Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks
Authors
Keywords
-
Journal
Production Engineering-Research and Development
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-02-22
DOI
10.1007/s11740-022-01113-2
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Exploitation of force displacement curves in blanking—feature engineering beyond defect detection
- (2021) Christian Kubik et al. The International Journal of Advanced Manufacturing Technology
- Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network
- (2021) Chih-Yung Huang et al. SENSORS
- Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking
- (2021) Christian Kubik et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Transfer learning enabled convolutional neural networks for estimating health state of cutting tools
- (2021) Mohamed Marei et al. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
- Review of tool condition monitoring in machining and opportunities for deep learning
- (2020) G. Serin et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Industrial Remaining Useful Life Prediction by Partial Observation Using Deep Learning With Supervised Attention
- (2020) Xiang Li et al. IEEE-ASME TRANSACTIONS ON MECHATRONICS
- A Comprehensive Survey on Transfer Learning
- (2020) Fuzhen Zhuang et al. PROCEEDINGS OF THE IEEE
- Manufacturing of advanced smart tooling for metal forming
- (2019) Jian Cao et al. CIRP ANNALS-MANUFACTURING TECHNOLOGY
- Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process
- (2019) Xuefeng Wu et al. SENSORS
- An Intelligent Milling Tool Wear Monitoring Methodology Based on Convolutional Neural Network with Derived Wavelet Frames Coefficient
- (2019) Xincheng Cao et al. Applied Sciences-Basel
- Overview and comparison of different sensor positions and measuring methods for the process force measurement in stamping operations
- (2018) Peter Groche et al. MEASUREMENT
- Audio signal analysis for tool wear monitoring in sheet metal stamping
- (2017) Indivarie Ubhayaratne et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A survey of deep neural network architectures and their applications
- (2017) Weibo Liu et al. NEUROCOMPUTING
- Preliminary study for online monitoring during the punching process
- (2016) Delima Yanti Sari et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Prediction of tool wear in the blanking process using updated geometry
- (2016) Seunghyeon Cheon et al. WEAR
- Transfer Learning for Visual Categorization: A Survey
- (2015) Ling Shao et al. IEEE Transactions on Neural Networks and Learning Systems
- Improving the wear resistance of tools for stamping
- (2010) G. Straffelini et al. WEAR
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now