A novel approach for tool condition monitoring based on transfer learning of deep neural networks using time–frequency images
Pages 1159 - 1171
Abstract
Traditional tool condition monitoring methods developed in an ideal environment are not universal in multiple working conditions considering different signal sources and recognition methods. This paper presents a novel tool condition monitoring approach that packages deep learning networks for accurate condition recognition with a cocktail solver library. First, the multisource signals from the machining process are collected and sequenced as the input of the cocktail solver library. The machining signals are transformed into a series of two-dimensional images by a continuous wavelet transform. In addition, ten pretrained networks with transfer learning are rapidly transferred with a finetuning operation, which contributes to a set of monitor networks. Three major processes are integrated by the cocktail solver library, which is the choosing dataset process for multi-signals, the training option process for network training parameters, and the network package process for the basic monitoring model. A Bayesian optimization method is employed to handle a tradeoff for these three processes to improve the prediction accuracy and reduce the recognition time. In the testing experiment, the milling datasets are used to train the model, and the results show that the accuracy of the model proposed in this paper can exceed 90%. The proposed method was also compared with other traditional methods to verify its effectiveness.
References
[1]
Agogino, A., & Goebel, K. (2007). Milling data set. BEST Lab, UC Berkeley. NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA. Retrieved from http://ti.arc.nasa.gov/project/prognostic-data-repository
[2]
Ardakani AA, Kanafi AR, Acharya UR, Khadem N, and Mohammadi A Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks Computers in Biology and Medicine 2020 121 103795
[3]
Aslan A Optimization and analysis of process parameters for flank wear, cutting forces and vibration in turning of AISI 5140: A comprehensive study Measurement 2020 163 107959
[4]
Bhat NN, Dutta S, Vashisth T, Pal S, Pal SK, and Sen R Tool condition monitoring by SVM classification of machined surface images in turning The International Journal of Advanced Manufacturing Technology 2016 83 9–12 1487-1502
[5]
Cabezas M, Kentsch S, Tomhave L, Gross J, Caceres MLL, and Diez Y Detection of invasive species in wetlands: Practical DL with heavily imbalanced data Remote Sensing. 2020 12 3431
[6]
Cao P, Zhang S, and Tang J Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning IEEE Access 2018 6 26241-26253
[7]
Cao X, Chen B, Yao B, and He W Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification Computers in Industry 2019 106 71-84
[8]
Cheng C, Zhou B, Ma G, Wu D, and Yuan Y Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis with unlabeled or insufficient labeled data Neurocomputing 2020 409 35-45
[9]
Dara, S., & Tumma, P. (2018). Feature extraction by using deep learning: A survey. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 1795–1801.
[10]
Ferrag MA, Shu L, Friha O, and Yang X Cyber security intrusion detection for agriculture 4.0: Machine learning-based solutions, datasets, and future directions IEEE/CAA Journal of Automatica Sinica 2021 9 3 407-436
[11]
Hera A and Hou Z Application of wavelet approach for ASCE structural health monitoring benchmark studies Journal of Engineering Mechanics 2004 130 1 96-104
[12]
International Organization for Standardization. (1989). Tool life testing in milling—part 2: End milling ISO-8688-2. https://www.iso.org/standard/16092.html
[13]
Iwendi C, Mohan S, Khan S, Ibeke E, Ahmadian A, and Ciano T Covid-19 fake news sentiment analysis Computers and Electrical Engineering 2022 101 107967
[14]
Lee WJ, Wu H, Huang A, and Sutherland JW Learning via acceleration spectrograms of a DC motor system with application to condition monitoring The International Journal of Advanced Manufacturing Technology 2020 106 3–4 803-816
[15]
Li Y, Mou W, Li J, Liu C, and Gao J An automatic and accurate method for tool wear inspection using grayscale image probability algorithm based on Bayesian inference Robotics and Computer-Integrated Manufacturing 2021 68 102079
[16]
Li X, Liu X, Yue C, Liang SY, and Wang L Systematic review on tool breakage monitoring techniques in machining operations International Journal of Machine Tools & Manufacture 2022 176 103882
[17]
Liao Y, Ragai I, Huang Z, and Kerner S Manufacturing process monitoring using time-frequency representation and transfer learning of deep neural networks Journal of Manufacturing Processes 2021 68 231-248
[18]
Marei M, Zaatari SE, and Li W Transfer learning enabled convolutional neural networks for estimating health state of cutting tools Robotics and Computer-Integrated Manufacturing 2021 71 102145
[19]
Miao R, Gao Y, Ge L, Jiang Z, and Zhang J Online defect recognition of narrow overlap weld based on two-stage recognition model combining continuous wavelet transform and convolutional neural network Computers in Industry 2019 112 103115
[20]
Mohanraj T, Shankar S, Rajasekar R, Sakthivel NR, and Pramanik A Tool condition monitoring techniques in milling process—A review Journal of Materials Research and Technology 2020 9 1 1032-1042
[21]
Mostaghimi H, Park CI, Kang G, Park SS, and Lee DY Reconstruction of cutting forces through fusion of accelerometer and spindle current signals Journal of Manufacturing Processes 2021 68 990-1003
[22]
Nirwan NW and Ramani HB Condition monitoring and fault detection in roller bearing used in rolling mill by acoustic emission and vibration analysis Materials Today: Proceedings 2022 51 344-354
[23]
Opitz, J., & Burst, S. (2019). Macro F1 and Macro F1.
[24]
Pardalos PM, Zhigljavsky A, and Žilinskas J Advances in stochastic and deterministic global optimization 2016 Springer International Publishing
[25]
Pimenov DY, Bustillo A, Wojciechowski S, Sharma VS, Gupta MK, and Kuntoğlu M Artificial intelligence systems for tool condition monitoring in machining: Analysis and critical review Journal of Intelligent Manufacturing 2022
[26]
Sarıkaya M, Gupta MK, Tomaz I, Pimenov DY, Kuntoğlu M, Khanna N, et al. A state-of-the-art review on tool wear and surface integrity characteristics in machining of superalloys CIRP Journal of Manufacturing Science and Technology 2021 35 624-658
[27]
Shahriari B, Swersky K, Wang Z, Adams RP, and de Freitas N Taking the human out of the loop: a review of bayesian optimization Proceedings of the IEEE 2016 104 1 148-175
[28]
Sun H, Zhang J, Mo R, and Zhang X In-process tool condition forecasting based on a deep learning method Robotics and Computer-Integrated Manufacturing 2020 64 101924
[29]
Traini E, Bruno G, and Lombardi F Tool condition monitoring framework for predictive maintenance: A case study on milling process International Journal of Production Research 2021 59 23 7179-7193
[30]
Wu J, Xu X, Liu C, Deng C, and Shao X Lamb wave-based damage detection of composite structures using deep convolutional neural network and continuous wavelet transform Composite Structures 2021 276 114590
[31]
Xu D, Liao Z, Axinte D, and Hardy M A novel method to continuously map the surface integrity and cutting mechanism transition in various cutting conditions International Journal of Machine Tools and Manufacture 2020 151 103529
[32]
Xu L, Huang C, Li C, Wang J, Liu H, and Wang X Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining Journal of Intelligent Manufacturing 2021 32 1 77-90
[33]
Zhou C, Guo K, and Sun J An integrated wireless vibration sensing tool holder for milling tool condition monitoring with singularity analysis Measurement 2021 174 109038
[34]
Zhu J, Chen N, and Shen C A new deep transfer learning method for bearing fault diagnosis under different working conditions IEEE Sensors Journal 2020 20 15 8394-8402
Recommendations
Breast cancer masses classification using deep convolutional neural networks and transfer learning
AbstractWith the recent advances in the deep learning field, the use of deep convolutional neural networks (DCNNs) in biomedical image processing becomes very encouraging. This paper presents a new classification model for breast cancer masses based on ...
Deep representation-based transfer learning for deep neural networks
AbstractIn recent years, deep neural networks (DNNs) have become the de facto models for practically all visual tasks and most temporal analysis tasks due to the abundance of available labeled data and advances in computational resources. Deep ...
Highlights- A deep representation-based transfer learning method is proposed for knowledge transfer between deep neural networks.
Transfer learning approach in deep neural networks for uterine fibroid detection
Convolutional neural network (CNN), a class of deep neural network, takes images as input and automatically extracts features for effective class prediction. The performance of CNN architecture is a major concern while dealing with fewer data. Traditional ...
Comments
Information & Contributors
Information
Published In
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 14 March 2023
Accepted: 21 February 2023
Received: 16 October 2022
Author Tags
Qualifiers
- Research-article
Funding Sources
- National Key Laboratory of Science and Technology on Helicopter Transmission
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 15 Oct 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in