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- W3040712768 abstract "Manufacturing domain exists as a least intervened area about deep learning methods. This work intends to validate the scope of Convolutional Neural Network (CNN) in classifying the images of faced parts with respect to the feed with which the specimens are machined. Feed rates inherently contribute to the lay pattern of the machined face. A pre-trained model was used to receive images of machined specimens and predict the feed with which it was machined. Images were captured using a microscopic camera and pre-processed with care not to drop lay patterns that may become the decision factor. Four thousand images belonging to two classes were used to train the network which was divided in the ratio 8:2 in the training and test sets. The classification was binary in nature based on the two feed rates with which specimens were machined. The accuracy of the developed model when used for prediction was 95%." @default.
- W3040712768 created "2020-07-16" @default.
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- W3040712768 date "2020-08-14" @default.
- W3040712768 modified "2023-09-24" @default.
- W3040712768 title "Feed Rate Based Classification of Machined Parts using Convolutional Neural Network" @default.
- W3040712768 cites W1978618432 @default.
- W3040712768 doi "https://doi.org/10.1201/9780429353628-55" @default.
- W3040712768 hasPublicationYear "2020" @default.
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