Tool wear monitoring in turning using image processing techniques

Autor: K.M. Patel, M.A. Makhesana, P.J. Bagga, Kavan Patel
Rok vydání: 2021
Předmět:
Zdroj: Materials Today: Proceedings. 44:771-775
ISSN: 2214-7853
Popis: Observing the tool wear is very important in metal cutting industry as it affects the dimensional accuracy and surface quality of work part. Generally, tool wear is measured directly using an optical microscope which is an offline and time-consuming technique. In the indirect technique, the parameter such as force, surface finish, temperature etc. that is affected by tool wear is measured and correlated with tool life. It has been observed that measurement of tool wear using image processing techniques has a lot of scope. Digital image processing techniques automate the task of measurement and monitoring of tool wear. A novel on-line tool wear measuring algorithm is proposed to monitor tool wear using machine vision. This work is focused on implementing digital image processing techniques to automate the task of two dimensional flank wear measurement. The tool wear images are acquired by a camera, and the wear boundaries are recognized by image pre-processing, threshold segmentation and edge detection to measure the wear on tool. The wear values obtained by proposed method are compared with manual measurement. The maximum flank wear with proposed algorithm and manual measurement is found as 730 and 718 µm respectively. It is found that the maximum variation in the results obtained by algorithm and manual measurement is 4.98%. This shows the higher detection accuracy for on-line tool wear monitoring by the proposed algorithm.
Databáze: OpenAIRE
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