Loading...
Implementation of transformer-based deep learning architecture for the development of surface roughness classifier using sound and cutting force signals
Bhandari, Binayak Park, Gijun Shafiabady, Niusha
Bhandari, Binayak
Park, Gijun
Shafiabady, Niusha
Abstract
Enhanced machining quality, including the appropriate surface roughness of the machined parts, is the focus of many industries. This paper proposes and implements transformer-based deep learning (DL) architecture for machining roughness classification for the end-milling operation using cutting force and machining sound data. To increase the accuracy of the classification outcomes, audio feature extraction techniques—Mel-spectrogram and Mel-frequency cepstral coefficients (MFCCs)—were incorporated with the DL model. To measure and demonstrate the proposed model’s performance, a number of experiments were conducted by training the models on 0–30 s machining data, including end mill-workpiece impact at the beginning of the experiment and 10–40 s machining data. Based on the outcomes’ accuracies, four DL models were designed and the number of parameters, maximum epoch required for convergence, and training, validation and testing accuracies were compared for each model. DL models trained on 10–40 s machining data achieved over 90% validation and test accuracies, suggesting that the cutting force and machining data after reaching steady-state performs better than data from the beginning of machining operation. Confusion matrices were plotted for the inference results of each model to observe the prediction accuracy visually. The proposed method will be able to predict machining surface quality with high accuracy saving time and cost for post-operations.
Keywords
transformers-based deep learning, Taguchi method, surface roughness, machining, classification, smart factory
Date
2023
Type
Journal article
Journal
Neural Computing and Applications
Book
Volume
35
Issue
18
Page Range
13275-13292
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled