Nickel-Based Superalloys (NBSAs) are widely used for components subjected to high-temperature applications due to their excellent mechanical strength, toughness, and corrosion resistance. Despite favorable properties, NBSAs work-harden during machining, resulting in acute temperature rise at the cutting edge, severe plastic deformation, and rapid tool wear. The lower thermal conductivity, intense friction at the chip-tool interface, chemical affinity with tool material, and temperature gradients typically lead to abrupt crater formation or cutting-edge chipping in addition to rapid flank wear. Three distinct phenomena characterize tool wear during end milling of NBSAs; rapid flank wear, abrupt crater formation, and cutting-edge chipping. The continued use of worn or damaged cutting tools leads to poor surface finish and, eventually, catastrophic failures, resulting in significant machine downtime. As each tool wear condition has a unique mitigation strategy, timely identification and classification are imperative to implement solutions that minimize wear and guide tool replacement.

In recent years, the augmentation of vision-based systems with pre-trained Convolutional Neural Networks (CNNs) has shown great promise in failure identification and classification tasks. The present work develops an image-based classification model using a pre-trained CNN, Efficient-Net-b3, for identifying three tool wear conditions during end milling of Inconel 718 (IN718). The network training uses labeled image datasets that capture various tool wear characteristics generated using end-milling experiments. The extensive training dataset requirement of the CNN was met using image augmentation techniques by varying the brightness, contrast, and orientation of the captured images. The prediction abilities of the algorithm were corroborated by validating the model on a validation dataset and further testing on new unseen datasets. It has been shown that Efficient-Net-b3 demonstrates robust prediction accuracy for all three tool wear conditions. The proposed classification model can be further employed for developing an on-machine vision-based tool wear classification system.

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