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.
Machine Learning-Based Prediction of Tensile Strength and Hardness in GTAW-Welded IS 2062 E250 Mild Steel Under Varying Heat Input
Gas Tungsten Arc Welding (GTAW) joints in structural mild steel exhibit a nonlinear, current-dominated relationship between tensile strength, hardness, and heat input that is difficult to capture using classical linear regression. A 16-run Taguchi L16 experimental design was conducted on IS 2062 Grade E250 mild steel (current: 120–150 A; voltage: 10–16 V), with heat input for each run (1.20–2.40 kJ/mm) calculated using the standard heat-input equation. Six regression algorithms Linear Regression, Decision Tree, Random Forest, Support Vector Regression (SVR), XGBoost, and Artificial Neural Network (ANN) were trained and evaluated using leave-one-out cross-validation (LOOCV), which is statistically appropriate for the limited sample size. XGBoost produced the most accurate tensile-strength predictions (R² = 0.953, RMSE = 19.71 MPa), while the ANN achieved the best hardness predictions (R² = 0.992, RMSE = 0.63 HB). In contrast, linear regression performed poorly for tensile strength (R² = −0.099) because the relationship peaked at 130 A rather than varying monotonically with current. ANOVA identified welding current as the statistically dominant factor influencing both responses (p < 0.001). The findings demonstrate that tree-based and neural-network models substantially outperform conventional linear regression for modelling nonlinear GTAW response surfaces, even with a limited dataset, providing a practical and cost-effective approach for pre-screening welding parameters prior to experimental validation, while highlighting the need for future validation using larger, replicated datasets.
Copyright (c) 2026 Jadhav Priyanka Jalindar, Ganesh Bhavar, Pooja Kulkarni (Author) https://creativecommons.org/licenses/by/4.0
Design and Structural Analysis of an Automotive Crash Box Using Advanced Hybrid Composite Materials and Optimized Polygonal Geometry
The crash box is a critical energy-absorbing component of the vehicle crumple zone, designed to protect occupants during frontal collisions by reducing the impact force transmitted to the passenger compartment. With the increasing demand for lightweight vehicle design, improved crashworthiness, and stringent safety regulations, the evaluation of advanced materials and optimized crash box geometries has become increasingly important. This study presents a structural analysis of conventional rectangular and optimized polygonal crash box designs using advanced aluminium-based hybrid composites, namely Al + SiC + Graphene and Al + Al₂O₃ + CNT. The study involves CAD modelling, finite element mesh generation, and explicit dynamic crash simulations using Finite Element Analysis (FEA). The crashworthiness performance of the developed designs is evaluated and compared in terms of equivalent stress, total deformation, equivalent elastic strain, and energy absorption to identify an effective lightweight crash box configuration for enhanced automotive safety.
Copyright (c) 2026 Rushikesh R. Waghmare, Chetan D. Sagar, Sachin C. Borse (Author) https://creativecommons.org/licenses/by/4.0
Structural Analysis and Weight Optimization of Tapered Leaf Spring for Heavy Commercial Vehicles Using Taguchi Design Method
The leaf spring is a critical suspension component in heavy commercial vehicles, directly governing ride comfort, load distribution, and structural durability. Conventional multi-leaf springs are heavy, suffer from inter-leaf friction, and offer limited scope for fatigue-life optimization, motivating the transition toward parabolically tapered single-leaf configurations. This study investigates the structural performance and weight reduction potential of an EN-45 steel tapered leaf spring designed for a 7.5-ton-class commercial truck rear suspension. A three-dimensional finite element model was developed in ANSYS Workbench R23 and validated against analytical Euler–Bernoulli closed-form solutions within a deviation of 4.6 percent. The Taguchi L9 orthogonal array was employed to systematically vary four control factors camber, root thickness, leaf width, and material modulus across three levels each, with maximum von Mises stress and structural mass selected as response variables. Signal-to-noise (smaller-the-better) and analysis of variance (ANOVA) frameworks were applied to identify dominant factors and the optimum factor combination. Results demonstrate that leaf root thickness contributes 62.4 percent to the variance of maximum stress, followed by camber at 18.2 percent, leaf width at 9.7 percent, and material modulus at 3.1 percent. The optimum configuration delivers a 28.9 percent reduction in spring mass (from 19.4 kg to 13.8 kg), a 16.1 percent improvement in vertical stiffness, and a maximum von Mises stress of 421 MPa, well below the allowable limit of 588 MPa for EN-45 steel. A confirmation run yielded an S/N ratio improvement of 2.84 dB over the baseline. These findings establish the Taguchi method as a computationally efficient design framework for lightweight commercial-vehicle leaf springs, with direct implications for fuel economy and payload capacity in the Indian heavy-vehicle sector.
Copyright (c) 2026 Harshwardhan D. Kamble, Prof. Vishal V. Chahare (Author) https://creativecommons.org/licenses/by/4.0
Seismic Performance of Multi-Storey Composite Column Framed Buildings (G+3, G+5 and G+8) with Lead Rubber Bearing Base Isolation Using SAP2000
In earthquake-sensitive areas, especially for multi-storey buildings, structural damage can be severe due to earthquake-induced forces. Base isolation systems, particularly Lead Rubber Bearings (LRBs), have proven effective in enhancing seismic performance by minimizing the transmission of ground motion to the superstructure. In this study, the seismic behaviour of composite column framed buildings with G+3, G+5, and G+8 configurations was analysed using SAP2000 under fixed-base and LRB base-isolated conditions. Three-dimensional numerical models were developed in accordance with IS 1893:2016 and IS 456:2000 and analysed using the Nonlinear Time History Analysis (NLTH) method. Structural response was evaluated in terms of natural time period, storey displacement, inter-storey drift, and base shear. The results showed that the fundamental time period increased significantly with the introduction of LRB isolation, leading to a reduction in seismic force demand. The base-isolated structures reduced base shear by 50.70%, 44.42%, and 29.42% for G+3, G+5, and G+8 buildings, respectively, while maintaining inter-storey drift values within the permissible code limits. Although the flexibility of the isolation system resulted in increased storey displacements, these remained within acceptable performance limits and contributed to enhanced energy dissipation during seismic events. The findings demonstrate that LRB base isolation effectively reduces seismic demand and enhances the seismic performance of composite column buildings, making it a suitable and reliable solution for the design of resilient multi-storey structures in earthquake-prone regions.
Copyright (c) 2026 Kiran Shrikhande, Prof. Dipak Gaidhane (Author) https://creativecommons.org/licenses/by/4.0
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
Material Selection for Lightweight Electric Vehicle Battery Pack Enclosures
Electric vehicles (EVs) are becoming increasingly common, creating a growing demand for battery packs that are efficient, safe, and lightweight. The battery system accounts for a significant portion of a vehicle's total weight; therefore, the material selected for the battery enclosure directly influences driving range, energy efficiency, crashworthiness, and thermal safety. This paper reviews lightweight materials, battery pack architectures, thermal management strategies, and sustainability aspects associated with EV battery enclosures. It discusses conventional metals such as advanced high-strength steels, aluminum, and magnesium, as well as advanced polymer composites, including carbon fiber reinforced polymer (CFRP), glass fiber reinforced polymer (GFRP), sheet molding compound (SMC), and hybrid material systems. The paper also examines emerging thermal management approaches, including phase change materials (PCMs) and fire-resistant polymer composites designed to mitigate thermal runaway. Furthermore, recent battery architectures, such as Cell-to-Pack (CTP) and Cell-to-Body (CTB), are reviewed with respect to their manufacturability and serviceability. Finally, the environmental impacts of various structural materials are compared from a life-cycle perspective to support sustainable material selection for future EV battery systems. This review provides a comprehensive overview of material selection strategies for next-generation electric vehicle battery pack enclosures, with an emphasis on lightweight design, thermal safety, and sustainability.
Copyright (c) 2026 Shreepad Pathak, Palash Parmar, Nainesh Patankar, Yash Patankar (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
SmartGuard: An IoT-Enabled Intelligent Helmet System with GPS Tracking, Collision Detection, and Emergency Response for Enhanced Road Safety
Road traffic fatalities continue to pose a critical global challenge, with powered two-wheelers accounting for a disproportionate share of casualties in developing nations. This paper presents SmartGuard, a novel IoT-enabled intelligent helmet system that integrates real-time GPS tracking, multi-axis fall and collision detection, bio-impedance alcohol sensing, electrooculography (EOG)-based drowsiness monitoring, photoplethysmography (PPG) vital-sign acquisition, and cloud-based emergency response into a single wearable device. A convolutional neural network (CNN) model deployed on an ESP32-S3 microcontroller achieves 97.4% accident-detection accuracy with 18 ms inference latency. A patented bio-impedance chin-strap sensor estimates blood-alcohol concentration (BAC) with a mean absolute error of 0.0028% w/v, triggering a dual-condition vehicle ignition interlock when BAC exceeds 0.03% w/v or the helmet is not worn. Field trials spanning 48,600 km and 120 participants over six months validate GPS accuracy of ±2.3 m and emergency notification latency of 2.7 seconds. SmartGuard provides a scalable, non-intrusive, and cost-effective framework for substantially reducing road fatalities and improving post-accident survivability.
Copyright (c) 2026 Sairaj Jadhav, Shwet Jain, Prof. Pravin Hujare (Author) https://creativecommons.org/licenses/by/4.0
Design and Development of an Automated Nitrogen Level and Flow Measurement System for Cryogenic Tanks
Cryogenic liquids, particularly liquid nitrogen (LN₂), are extensively used in industrial, medical, scientific, and research applications, where accurate level monitoring and controlled fluid transfer are essential for operational safety, efficiency, and resource optimization. Conventional cryogenic measurement methods often rely on independent level or flow sensing devices, manual observation, or complex instrumentation, resulting in limited automation, reduced accuracy under dynamic conditions, and increased risk of overfilling and cryogenic fluid loss. This study presents the design and development of an automated nitrogen level and flow measurement system for cryogenic tanks. The proposed system integrates a turbine flow meter, a cryogenic solenoid valve, and an Arduino-based control unit to provide real-time monitoring, accurate flow measurement, and automated filling control. Pulse signals generated by the turbine flow sensor are processed by the microcontroller to determine the instantaneous flow rate and cumulative transferred volume. A user-defined target volume is continuously monitored, and the solenoid valve is automatically actuated to terminate the filling process once the preset value is achieved. A digital display provides continuous information on flow rate, cumulative volume, and system status. The proposed system is designed using cryogenic-compatible components to ensure reliable operation under extremely low-temperature conditions. The integrated approach enhances measurement accuracy, minimizes manual intervention, improves operational safety, and reduces liquid nitrogen wastage. The developed system offers a practical and cost-effective solution for laboratory facilities, healthcare institutions, and industrial cryogenic storage and distribution applications.
Copyright (c) 2026 Vaishnavi Dnyaneshwar Singare, Aishwarya Vijay Gangadhare, Samruddhi Prashant Survase, Shivam Samkidas Rathod (Author) https://creativecommons.org/licenses/by/4.0
Cryogenic and Conventional Cooling Techniques in Machining: A Comparative Study for Sustainable Manufacturing
Machining processes generate substantial heat due to friction and plastic deformation, adversely affecting tool life, surface quality, and dimensional accuracy. Effective cooling strategies are therefore essential for improving machining performance and promoting sustainable manufacturing. This study presents a comparative evaluation of cryogenic and conventional cooling techniques with respect to cutting temperature, tool wear, and surface roughness. The analysis is based on theoretical modelling supported by a simulated experimental dataset developed from trends reported in the literature. Performance was assessed using graphical analysis and statistical validation through analysis of variance (ANOVA). The results demonstrate that cryogenic cooling significantly reduces cutting temperature and tool wear while improving surface finish compared with conventional cooling. Furthermore, the elimination of conventional cutting fluids reduces environmental impact and supports sustainable manufacturing practices. Although cryogenic cooling requires higher initial investment, its advantages in terms of extended tool life, improved machining performance, and reduced ecological footprint make it a promising cooling strategy for modern manufacturing applications.
Copyright (c) 2026 Rohit Gavali, Siddhodhan Paikrao (Author) https://creativecommons.org/licenses/by/4.0
Risk Analysis and Management in CNC Machining Operations: A Comprehensive Framework for Hazard Identification, Mitigation, and Process Safety Enhancement
Computer Numerical Control (CNC) machining plays a vital role in modern manufacturing by enabling high precision, productivity, and repeatability. However, CNC operations involve various mechanical, electrical, thermal, and operational hazards that require systematic risk management to ensure workplace safety and process reliability. This review presents a comprehensive overview of risk analysis and management approaches applicable to CNC machining, focusing on widely adopted methodologies such as Failure Mode and Effects Analysis (FMEA), Hazard and Operability Study (HAZOP), and Fault Tree Analysis (FTA). The paper examines their applications, strengths, and limitations in hazard identification, risk assessment, and implementation of effective mitigation strategies. It also discusses recent developments in predictive maintenance, Industry 4.0, and integrated safety management for enhancing manufacturing performance. The review highlights the importance of combining multiple risk assessment techniques to improve safety, reduce operational risks, minimize downtime, and support sustainable manufacturing practices. The findings provide a valuable reference for researchers, manufacturing engineers, and industrial practitioners seeking effective strategies for improving safety and reliability in CNC machining operations.
Failure Mode and Effects Analysis (FMEA)
Copyright (c) 2026 Jay Lapurkar, Siddhant Diwan (Author) https://creativecommons.org/licenses/by/4.0
Integrated Thermal–Structural Risk Assessment of Slotted Brake Disc Manufacturing Using FEA and FMEA Techniques
Slotted brake discs are widely adopted in high-performance automotive braking systems owing to their superior heat dissipation characteristics and improved wet-weather performance. However, the introduction of slot features creates stress concentration zones that may lead to thermal fatigue, crack initiation, and premature structural failure of the brake disc. This paper presents an integrated Risk Assessment and Method Statement (RAMS) framework that combines Finite Element Analysis (FEA) for thermal and structural simulations with Failure Mode and Effects Analysis (FMEA) for systematic risk assessment and quantification. Temperature distributions and Von Mises stress fields are obtained through coupled computational analysis under realistic braking conditions. Ten critical failure modes are identified and ranked using the Risk Priority Number (RPN) methodology. The parametric study compares solid, six-slot, and eight-slot disc configurations fabricated from grey cast iron (IS 210 Gr. FG 260). The results show that slot geometry significantly reduces peak disc temperature while simultaneously increasing localised stress concentrations at the slot edges. The highest RPN recorded is 144 for slot surface wear, while the eight-slot configuration demonstrates the best thermal performance. The proposed RAMS framework provides a structured approach for brake disc geometry optimisation, manufacturing quality planning, and engineering decision-making.
Copyright (c) 2026 Prathamesh Jadhav, Dr. Avinash Somatkar (Author) https://creativecommons.org/licenses/by/4.0
Risk Assessment and Mitigation in Smart Manufacturing Systems Using a Qualitative Risk Matrix Approach
Industry 4.0 technologies, including the Internet of Things (IoT), cyber-physical systems (CPS), artificial intelligence (AI), and automation, have significantly transformed modern manufacturing by improving productivity, flexibility, and operational efficiency. However, the increasing connectivity and complexity of smart manufacturing systems have also introduced new technical, operational, cybersecurity, and safety-related risks that require systematic assessment and management. This paper presents a structured framework for risk identification, assessment, and mitigation using a qualitative risk matrix approach. The proposed methodology classifies manufacturing risks into major categories and evaluates them based on their probability of occurrence and potential impact to support effective risk prioritization. Appropriate mitigation strategies, including predictive maintenance, enhanced cybersecurity measures, system redundancy, real-time monitoring, and workforce training, are discussed to improve system reliability and operational safety. The study highlights that systematic risk assessment enables early identification of critical vulnerabilities, supports informed engineering decision-making, and contributes to more resilient, reliable, and sustainable smart manufacturing systems. The proposed framework provides a practical reference for manufacturing engineers and industrial practitioners implementing risk management within Industry 4.0 environments.
Copyright (c) 2026 Sharvani Jagtap, Aarya Chavan (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.
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