1021 KB25 KB199920252020Nguyen, Van ThienNguyen, VietHungPham, VanTrinhštevilka:4letnik:66str. 227-234ISSN:0039-2480COBISSID_HOST:16176899URN:URN:NBN:SI:doc-MMX4J1QWenZveza strojnih inženirjev in tehnikov Slovenije etc.Strojniški vestnikcast irončelno rezkanjedeep learning network (DLN)face millinggloboke nevronske mreže (DLN)identifikacijaobraba orodjasoftmaxstacked auto-encoder (SAE)tool wearzloženi samokodirnik (SAE)železova litinaDeep stacked auto-encoder network based tool wear monitoring in the face milling process|Tool wear identification plays an important role in improving product quality and productivity in the manufacturing industry. The actual tool wear status with input cutting parameters may cause different levels of spindle vibration during the machining process. This research proposes an architecture comprising a deep learning network (DLN) to identify the actual wear state of machining tool. Firstly, data on spindle vibration signals are obtained from an acceleration sensor. The data are then pre-processed using the fast Fourier transform (FFT) method to reveal the relevant outstanding features in the frequency domain. Finally, the DLN is constructed based on stacked auto-encoders (SAE) and softmax, which is trained with the input data on the vibration features of the respective tool wear state. This DLN architecture is then used to identify the actual wear statuses of machining tool. The experimental results from the collected data show that the proposed DLN architecture is capable of identifying actual tool wear with high accuracyTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Fakulteta za strojništvo