burn occurs on the workpiece during grinding, the surface quality deteriorates and the internal structure and mechanical qualities of the material are adversely affected. Therefore, detecting grinding burn is critical for bearing manufacturers. In this study, during the grinding of the bearing parts, the machine conditions were changed in accordance with the real grinding scenario, and burnt and non-burned bearing data were obtained with the acoustic emission sensor. Then, time-frequency representations were obtained from these data with the continuous wavelet transform. These images have been classified in the GoogLeNet Network by transfer learning. Combinations of faulty/ normal data processed under different machine settings and combinations of faulty/ normal data processed with the same machine parameters were classified with the proposed method and compared. Faulty bearings processed with the same machine characteristics were detected with 100% accuracy using the proposed method. This percentage gives a reliable result for bearing producers. This study contributes to the literature in three ways: (a) It is based on data collected under real-world grinding situations. (12 different machine settings were employed.) (b) Various machine conditions were compared. (c) Faulty bearings were detected with high accuracy. © 2023 The Author(s).
WoS Q Kategorisi
Q1Kaynak
FractalsCilt
31Sayı
6Koleksiyonlar
İlgili Öğeler
Başlık, yazar, küratör ve konuya göre gösterilen ilgili öğeler.
-
Comparison of EEG- Based Deep Neural Network Classifiers for Emotion Recognition using Selected Electrodes
Gul, Ayse Nur Ay; Altuntas, Abdullah (Institute of Electrical and Electronics Engineers Inc., 2023)In this study, SVM and DNN models were employed to classify participants' emotional states using the publicly available SEED dataset, achieving impressive accuracy rates of 79.8% for DNN and 79.4% for SVM, surpassing the ... -
A hyper parameterized artificial neural network approach for prediction of the factor of safety against liquefaction
Kurnaz, Talas Fikret; Erden, Caner; Kökçam, Abdullah Hulusi; Dağdeviren, Uğur; Demir, Alparslan Serhat (Elsevier B.V., 2023)Soil liquefaction during earthquakes is a complex geotechnical engineering problem. Although various analytical approaches exist for predicting liquefaction risk, their limitations have led researchers to explore using ... -
Makine öğrenmesi yöntemleri ile tiroit hastalığının teşhisi
Yıldız, Abdulbaki (Sakarya Uygulamalı Bilimler Üniversitesi, 2019)[Abstract Not Available]