Abstract
Tremendous use of sophisticated computer aided manufacturing systems necessitates monitoring of tools. Monitoring of tools facilitates to reduce machine tool downtime, increases quality of the product, provide better surface finish and reduces cost. Sophisticated digital image processing algorithms and availability of high resolution cameras has enabled automated monitoring of tools. To exactly predict the tool wear is a challenging task due to its complexity involved in the algorithm that facilitates extraction of features. In this paper, tool wear monitoring through texture feature extraction using Gabor filter is presented. Gabor filter has demonstrated better multi resolution properties that facilitate texture feature extraction. Statistical parameters such mean, standard deviation, variance, skew and kurtosis are calculated to measure the tool wear. Experimental results demonstrate better tool wear prediction as compared to other algorithms.
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Ambadekar, P., Choudhari, C. (2019). Application of Gabor Filter for Monitoring Wear of Single Point Cutting Tool. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore. https://doi.org/10.1007/978-981-13-9181-1_21
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DOI: https://doi.org/10.1007/978-981-13-9181-1_21
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