A Hybrid MADM Approach Integrating Fuzzy Logic and MOORA Prathamesh R. Potdar, Santosh B. Rane Multi Attribute Decision Making Theory Emerging Methods and Applications, 2026 In today's era of digitalization, problems have become more complex. Both organizations and individuals base their decisions on multiple criteria to achieve the optimal solutions. This process is known as Multi-Attributes Decision-Making (MADM), a subset of Multi-Criterial Decision-Making (MCDM).
Evaluating the wear and friction characteristics of heat-treated and as-cast ex-situ Al-Si7Mg/SiC composites under lubricated conditions Vishal Mehta, Anand Joshi, Prathamesh Potdar, Mayur Sutaria Discover Materials, 2025 The present study investigates wear and friction characteristics of Al-Si7Mg aluminum matrix composites (AMCs) reinforced with silicon carbide (SiC) in as-cast and heat-treated conditions, with a focus upon the influence of lubrication. The composites were developed by an ex-situ technique using a stir casting process. Tribological performance was evaluated using a pin-on-disk tribotester, varying the flow rate of lubricant, applied load, and sliding distance. To identify the significance of variables on wear and COF, ANOVA analysis was carried out. The findings showed that heat-treated composites outperformed as-cast samples with regard to wear resistance and COF, which was attributed to better interfacial bonding between the SiC particles and the Al-Si7Mg matrix. The predominant wear mechanisms were further validated by SEM-Scanning Electron Microscopy analysis of worn-out faces, which showed that heat-treated samples showed mild delamination and as-cast samples showed severe abrasive wear. Lubrication played an important role in decreasing wear by minimizing metal-to-metal contact and enhancing surface morphology. X-ray Diffraction (XRD) analysis recognized the primary phases, including aluminum (Al), silicon carbide (SiC), and a minor formation of aluminum carbide (Al₄C₃) in the composites. The findings highlight the potential of Al-Si7Mg/SiC AMC as favorable materials for applications in automotive, where reduced wear and lower COF are critical, such as in engine components, brake systems, and transmission parts. The tribological performance of Al-Si7Mg/SiC composites was demonstrated to be greatly improved by the combination of heat treatment and lubrication, offering a comprehensive understanding of their behavior in real-world applications.
RELIABILITY ANALYSIS FRAMEWORK FOR SECOND LIFE OF ELECTRIC VEHICLE BATTERIES IN SUSTAINABLE ENERGY STORAGE Krunal H. Ajmeri, Prathamesh R. Potdar Iet Conference Proceedings, 2025 Current need for sustainable energy storage solutions floats our attention to methods for reuse of retired electric vehicle (EV) batteries, called second life (SL) batteries. Since such batteries are so close to an entry into the power system, their reliability analysis is an essential prerequisite, which it provides the foundation for to decide both how long they can be operated and how they can be safe. A complete methodology to evaluate reliability aspects in second life EV batteries that are used for sustainable energy storage system is provided in the paper. There is a systematic way of evaluating reliability characteristics in the second life of EV batteries. This framework creates a procedure for a systematic evaluation of batteries with testing protocols and decision methods for assessing sustainability of batteries. Structured evaluation procedures are integrated in the system, which includes thorough testing standards and determination elements to evaluate potentials to sustainability outcomes. The framework is comprised of degradation modes and safety limits under demanding working conditions consisting of three main performance aspects. To ensure the effectiveness of microgrids and hybrid renewable energy systems, it is essential to conduct studies based on real-world applications. Future research should develop cost-effective testing methods and explore new application areas.
Reliability improvement of moulded case circuit breaker using Design for Six Sigma Santosh B Rane, Sainath Ghanshyam Bidikar, Prathamesh Ramkrishana Potdar International Journal of Quality and Reliability Management, 2025 PurposeThe purpose of this study is to develop a systematic approach by demonstrating the Design for Six Sigma (DFSS) approach for reliability improvement of moulded case circuit breaker (MCCB) (current rating 250A).Design/methodology/approachIn this study, the Define, Measure, Analyze, Design and Verify (DMADV) methodology of DFSS has been used to improve the reliability of MCCB. Mechanical endurance test (MET), project risk management, customer-based product development (PD), and other tools and techniques are used appropriately in DMADV methodology for improving the reliability of MCCB.FindingsIt has been observed that the reliability of MCCB has been improved from 61.76% to 98.17% for 20,000 operating cycles by implementing suggested improvement measures and the Weibull distribution is the most suitable distribution for reliability analysis of collected data.Research limitations/implicationsThis study considered only the aspects of DMADV methodology of the DFSS approach and does not cover other PD approaches such as lean and green PD.Practical implicationsThis study clearly shows an enhancement in the reliability of MCCB which further leads to an increase in the warranty period. This will attract more customers and enhance business.Social implicationsThe improvement in the reliability of MCCB would significantly reduce fatal accidents ensuring workplace safety in the industry.Originality/valueThe originality of this study is the reliability assessment using degradation analysis in the design phase of the DMADV process to predict failure during design verification.
Computer Vision Based Intelligent Detection Of Surface Irregularities with YOLO and OpenCV using Deep Learning Sonali Rangdale, Prathmesh Pathak, Prathamesh Potdar, Prathamesh Takawale, Abhishek Unde, Rupali Umbare 2025 4th Opju International Technology Conference on Smart Computing for Innovation and Advancement in Industry 5 0 Otcon 2025, 2025 Surface defects like cracks, scratches, and corrosion must be accurately detected in order to prolong the life span and enhance the performance and reliability of steel products in industries like automobile, construction, and manufacturing. Conventional inspection techniques have long examined these manually, which is very time-consuming and error prone. The work here introduces an automated approach for improving the detection of steel surface defects via deep learning-based image recognition. We suggested the YOLO framework to achieve defect detection, while OpenCV performs different preprocessing tasks, like resizing, normalization, and augmentation. The TensorFlow framework provides the environment for training the model, which learns from the surface defects of various models and classifies them. The performance evaluation on a well-developed dataset of steel components has been found to be exemplary, with a level of accuracy reaching 88.3%, which surpasses conventional methods of image processing. An F1 metric was employed to further validate this technique as being less time-consuming and still highly efficient in defect detection with much better accuracy.
