Om M. Khare

Revati M. Wahul, Aditya M. Rahalkar, Om M. Khare et al.

Tool Condition Monitoring (TCM) is vital for maintaining productivity and product quality in machining. This study leverages machine learning to analyze real-time force signals collected from experiments under various tool wear conditions. Statistical analysis and feature selection using decision trees were followed by classification using a K-Nearest Neighbors (KNN) algorithm, with hyperparameter tuning to enhance performance. While machine learning has been widely applied in TCM, interpretability remains limited. This work introduces a KNN-based white-box model that enhances transparency in decision-making by revealing how features influence classification. The model not only detects tool wear but also provides insights into the reasoning behind each decision, enabling manufacturers to make informed maintenance choices.

Om M. Khare, Shubham Gandhi, Aditya M. Rahalkar et al.

Effective detection of road hazards plays a pivotal role in road infrastructure maintenance and ensuring road safety. This research paper provides a comprehensive evaluation of YOLOv8, an object detection model, in the context of detecting road hazards such as potholes, Sewer Covers, and Man Holes. A comparative analysis with previous iterations, YOLOv5 and YOLOv7, is conducted, emphasizing the importance of computational efficiency in various applications. The paper delves into the architecture of YOLOv8 and explores image preprocessing techniques aimed at enhancing detection accuracy across diverse conditions, including variations in lighting, road types, hazard sizes, and types. Furthermore, hyperparameter tuning experiments are performed to optimize model performance through adjustments in learning rates, batch sizes, anchor box sizes, and augmentation strategies. Model evaluation is based on Mean Average Precision (mAP), a widely accepted metric for object detection performance. The research assesses the robustness and generalization capabilities of the models through mAP scores calculated across the diverse test scenarios, underlining the significance of YOLOv8 in road hazard detection and infrastructure maintenance.