A simple online tool condition monitoring system using artificial neural networks. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Mukherjee, A; Das, S
• Is Part Of: IOP conference series. Volume 1080:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: machining -- turning -- online -- tool condition monitoring -- tool wear -- spindle speed -- artificial neural networks -- ANN -- NN; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1080/1/012021
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: During machining occurrence of tool wear is a common phenomenon. As tool wear increases, rubbing of flank surface and workpiece also gets increased. Then, the desired quality of workpiece is not possible to achieve. To minimize this loss of quality, a cutting tool should be changed or ground after reaching certain amount of average flank wear (0.3 mm for uniform and 0.6 mm or non-uniform flank wear). For the detection of worn out state of a cutting tool, condition monitoring is required. During past decades, a lot of research works had been done on both offline and online monitoring of cutting tool. Most of the researchers used high cost setups and sensors for wear detection purpose. In this work, tool wear is detected using spindle speed as the wear detection parameter. Artificial Neural Networks is used as data analysing tool. Back propagation algorithm is used as learning algorithm. Results show that proposed methodology is capable to detect tool wear satisfactorily.