Ross, Nimel SwornaShibi, C. SherinMustafa, Sithara MohamedGupta, Munish KumarKorkmaz, Mehmet ErdiSharma, Vishal S.Li, Z.2024-09-292024-09-2920231530-437X1558-1748https://doi.org/10.1109/JSEN.2023.3269529https://hdl.handle.net/20.500.14619/6151A crucial method of maintenance in the manufacturing industry is machine vision-based fault diagnostics and condition monitoring of machine tools. The friction that occurs between the tool and the workpiece has a greater influence on the surface properties of the material. Effective problem diagnosis is necessary for machine systems to continue operations safely. Data-driven approaches have recently exhibited great promise for intelligent fault diagnosis. Unfortunately, the data collected under real-world conditions may be imbalanced, making diagnosis difficult. In dry, minimum quantity lubrication (MQL), and cryogenic circumstances, the method of failure detection of the proposed design is novel. The purpose of this interrogation is to evaluate the roughness profiles obtained from the machined surfaces and class separation. Markov transition field (MTF) is adopted to encode the surface profiles. In addition to this, conditional generative adversarial network (CGAN) for augmentation and bidirectional long-short term memory (BLSTM), multilayer perceptron (MLP), and 2-D-convolutional neural network (CNN) models are used for surface profile classification and correlation with process parameters. According to the study's finding, the 2-D-CNN was significantly more accurate than the models in predicting surface profiles, with an average accuracy of above 99.6% in both training and testing. In the limelight, the suggested approach can demonstrate to be quite useful for categorizing and proposing appropriate machining circumstances, specifically in situations with minimal data.eninfo:eu-repo/semantics/closedAccessSurface treatmentMachiningSurface morphologyMarkov processesSurface roughnessRough surfacesPredictive modelsConditional generative adversarial network (CGAN)cryogenicdeep learning (DL)machiningMarkov transition field (MTF)Article10.1109/JSEN.2023.32695292-s2.0-851596755611363912Q11362923WOS:001014626700120Q1