International Research Journal of Innovation in Science and Technology
International Research Journal of Innovation in Science and Technology (IRJIST) is a peer-reviewed, open-access journal publishing original research and review articles in science, technology, and innovation.
Articles published in the International Research Journal of Innovation in Science and Technology (IRJIST) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Authors retain the copyright of their work and grant the journal the right of first publication. Proper attribution to the authors and the journal is required for any reuse of the published content.
Optimization of Roller Burnishing Parameters for Al7075 Using Hybrid Nanofluids: A Comparative Performance Study
Roller burnishing is a chipless surface-finishing process used to improve surface integrity while also increasing the dimensional accuracy and fatigue life of aluminum-based components through controlled cold plastic deformation. Al7075-T6 is one of the most challenging alloys to machine due to its low thermal conductivity and tendency to gall, and it generally requires a very high-quality surface finish that cannot be achieved under traditional flood-coolant conditions. This paper examines the multi-objective optimization of burnishing parameters during roller burnishing of Al7075 under five lubrication modes: dry, mineral oil, Al₂O₃/water MQL, CuO/water MQL, and Al₂O₃–CuO hybrid nanofluid MQL. For a constant penetration depth of 0.2 mm and three passes, a Taguchi L₉ orthogonal array was adopted to vary spindle speed (200–800 rpm), feed rate (0.05–0.20 mm/rev), burnishing force (100–300 N), and hybrid nanofluid concentration (0.5–1.5 wt.%). Surface roughness (Rₐ), microhardness (HV), and roundness error were evaluated as performance responses, and the dominant process parameters were identified using signal-to-noise ratio analysis and ANOVA. The aim was to quantify the surface-quality advantage of hybrid nanofluid burnishing over dry and single-nanoparticle MQL environments and to determine the parameter combination yielding both minimum Rₐ and maximum microhardness. Results show that the average surface roughness (Rₐ) obtained using the Al₂O₃–CuO hybrid nanofluid improved from 1.82 μm under dry burnishing to 0.62 μm, representing an improvement of nearly 66%, while the microhardness increased from 118 HV to 156 HV. Feed rate (35.2%) and hybrid nanofluid concentration (28.7%) were found to be the dominant process parameters. These results validate the use of hybrid nanofluid MQL as a sustainable and high-performance lubrication solution for finish burnishing of high-strength aluminum alloys.
Copyright (c) 2026 Kotkar Yogesh Uttam, Prof. Vishal Vijay Chahare (Author) https://creativecommons.org/licenses/by/4.0
Influence of Hybrid Nanofluid Composition on Surface Roughness and Hardness During Roller Burnishing of Aluminum Alloys
Hybrid nanofluids, formed by combining two different nanoparticles in a single base fluid, have gained attention for their enhanced lubrication and heat-transfer capabilities. However, the performance of such fluids is strongly influenced by the nanoparticle composition ratio, while most previous studies have employed a fixed 50:50 mixture. This study investigates the effect of hybrid nanofluid composition on surface roughness (Ra) and microhardness (HV) during roller burnishing of Al6061-T6, Al7075-T6, and Al2024-T3 aluminum alloys. Four hybrid nanofluid systems, namely Al₂O₃-CuO, Al₂O₃-TiO₂, Al₂O₃-graphene, and CuO-MWCNT, were prepared using deionized water at a constant nanoparticle concentration of 1.0 wt.%. Five composition ratios ranging from 100:0 to 0:100 were evaluated under identical burnishing conditions. The results showed that the optimum composition varied with the hybrid pair and generally differed from the commonly used 50:50 ratio. Among the investigated formulations, the Al₂O₃-graphene hybrid achieved the lowest surface roughness (Ra = 0.58 μm on Al7075-T6), while the Al₂O₃-CuO hybrid produced the highest microhardness (156 HV). The optimum compositions were identified as 75:25 for Al₂O₃-CuO, 60:40 for Al₂O₃-TiO₂, 60:40 for Al₂O₃-graphene, and 50:50 for CuO-MWCNT. The findings highlight the importance of composition optimization in improving surface integrity during hybrid nanofluid-assisted roller burnishing.
Copyright (c) 2026 Prof. Vishal Vijay Chahare, Murarikar Ganesh Balaji (Author) https://creativecommons.org/licenses/by/4.0
WeedAway: A Tractor-Attached Autonomous Post-Harvest Weed and Debris Collection System
Post-harvest weed management remains one of the most labor-intensive and economically significant challenges in Indian agriculture. After harvesting, residual weed stumps and roots persist in the field and, if left unmanaged, compete with subsequent crops. Existing solutions, including manual weeding, chemical herbicides, and rotary tillers, do not simultaneously cut and collect weed biomass in a single pass. This paper presents WeedAway, a tractor-attached post-harvest weed and debris collection system that integrates angled cutting blades, a perforated conveyor belt, a blower-based soil separation stage, and a collection bin. The prototype was fabricated using conventional manufacturing processes, including sheet metal cutting, welding, drilling, and bolt assembly. Preliminary field observations indicated a reduction of approximately 65% in field-preparation time and a significant decrease in manual labor requirements. Experimental observations and comparison with existing mechanized weed-management systems confirm the feasibility of the proposed design for small and marginal Indian farmers.
Copyright (c) 2026 Nitin Ambhore, Shreeharsh Omase, Rehan Mulani, Darshan Patil, Om Shinde (Author) https://creativecommons.org/licenses/by/4.0
Role of Early Risk Identification in Reducing Project Delays and Cost Overruns
Project delays and cost overruns continue to affect construction, infrastructure, transportation, and information technology projects across the world. Studies conducted over the past two decades indicate that a large percentage of projects exceed planned cost and schedule targets due to inadequate planning, weak governance, and ineffective risk management practices. This paper presents a systematic review of the role of Early Risk Identification (ERI) in reducing project delays and cost overruns using published industry reports, project management studies, and global datasets from 2005–2025. The review examines the causes of project overruns, traditional and modern risk identification techniques, sector-specific evidence, and the growing role of technologies such as Artificial Intelligence (AI), Building Information Modelling (BIM), and digital twins in project risk management. The study also discusses behavioural and organizational barriers including optimism bias, weak stakeholder coordination, and delayed decision-making. The findings suggest that proactive risk identification significantly improves project performance by reducing schedule delays, improving cost control, and strengthening project governance. The paper concludes with practical recommendations for implementing structured ERI frameworks in modern project environments.
Copyright (c) 2026 Kartavya Patil, Nishant Patil (Author) https://creativecommons.org/licenses/by/4.0
Engineering Approaches to Failure and Safety Assessment in Mechanical Systems
Risk analysis plays an important role in ensuring the safety, reliability, and operational performance of mechanical engineering systems. This paper presents a review of commonly used risk analysis techniques applied in mechanical engineering, including Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), Hazard and Operability Study (HAZOP), Probabilistic Risk Assessment (PRA), and Risk Priority Number (RPN)-based approaches. Each technique is discussed with respect to its working principle, application areas, advantages, and limitations. Qualitative and quantitative risk assessment methods are also compared, and a general framework for selecting suitable techniques based on system complexity, data availability, and industrial requirements is presented. Illustrative case studies related to pressure vessel systems, rotating machinery, and aerospace structural components are included to explain practical applications of these methods. The review indicates that combined use of qualitative and quantitative approaches can improve risk identification and support better decision-making in complex engineering systems. The paper is intended to provide a useful reference for engineering students, researchers, and professionals working in the fields of mechanical system safety, maintenance, and reliability engineering.
Failure Mode and Effects Analysis (FMEA)
Copyright (c) 2026 Yash Vikas Mali, Mudra Dhanraj Patil (Author) https://creativecommons.org/licenses/by/4.0
Integrated Risk Analysis and Thermal Runaway Management in Electric Vehicle Lithium-Ion Batteries: A Lifecycle and Safety Engineering Perspective
The increasing use of electric vehicles (EVs) has led to a rapid rise in the use of lithium-ion battery systems because of their high energy density and better performance. However, along with these advantages, battery safety has become a major concern, especially due to thermal runaway, which can lead to fire, explosion, and release of harmful gases. This paper presents a review of risk analysis and thermal runaway management in EV lithium-ion batteries from a lifecycle and safety engineering perspective. The study discusses the main thermal and electrochemical processes responsible for thermal runaway and explains different risk factors associated with battery manufacturing, operation, transportation, storage, and recycling stages. Various risk analysis methods such as Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), Bayesian networks, and machine learning-based prediction methods are also discussed. The paper further explains the importance of Battery Management Systems (BMS), thermal management systems, and monitoring techniques in improving battery safety and reliability. The review highlights the need for an integrated safety approach that combines design safety, operational monitoring, thermal management, and lifecycle risk assessment for better prevention of battery failures. The study concludes that multidisciplinary engineering methods and continuous technological improvements are important for safe and reliable large-scale deployment of electric vehicle battery systems
Copyright (c) 2026 Pranav Gogawale, Samprada Kumar (Author) https://creativecommons.org/licenses/by/4.0
BioMedLink - Health Records at Your Fingertips
In modern healthcare systems, quick and secure access to patient medical records is very important, especially during emergency situations where the patient may be unconscious or unable to communicate properly. This paper presents BioMedLink, a fingerprint-based medical record retrieval system designed to provide fast and secure access to patient information using biometric authentication. The system captures fingerprint data, performs feature extraction, and matches the generated fingerprint template with records stored in the hospital database. If the record is unavailable locally, the system sends a secure request to a centralized healthcare hub connected with multiple hospitals. After successful authentication, patient medical details such as previous diagnoses, treatment history, and emergency-related information can be retrieved in real time. The proposed system uses SHA-256 hashing, encrypted communication, role-based access control, and audit logging to maintain data privacy and system security. The platform is developed using React, Node.js, Express, Python, and MongoDB to ensure scalability and smooth inter-hospital communication. The proposed framework helps improve accessibility of medical records, reduces retrieval delay during emergencies, and supports better coordination between healthcare institutions.
Copyright (c) 2026 Jayant Nandwalkar, Vaishnavi Patil, Yash Waykole, Divesh Prasad (Author) https://creativecommons.org/licenses/by/4.0
Operational and Mechanical Risk Management in Automotive Showroom Facilities
Automotive showroom facilities are commonly operated as commercial retail environments despite involving several operational and mechanical conditions associated with light industrial facilities. The presence of heavy vehicle loading, confined vehicle movement, hydraulic lifting systems, and flammable detailing chemicals introduces risks that are often insufficiently addressed through conventional commercial safety practices. This study investigates operational and mechanical risks in small-to-medium automotive showroom facilities located in the Pune metropolitan region. Risk identification was conducted through structured facility walkthroughs and zone-based observation across six dealership sites. A 5×5 Risk Matrix and an adapted Failure Mode and Effects Analysis (FMEA) framework were used to prioritise identified hazards. SolidWorks-based layout modelling was additionally employed to evaluate showroom floor slab loading under different vehicle display configurations. The analysis identified floor slab overloading from clustered SUV displays, vehicle-pedestrian interaction during manoeuvring operations, solvent vapour accumulation in detailing bays, and hydraulic ramp failure modes as major operational concerns. The findings indicate that relatively practical engineering controls, including layout optimisation, pedestrian segregation, preventive inspection practices, and controlled chemical storage, can significantly improve operational safety within dealership environments. The study highlights the need for structured facility-level risk management approaches in automotive showroom operations and demonstrates the applicability of engineering risk assessment methods within this sector.
Copyright (c) 2026 Shubham Bandal, Raj Bobade, Samarth Yadav (Author) https://creativecommons.org/licenses/by/4.0
Quantitative Risk Assessment of Fire Hazards in Electric Vehicle Lithium-Ion Batteries
The swift growth of electric vehicles (EVs) on the global scale has brought a new dimension of fire safety issues, which are based on the electrochemical characteristics of lithium-ion (Li-ion) battery systems. In this paper, a systematic risk evaluation of EV battery fire hazards is made using standard engineering risk processes like Failure Mode and Effects Analysis (FMEA), Probability Risk Matrices, Fault tree analysis (FTA) and Event tree analysis (ETA). Information in the world EV fire incident databases between 2016 and 2024 is synthesized to measure the likelihood and severity of the major hazard scenarios, such as thermal runaway, internal short circuits, overcharge, and mechanical abuse. The analysis creates a multi-layered risk priority framework using Risk Priority Number (RPN) scoring, which ranks eight major failure modes according to occurrence, severity and detectability. The comparative analysis of the most popular battery chemistries, NMC, LFP, NCA and LCO, provide evidence that LFP designs have better thermal stability with much reduced fire propagation risks. Findings show that the highest-risk mode is thermal runaway (RPN = 448), with battery management system (BMS) failures and separator membrane degradation becoming secondary risks. The article closes with a set of evidence-based suggestions on passive and active mitigation of fire, regulatory adherence, and research perspectives in solid-state battery implementation and artificial intelligence-based battery surveillance.
Copyright (c) 2026 Chinmay Deshpande, Anurag Ghadge, Avinash Somatkar (Author) https://creativecommons.org/licenses/by/4.0
Comparative Reliability Assessment of Solar Photovoltaic and Wind Energy Systems Using Failure Mode and Effects Analysis (FMEA)
This study presents a comparative reliability and risk assessment of solar photovoltaic (PV) and wind energy systems using Failure Mode and Effects Analysis (FMEA). With the rapid expansion of renewable energy infrastructure, understanding system-specific failure behavior has become essential for ensuring operational stability and long-term performance. The analysis identifies critical failure modes across key subsystems, including PV modules, inverters, turbine blades, gearboxes, and generators. Each failure mode is evaluated using Severity, Occurrence, and Detection parameters to compute the Risk Priority Number (RPN). The study further examines the influence of environmental and operational conditions such as temperature variation, dust accumulation, and wind variability on system reliability. The results demonstrate distinct risk profiles for the two technologies. Solar PV systems exhibit predominantly environment-driven degradation, resulting in distributed and manageable risks. In contrast, wind energy systems show higher criticality due to mechanical complexity and continuous dynamic loading, leading to more concentrated and severe failure modes. These findings emphasize the need for differentiated maintenance strategies, including preventive and condition-based approaches tailored to system characteristics. The proposed comparative framework provides practical insights for reliability assessment, maintenance planning, and decision-making in renewable energy system deployment.
Copyright (c) 2026 Samruddhi Survase, Avinash Somatkar (Author) https://creativecommons.org/licenses/by/4.0
A Structured Analytical Framework for Failure Prioritization Using FMEA in Mechanical Manufacturing Systems
This paper presents a structured review and analytical framework for failure prioritization using Failure Mode and Effects Analysis (FMEA) in mechanical manufacturing systems, supported by real-world industrial case studies. The study combines subsystem-level analysis with documented applications from automotive, aerospace, steel, and heavy equipment manufacturing, including systems comparable to those used by Toyota, Boeing, Tata Steel, Siemens, Caterpillar, and Mahindra. Key mechanical subsystems such as bearing assemblies, spindle systems, hydraulic units, and gear trains are evaluated using Severity, Occurrence, and Detection parameters to calculate Risk Priority Numbers (RPN). The case study analysis highlights consistent failure patterns across industries, with fatigue, wear, and thermal stress emerging as dominant contributors to high-priority failures. The results demonstrate that structured implementation of FMEA leads to measurable improvements, including reductions in unscheduled downtime, enhancement in product quality, and optimization of maintenance practices. These improvements are consistently observed across different industrial domains, confirming the applicability of FMEA as a generalized failure prioritization tool. The study further discusses the limitations of conventional RPN-based approaches and highlights the need for integration with predictive maintenance, real-time monitoring, and data-driven technologies. The findings establish FMEA as a practical and scalable methodology for failure prioritization in modern manufacturing environments.
Copyright (c) 2026 Yash Amol Pawar, Abhijeet Pramod Desai, Prajwal Hanumant Shivtare (Author) https://creativecommons.org/licenses/by/4.0
Design and Performance Evaluation of an Industrial Nozzle for Abrasive Jet Micro-Drilling of Glass
This study presents an integrated computational and experimental investigation of abrasive jet machining (AJM) for micro-drilling of glass plates using a standard industrial nozzle. Computational Fluid Dynamics (CFD) simulations were performed to analyze abrasive particle flow behavior, velocity distribution, and impact characteristics within the nozzle. Experimental trials based on a Taguchi design were conducted to evaluate the effects of key process parameters, including jet pressure, abrasive feed rate, stand-off distance, abrasive type, and grain size. The results show that jet pressure has a strong influence on material removal rate (MRR), while abrasive feed rate and stand-off distance significantly affect hole accuracy and surface quality. Optimal machining performance was achieved at a jet pressure of 5.5–6 bar, abrasive feed rate of approximately 3 g/s, and stand-off distance of around 1.5 mm, producing holes with high dimensional accuracy (~2.00 mm), low surface roughness (~0.9 µm), and minimal taper (<2°). The study establishes a correlation between CFD predictions and experimental observations, providing a reliable framework for process optimization. The findings contribute to the development of practical guidelines for high-precision AJM micro-drilling in brittle materials such as glass.
Copyright (c) 2026 Om Kamble, Harshad Savant (Author) https://creativecommons.org/licenses/by/4.0
A Hybrid Fuzzy AHP–Entropy–TOPSIS and SEM Framework for Adoption and Impact Assessment of Intelligent Decision Support Systems in Business Management
Intelligent Decision Support Systems (IDSS) integrate artificial intelligence, machine learning, and advanced analytics to enhance organisational decision-making. While IDSS adoption has grown considerably in recent years, a unified framework addressing both adoption drivers and measurable performance outcomes across multiple business sectors remains lacking. To address this gap, this study introduces the Multi-Criteria Adoption Assessment Model (MCAAM). The framework integrates fuzzy Analytical Hierarchy Process (AHP), Shannon entropy weighting, TOPSIS, and covariance-based structural equation modelling (CB-SEM) to evaluate adoption determinants, organisational ranking, and causal relationships. Unlike existing approaches such as TAM and UTAUT, MCAAM integrates subjective expert judgment with objective empirical data within a single evaluation structure. Survey data were collected from 387 business organisations across India, Southeast Asia, and the Middle East, spanning manufacturing (n = 112), finance (n = 98), healthcare (n = 89), and supply chain (n = 88) sectors during March–September 2024. Findings indicate that the strongest predictors of adoption are trust in AI-driven outcomes (β = 0.412, p < 0.001) and data infrastructure maturity (β = 0.387, p < 0.001). Organisations implementing IDSS achieved a 34.7% improvement in decision accuracy and a 28.3% reduction in decision latency. Limitations include the cross-sectional design, geographic concentration, and reliance on self-reported data, which may affect generalisability. Future research should incorporate longitudinal designs, broader geographic coverage, and integration of generative AI capabilities.
Intelligent Decision Support Systems (IDSS)
Copyright (c) 2026 Dhiraj Jadhav, Gayatri Pimple, Vikas Jadhav, Vaishnavi Bagal (Author) https://creativecommons.org/licenses/by/4.0
Risk in Active Manufacturing Plants: A Practical Multi-Hazard Approach
Manufacturing plants are complex environments where ongoing production exposes workers, equipment, and nearby communities to various types of risks. This study analyzes these risks during active production and presents a structured approach for identifying and evaluating them. A combination of established methods, including Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), and Bow-Tie analysis, was used to assess hazards across mechanical, chemical, electrical, ergonomic, and environmental domains. Data were collected from five large-scale manufacturing facilities in India, covering automotive, pharmaceutical, chemical, and electronics sectors. The analysis identified a total of 1,697 hazards, with mechanical hazards (28.4%), chemical exposure (22.1%), and process-related deviations (19.7%) emerging as the dominant risk categories, together accounting for over 70% of the total identified risks. The study further shows that the implementation of proactive strategies such as real-time monitoring, predictive maintenance, and anomaly detection can lead to a significant reduction in overall risk levels (approximately 40–45%), while also enabling early detection of near-miss events with prediction rates of around 85–90%. Overall, the findings highlight the importance of combining traditional risk assessment methods with modern data-driven approaches, along with strong safety practices, to improve risk management and ensure safer manufacturing operations.
Copyright (c) 2026 Vikas Jadhav, Vaishnavi Bagal (Author) https://creativecommons.org/licenses/by/4.0
Design and Development of an Autonomous Machine Vision-Based Weed Detection and Removal Robot for Agriculture 4.0 Applications
Weed growth significantly reduces agricultural productivity by competing with crops for essential resources such as nutrients, water, and sunlight. Conventional weed control methods, including manual labor and chemical herbicides, are often inefficient, labor-intensive, and environmentally unsustainable. This paper presents the design and development of an autonomous machine vision-based weed detection and removal system for Agriculture 4.0 applications. The proposed system integrates a vision module for real-time image acquisition, a convolutional neural network (CNN) for crop–weed classification, and a robotic actuation mechanism for targeted weed removal. The system is designed to operate under varying field conditions and enables precise intervention without damaging crops. Experimental evaluation indicates that the system achieves classification accuracy in the range of approximately 88–93%, demonstrating reliable performance in real-time scenarios. The proposed approach reduces dependency on chemical herbicides and labor while contributing to sustainable and intelligent farming practices.
Copyright (c) 2026 Vaishnavi D. Singare, Avinash A. Somatkar (Author) https://creativecommons.org/licenses/by/4.0
CFD Analysis of Heat Transfer in a Throttling Needle Valve
This study presents a Computational Fluid Dynamics (CFD)-based analysis of heat transfer in a throttling needle valve under steady-state conditions. When fluid flows through a partially restricted passage, a sharp pressure drop and velocity increase occur, resulting in significant viscous dissipation and localized heat generation. The objective is to quantify thermal effects, evaluate their spatial distribution, and examine their influence on fluid properties and valve performance. A three-dimensional CFD model was developed using ANSYS Fluent with a pressure-based solver, k–ω SST turbulence model, and energy equation including viscous dissipation. Water was used as the working fluid with an inlet pressure of 10 psi, outlet pressure of 0 psi, and 30% valve opening. Results indicate a peak temperature of approximately 79.5 °C at the needle tip and orifice, along with a fluid temperature rise of 5–10 °C and a mass flow rate of 547.95 kg/s. Turbulent flow behavior with recirculation zones was observed. These findings emphasize the necessity of incorporating thermal considerations in the design of high-pressure throttling systems.
Copyright (c) 2026 Atharva Pacharne, Devesh Balwantrao (Author) https://creativecommons.org/licenses/by/4.0
Design and Analysis Frameworks for Additively Manufactured Orthopaedic Implants: A Review of Lattice Architectures and Finite Element Approaches
Additive manufacturing (AM) has emerged as a transformative approach in orthopaedic implant design by enabling the fabrication of patient-specific geometries and complex internal architectures. This paper presents a comprehensive review of design frameworks and analytical methodologies used in the development of additively manufactured orthopaedic implants, with particular emphasis on lattice structures and finite element analysis (FEA). The study outlines the complete workflow from medical imaging and computer-aided design to fabrication and post-processing, highlighting key considerations in implant design such as anatomical conformity, fixation strategies, and material selection. The role of lattice and porous architectures in tailoring mechanical properties and promoting osseointegration is critically examined, with attention to design parameters including pore size, strut thickness, and relative density. Furthermore, commonly adopted finite element modelling approaches are analysed to evaluate stress distribution, deformation, and micromotion under physiological loading conditions. The review also discusses challenges related to process variability, modelling assumptions, and the absence of standardized validation frameworks. Emerging trends, including the integration of optimization techniques and artificial intelligence, are explored as potential solutions to improve design efficiency and predictive accuracy. The findings indicate that the effective combination of advanced design strategies, computational modelling, and additive manufacturing technologies can significantly enhance implant performance and reliability. This study provides a structured perspective on current developments and future directions in the field of additively manufactured orthopaedic implants.
Copyright (c) 2026 Omkar Raghu Pujari, Ayan Riyaz Nadaf (Author) https://creativecommons.org/licenses/by/4.0
Risk Assessment in Automated Manufacturing Systems: A Hybrid Framework for Industry 4.0 and Beyond
Industry 4.0 has transformed manufacturing systems through the integration of cyber-physical systems, Internet of Things (IoT), machine learning, and digital twin technologies. While this interconnected architecture improves efficiency and flexibility, it also introduces complex and interdependent risk scenarios that are not adequately addressed by conventional assessment methods. This paper proposes a hybrid risk assessment framework for automated manufacturing systems that combines classical techniques such as Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) with machine learning-based prediction and digital twin simulation. The framework incorporates a composite risk scoring model integrating static risk prioritisation with real-time predictive analytics and contextual factors. A four-layer architecture is developed, comprising risk taxonomy, dynamic monitoring, simulation-based validation, and governance. The proposed approach is evaluated using benchmark manufacturing datasets, including the PHM 2010 milling dataset and the CWRU bearing dataset, under stratified k-fold cross-validation. Results indicate that the hybrid FMEA + Random Forest model achieves superior performance, with an F1-score of 0.972 and Area Under the Curve (AUC) of 0.981, outperforming standalone models. Case studies across automotive, electronics, smart factory, and pharmaceutical domains demonstrate the practical applicability of the framework. The study further highlights the role of explainable artificial intelligence (XAI) in improving transparency and trust in risk prediction systems. The findings suggest that hybrid, data-driven approaches are essential for effective risk management in Industry 4.0 and emerging Industry 5.0 environment.
risk assessment; automated manufacturing
Copyright (c) 2026 Kushal Chaudhari, Nikhil More, Avinash Somatkar (Author) https://creativecommons.org/licenses/by/4.0
Risk-Informed Performance Optimization of Waste Heat Recovery Systems: A 47-System Empirical Study with Statistical Validation and ROI Analysis
Waste Heat Recovery (WHR) systems play a key role in improving industrial energy efficiency and reducing emissions, but their performance is often limited by thermal, mechanical, corrosion, operational, and financial risks. This study develops a quantitative risk assessment framework for WHR systems using a modified Risk Priority Number (RPN) approach, normalized to a 1–5 scale. The framework is validated through empirical analysis of 47 operational WHR systems across five industrial categories over a 36-month period, with a total of 1,410 component-level risk evaluations. The weighted mean heat recovery efficiency was calculated as 68.3 ± 3.95%, while the overall risk index averaged 3.51. Implementation of structured risk management resulted in a 48% reduction in maintenance cost index, compared to a significant increase observed in unmanaged systems. Economic analysis based on site-level data shows a return on investment (ROI) of 312%. Statistical validation using one-way ANOVA (F(4,42) = 16.95, p < 0.001) confirms significant variation in risk profiles across system types, highlighting the importance of system-specific risk strategies. The proposed framework demonstrates measurable improvements in operational reliability, cost efficiency, and decision-making, providing a practical and data-driven approach for risk management in industrial WHR systems.
Waste Heat Recovery; Risk Priority Number; Quantitative Risk Management; FMEA; Predictive Maintenance; ROI Analysis; ANOVA; Industrial Decarbonisation
Copyright (c) 2026 Om Kale, Tushar Bagade (Author) https://creativecommons.org/licenses/by/4.0
Risk Analysis and Process Optimization in Toy Manufacturing Using FMEA, Six Sigma, and Statistical Process Control
This study presents a structured framework for risk evaluation and mitigation in the manufacturing process of Hot Wheels toy cars using Failure Mode and Effects Analysis (FMEA), Statistical Process Control (SPC), and Six Sigma methodologies. The multi-stage manufacturing process, including die casting, plastic molding, painting, assembly, wheel fitting, and packaging, is systematically analyzed to identify potential failure modes and associated risks. Risk prioritization is performed using the Risk Priority Number (RPN), based on severity, occurrence, and detection parameters. The analysis reveals that die casting (RPN = 240), painting (RPN = 216), and wheel fitting (RPN = 210) collectively contribute approximately 75% of total risk exposure, indicating critical process stages. Root cause analysis identifies process variability (38–42%) and human error (22–28%) as the primary contributors to failures, with high-risk categories accounting for nearly 66% of total risks. The implementation of SPC is shown to reduce process variability by 25–35%, while Six Sigma methodologies target defect reduction to near-zero levels (3.4 DPMO). The integration of these approaches, supported by lean manufacturing principles, enhances process stability, product quality, and operational efficiency. The study demonstrates that a targeted and integrated risk management approach can significantly improve reliability and performance in toy manufacturing systems.
Copyright (c) 2026 SHREEYASH MHATRE, AVINASH SOMATKAR (Author) https://creativecommons.org/licenses/by/4.0
Integrated Analytical Modeling of Material Removal Rate and Surface Roughness in Magnetic Levitation EDM of Ti-6Al-4V
Electrical Discharge Machining (EDM) has emerged as a critical non-conventional machining process for difficult-to-machine materials such as Ti-6Al-4V, widely used in aerospace and biomedical applications. However, achieving an optimal balance between material removal rate (MRR) and surface roughness (Ra) remains a significant challenge due to complex thermal, plasma, and electro-physical interactions involved in the process. This study presents an integrated analytical and experimental investigation of MRR and surface roughness in Magnetic Levitation EDM of Ti-6Al-4V under varying discharge conditions. A structured experimental framework was developed by systematically varying peak current, pulse-on time, and pulse-off time while maintaining a constant servo voltage. The results reveal a direct relationship between peak current and MRR, accompanied by a deterioration in surface quality due to increased discharge energy and crater formation. A clear trade-off between machining productivity and surface integrity is observed, consistent with findings reported in advanced machining and surface enhancement processes. The proposed analytical approach, based on discharge energy principles, establishes a predictive framework for evaluating EDM performance. This study contributes toward improved parameter selection and supports multi-objective optimization of machining efficiency and surface quality in high-performance titanium alloys.
Copyright (c) 2026 Gaurav Sanap, Vishal Sanap, Tanmay Patil, Aryan Kadu (Author) https://creativecommons.org/licenses/by/4.0
Generative AI and DFMEA for Optimized Lithium-Ion Battery Pack Design in Electric Two-Wheelers
Electric two-wheelers are rapidly emerging as a key component of sustainable transportation, where lithium-ion battery packs play a critical role in determining performance, safety, and efficiency. However, conventional battery design approaches face challenges related to thermal instability, structural limitations, and difficulty in optimizing multiple parameters under real-world operating conditions. This study proposes an integrated framework that combines generative artificial intelligence (AI) with Design Failure Mode and Effects Analysis (DFMEA) to improve battery pack design and enhance risk mitigation. Generative AI is used to explore multiple design configurations based on predefined constraints, while DFMEA systematically evaluates potential failure modes using severity, occurrence, and detection parameters. The proposed approach achieves a reduction of 15–20% in peak temperature, a 10–15% improvement in energy density, and a 12–18% decrease in overall battery weight. In addition, Risk Priority Numbers (RPNs) for critical failure modes are reduced by 25–35%, indicating improved system safety and reliability. These results demonstrate that integrating AI-driven design optimization with structured risk assessment enables the development of safer, more efficient, and high-performance battery systems for next-generation electric vehicles.
Copyright (c) 2026 Rohit S. Jadhav, Harsh A. Khutekar (Author) https://creativecommons.org/licenses/by/4.0
Thermal Performance Evaluation of Green Roof Systems for Passive Cooling in Urban Indian Buildings
Rapid urbanization in India has intensified heat stress and increased cooling energy demand due to heat-absorbing construction materials. This study experimentally evaluates green roof systems as a passive cooling strategy under peak summer conditions. A comparative analysis was conducted between conventional reinforced cement concrete (RCC) roofs and vegetated roof configurations under identical environmental exposure. Results show that green roofs reduce peak surface temperatures by 8–18°C, lower indoor air temperature by 2–6°C and decrease heat flux by 40–65 W/m², indicating enhanced thermal resistance and delayed heat transfer. These improvements are driven by vegetation shading, evapotranspiration, and substrate insulation. Unlike prior studies focused on surface temperature alone, this work integrates heat flux and indoor response, providing a comprehensive assessment under Indian climatic conditions. The findings support green roofs as an effective, low-energy solution for improving thermal comfort and reducing building heat gain.
Copyright (c) 2026 Anuj Borse, Yash Landge, Dr. Avinash Somatkar (Author) https://creativecommons.org/licenses/by/4.0
Surface Roughness Optimization in Hard Turning of AISI D2 Steel Using RSM and Machine Learning
Ultra-precision hard turning of hardened steels offers a promising alternative to grinding for achieving high surface quality; however, predicting surface roughness remains challenging due to complex parameter interactions. This study investigates the prediction and optimization of surface roughness (Ra) in hard turning of AISI D2 steel (62 HRC) using a cubic boron nitride (CBN) tool. Experiments were conducted by varying cutting speed, feed rate, and depth of cut under controlled conditions. Response Surface Methodology (RSM) was used to model and optimize the process, while machine learning models including Support Vector Machine (SVM), Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed for prediction. Model performance was evaluated using R, RMSE, and MAPE. Results indicate that feed rate is the most significant factor affecting surface roughness, followed by cutting speed, while depth of cut has minimal influence. Among the models, ANFIS achieved the highest accuracy (R = 0.81, RMSE = 0.17). Optimal conditions (Vc = 100 m/min, f = 0.025 mm/rev, ap = 0.09 mm) yielded a minimum surface roughness of 0.207 µm. The integration of RSM and machine learning provides an effective and reliable framework for accurate prediction and optimization in ultra-precision hard turning.
Copyright (c) 2026 Mauli S. Sudake, Aditya A. Pujari, Dr. Avinash Somatkar, Vishal V. Chahare (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Demand Forecasting and Inventory Optimization Using Prophet-Based Time Series Modelling
The increasing complexity of retail operations has intensified the need for precise demand estimation and efficient inventory management. This study proposes an integrated framework that combines time-series forecasting with automated inventory decision support to enhance supply chain performance. A forecasting model based on Prophet is employed to analyse historical sales data and generate future demand predictions, while a FastAPI-based backend facilitates real-time interaction with the system. To simulate practical retail conditions, multiple product categories with varying demand patterns and promotional influences are incorporated. An intelligent decision module evaluates predicted demand against available stock levels and classifies inventory status to identify potential shortages or stable conditions. The model’s performance is assessed by using standard metrics such as MAE, RMSE, and MAPE. Experimental evaluation demonstrates strong predictive performance, achieving an MAE of 5, RMSE of 5.39, and MAPE of approximately 3.55%, indicating high forecasting accuracy. The proposed approach offers a scalable and data-driven solution for improving operational efficiency in retail supply chains.
Copyright (c) 2026 Dr. Sanjay B. Patil, Mitali Patil, Kshitija S. Thakur, Rasika Pimple, Shweta Patil (Author) https://creativecommons.org/licenses/by/4.0
Thermoelectric Generator for Waste Heat Recovery System
The growing demand for sustainable energy solutions has intensified interest in recovering waste heat to reduce energy losses in automotive and industrial systems. In this study, a thermoelectric generator (TEG) system was designed, modelled, and experimentally validated to convert waste heat into electrical energy using the Seebeck effect. Thermoelectric modules were selected based on a high Seebeck coefficient and low thermal conductivity to minimize energy losses while maintaining a large temperature gradient across the system. Thermal and electrical performance of the TEG system under varying operating conditions was analysed using ANSYS and MATLAB Multiphysics simulations. Experimental validation demonstrated a maximum electrical power output of 3.0 W and a peak thermal-to-electrical conversion efficiency of 6.0% at a temperature differential of 350 °C. The results indicate that compact thermoelectric generator systems can serve as reliable, maintenance-free, and decentralized power sources for waste heat recovery in automotive exhausts and industrial processes. Although conversion efficiency and power management remain key challenges for thermoelectric technologies, the findings highlight their significant potential for clean energy harvesting from medium- to high-grade waste heat sources.
Copyright (c) 2026 Aryan Kadu, Tanmay Patil, Dr. Avinash Somatkar (Author) https://creativecommons.org/licenses/by/4.0
Rising Water Levels in Lonar Lake: A Conceptual Framework for Subsurface Hydrology and Cultural Heritage Risk
Lonar Lake, a rare terrestrial meteorite impact crater formed within the Deccan Trap basalt of central India, represents a unique convergence of geological, hydrological, ecological, and cultural significance. In recent years, the lake has exhibited persistent increases in water level, resulting in periodic inundation affecting culturally significant structures such as the Kamalja Mata Temple within the crater basin. While surface hydrological inputs are often cited as explanatory factors, they may be insufficient to explain the magnitude and persistence of observed lake-level changes. This study presents a hypothesis-driven, interdisciplinary framework to examine potential subsurface controls on the hydrological behaviour of Lonar Lake. Drawing upon established knowledge of impact-modified basaltic geology, fracture-controlled groundwater systems, crater lake hydrology, historical water-management practices, long-term irrigation, and modern hydraulic interventions, a set of interlinked hypotheses is proposed suggesting that Lonar Lake may function as a semi-open hydrological system influenced by regional groundwater connectivity. Rather than advancing definitive conclusions, the study outlines conceptual propositions and methodological pathways to guide future geological, hydrogeological, and archaeological investigations. Understanding these processes is essential for advancing crater-lake hydrology and for informing conservation strategies aimed at safeguarding the cultural heritage and ecological integrity of the Lonar crater system.
Copyright (c) 2026 Arjit Amol More, Rani Amol More (Author) https://creativecommons.org/licenses/by/4.0
Assessing Power Sector Reforms in India: Regulatory Performance, Distribution Stress and Reform Pathways
India’s power sector has evolved through successive phases of state-led development, market-oriented reforms, and regulatory restructuring, culminating in the Electricity Act, 2003. While regulatory reforms have enabled significant progress in generation capacity expansion, grid integration, renewable energy deployment, and electricity access, persistent challenges remain in distribution sector performance, tariff rationalization, subsidy dependence, and regulatory effectiveness. This paper critically examines the evolution and outcomes of power sector reforms under regulatory control in India, with a focused assessment of distribution sector stress, contemporary operational challenges, and emerging structural shifts driven by decentralization, digitalization, and changing consumer roles. Drawing on policy analysis and sectoral experience, the study identifies gaps between reform intent and outcomes and proposes a concise, integrated reform framework centered on financial sustainability, regulatory governance, pricing correction, and adaptive implementation. The paper contributes policy-relevant insights for strengthening the power sector’s role in supporting India’s long-term economic growth and energy transition.
Copyright (c) 2026 Lukesh Dhangaonkar, Dr. Mahadeo Narlawar (Author) https://creativecommons.org/licenses/by/4.0
Agrarian Crisis and Farmer Suicides in India: A Regional Analysis of Vidarbha
Indian agriculture continues to face persistent agrarian distress marked by income instability, climate vulnerability, and high incidences of farmer suicides, particularly in rainfed regions. This study examines the structural causes of agrarian distress with a focused regional analysis of Maharashtra and the Vidarbha region. Using a qualitative and analytical approach based on secondary data and policy review, the paper analyses economic, ecological, and institutional drivers of livelihood insecurity, including rising costs of cultivation, climate variability, weak market integration, and limitations of existing risk mitigation mechanisms. The study identifies conservation agriculture, strengthened Farmer Producer Organizations, and convergence of credit, insurance, and market support as critical pathways for stabilizing farm incomes and reducing vulnerability. It argues that fragmented and short-term interventions are insufficient and emphasizes the need for region-specific, livelihood-centered strategies to address agrarian distress and farmer suicides. The paper contributes policy-relevant insights for sustainable agricultural development in India.
Copyright (c) 2026 Rani More, Deepak Ulemale (Author) https://creativecommons.org/licenses/by/4.0
Automatic Shoe Shiner: A Low-Cost Electromechanical Assistive Device for Ergonomic Shoe Care
Routine shoe polishing is commonly performed using manual methods that require repeated forward bending, leading to ergonomic strain and discomfort, particularly affecting the back and knees. This study presents the design, scientific basis, and experimental evaluation of an Automatic Shoe Shiner developed as a low-cost, ergonomically optimized electromechanical device for household and community use. The system employs DC motors, controlled rotational motion, and friction-based brushing within a vertical configuration that enables shoe polishing in upright standing or sitting posture. A structured observation-based methodology was adopted to evaluate functional performance, ergonomic comfort, and operational stability under varying conditions. Experimental observations demonstrated uniform polishing performance, reliable low-voltage operation, and a significant reduction in bending-related strain compared to conventional manual methods. Beyond functional utility, the device visibly demonstrates applied physics and biomechanical principles, offering educational value as a hands-on STEM learning model. The Automatic Shoe Shiner highlights the potential of frugal, human-centered innovation to address everyday ergonomic challenges while promoting accessibility, preventive comfort, and practical science education.
Copyright (c) 2026 Arjit Amol More (Author) https://creativecommons.org/licenses/by/4.0