Transfer Learning-Based Condition Monitoring of Single Point Cutting Tool. (15th July 2022)

• Record Type: Journal Article
• Authors: Naveen Venkatesh, S.; Arun Balaji, P.; Elangovan, M.; Annamalai, K.; Indira, V.; Sugumaran, V.; Mahamuni, Vetri Selvi
• Other Names: Bhardwaj Arpit Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-07-15
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/3205960
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : Machining activities in recent times have shifted their focus towards tool life and tool wear. Cutting tools have been utilized on a daily basis and play a vital role in manufacturing industries. Prolonged and incessant operation of the cutting tool can lead to wear and tear of the component, thereby compromising the dimensional accuracy. The condition of a tool is estimated based upon the surface quality of the machined component, condition of the machine, and the rate of production. Maintaining the tool health plays a vital role in enhancing the productivity of manufacturing industries. Numerous efforts were experimented by the researchers to maintain the tool health condition. The drawbacks of conventional diagnostic techniques include requirement of high level of human intelligence and professional expertise on the field, which led the researchers to develop intelligent and automatic diagnostic tools. There are many techniques suggested by researchers to detect the condition of single point cutting tool. This article proposes the use of transfer learning technology to detect the condition of single point cutting tool. First, the vibration signals were collected from the cutting tool and plots were made which will work as input to the deep learning algorithms. The deep learning algorithms have the capability to learn from the plots of vibration signals and classify the state of the single point cutting tool. In this work, the pretrained networks such as VGG-16, AlexNet, ResNet-50, and GoogLeNet were employed to identify the state of the cutting tool. In the pretrained networks, the effect of hyperparameters such as batch size, solver, learning rate, and train-test split ratio was studied, and the best performing network was suggested for tool condition monitoring.