Abstract
To accurately and efficiently detect tool wear values during production and processing activities, a new online detection model is proposed called the Residual Dense Network (RDN). The model is created with two main steps: Firstly, the time-domain signals for a cutting tool are obtained (e.g., using acceleration sensors); these signals are processed to denoise and segmented to provide a larger number of uniform samples. This processing helps to improve the robustness of the model. Secondly, a new deep convolutional neural network is proposed to extract features adaptively, by combining the idea of a recursive residual network and a dense network. Notably, this method is specifically tailored to the tool wear value detection problem. In this way, the limitations of traditional manual feature extraction steps can be avoided. The experimental results demonstrate that the proposed method is promising in terms of detection accuracy and speed; it provides a new way to detect tool wear values in practical industrial scenarios.
Highlights
IntroductionWith economic globalization and the emergence of Industry 4.0, it has become increasingly important to enhance efficiency and promote business transformation of enterprises
Manufacturing is a critical part of a national economy
We mainly focus on the impact of the BN batch normalization and the Dropout regularization strategy on the network convergence performance in Residual Dense Network (RDN)
Summary
With economic globalization and the emergence of Industry 4.0, it has become increasingly important to enhance efficiency and promote business transformation of enterprises. Research in the area of prognostics and health management (PHM) plays a pivotal role in promoting the transformation of the production industry [1]. The goal of such research is to further optimize production processes; recent results have allowed the maintenance of industrial equipment to evolve from expert, experience based methods to automated adaptive learning methods. These intelligent detection methods can help to improve the efficiency of manufacturing processes from a number of perspectives
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
