Abhishek D. Patange

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.

Aditya M. Medhi, Abhishek D. Patange, Sujit S. Pardeshi et al.

The movement for open-design focuses on the creation of machines, physical systems, and products using design information shared publicly. It consists of the development of systems incorporating open-source hardware and software which can be easily/freely customized and implemented. Generally, this movement is adopted through the Internet and usually executed without economic recompense. The aim and idea of this movement is similar to the open-source movement, however is employed for designing & developing physical systems instead of software system alone. This design necessitates co-creating the end product, which is expected to be designed by the users, in place of an outdoor investor for example a private business. In tune with this, the comprehensive review is carried out wherein a variety of contemporary systems driven by open-design movement for diverse applications is discussed.

Abhishek Patange, Sharat Chidambaran, Prabhat Shankar et al.

Slug formation in oil and gas pipelines poses significant challenges to operational safety and efficiency, yet existing detection approaches are often offline, require domain expertise, and lack real-time interpretability. We present an interactive application that enables end-to-end data-driven slug detection through a compact and user-friendly interface. The system integrates data exploration and labeling, configurable model training and evaluation with multiple classifiers, visualization of classification results with time-series overlays, and a real-time inference module that generates persistence-based alerts when slug events are detected. The demo supports seamless workflows from labeled CSV uploads to live inference on unseen datasets, making it lightweight, portable, and easily deployable. By combining domain-relevant analytics with novel UI/UX features such as snapshot persistence, visual labeling, and real-time alerting, our tool adds significant dissemination value as both a research prototype and a practical industrial application. The demo showcases how interactive human-in-the-loop ML systems can bridge the gap between data science methods and real-world decision-making in critical process industries, with broader applicability to time-series fault diagnosis tasks beyond oil and gas.

Abhishek D. Patange, Sharad S. Mulik, R. Jegadeeshwaran et al.

Survival in remote snow bounded areas is unsafe and risky for mankind. Many problems like arthritis, frostbite, asthma, starvation can caused and lead to death. Indian Military provides transportation vehicles which are heavily built and needs manpower for monitoring. Hence it necessitates facilitating compact transportation to fulfill all requirements. This research aimed at design and analysis of mobile unmanned vehicle for transportation & providing medical help, food and other essential things necessary for surviving in such areas. This can also be used for military services to save the life of solider with less risk. It is typical medium weight, high speed vehicle which carries up to 35 kg load and can negotiate through loose snow, rough terrain with use of caterpillar track. The noteworthy feature of the vehicle is that it constitutes of spiral blades and V shape snowplow to make its way through snow. Hence it will repel the snow in outward direction for self-extraction. It also incorporates skis and hubs for changing the direction and smooth suspension. 3D model of the vehicle is drafted in CATIA and structural analysis is carried out in ANSYS. Control system design and mechatronics integration is proposed to develop the prototype by assembling various components.