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Hard turning is a precision machining process used in the manufacturing industry for the finishing of hardened alloy steel, which is known for its high hardness and wear resistance. In this work, an experimental investigation was conducted to predict surface roughness and cutting temperature during the hard turning of AISI 52100 steel using the minimal cutting fluid application (MCFA). The MCFA is a sustainable high-velocity pulsed jet technique that has emerged as an eco-friendly approach for reducing the environmental impact and improving surface integrity in machining processes. The influence of key machining parameters, such as cutting speed, feed rate, and depth of cut, on the performance indicators was modeled using the response surface methodology (RSM) and the artificial neural network (ANN). The RSM was employed for a structured, statistical analysis, while an ANN provided a data-driven approach for capturing complex non-linear relationships. Various network architectures were established and evaluated with a fixed number of cycles. Results showed that the ANN exhibited superior accuracy in predicting both responses. In comparison to the QR model, the ANN exhibited the lowest average error rate in accurately predicting the response. This was further validated through experimental trials, demonstrating that the ANN consistently outperformed the RSM across different parameter settings. Additionally, the use of the MCFA contributed to sustainable manufacturing by minimizing the use of cutting fluids while maintaining machining quality.

Over time, solar Photovoltaic (PV) systems experience a decline in performance and reliability due to various environmental factors. Fault Tree Analysis (FTA) can be used to assess the reliability of these systems and identify faults and failure modes that can significantly impact the entire PV system’s performance. However, in practice, obtaining accurate failure probability values for the components of a solar PV system is challenging since systems operate in an ever-changing environment, resulting in a scarcity of data for statistical estimation. This paper proposes a fuzzy theory-based FTA approach to obtain the failure probabilities of the faults more accurately. The Fuzzy-FTA methodology converts experts’ subjective opinions expressed in linguistic terms into a Failure Possibility Score (FPS), which is then converted into a failure probability value. The results of the proposed approach are compared to those obtained through the conventional FTA to assess its effectiveness, applicability, feasibility, and efficiency. Soiling, dust accumulation, inadequate system maintenance, bad system configuration, bird dropping, delamination, improper installation, shading, grounding, and discoloration are the most critical faults and impact on the performance and reliability of solar PV systems.

Reliability, Availability, Maintainability, and Dependability (RAMD) study of Tube-wells Integrated with Underground Pipelines (TIUP) is crucial as they are the backbone of the irrigation system. This study is carried out with an objective to perform RAMD analysis, and Failure Modes and Effects Analysis (FMEA) unified with the development of a novel stochastic model using Markovian approach to estimate the Steady-State Availability (SSA) of the TIUP. A real case study of a conventional TIUP system has been performed to validate theoretical and practical results of the proposed model. The failure and repair rates of all subsystems followed exponential distribution, and their impact on system/subsystem’s availability and other reliability measures has been investigated. All the repairs are perfect and random variables associated with failure and repair rates are statistically independent. The centrifugal pump and power supply units are the most critical components as far as reliability and maintainability aspects. The labor also plays a critical role in the operation of the TIUP system.

This paper analyzes the surface quality characteristics, such as arithmetic average roughness (Ra), maximum peak-to-valley height (Rt), and average peak-to-valley height (Rz), in hard turning of AISI 52100 steel using a (TiN/TiCN/Al2O3) coated carbide insert under a minimal cutting fluid environment (MCFA). MCFA, a sustainable high-velocity pulsed jet technique, reduces harmful effects on human health and the environment while improving machining performance. Taguchi’s L27 orthogonal array was used to conduct the experiments. The findings showed that surface roughness increases with feed rate, identified as the most influential parameter, while the depth of cut shows a negligible effect. The main effects plot of signal-to-noise (S/N) ratios for the combined response of Ra, Rt, and Rz revealed the optimal cutting conditions: cutting speed of 140 m/min, feed rate of 0.05 mm/rev, and depth of cut of 0.3 mm. Feed rate ranked highest in influence, followed by cutting speed and depth of cut. The lower values of surface roughness parameters were observed in the ranges of Ra ≈ 0.248–0.309 µm, Rt ≈ 2.013–2.186 µm, and Rz ≈ 1.566 µm at a feed rate of 0.05–0.07 mm/rev. MCFA-assisted hard turning reduces surface roughness by 35–40% compared to dry hard turning and 10% to 24% when compared to the MQL technique. Moreover, this study emphasizes the significant environmental benefits of MCFA, as it incorporates minimal eco-friendly cutting fluids that minimize ecological impact while enhancing surface finish.

This paper presents the design, kinematics, and development of a tendon-driven continuum robot for surgical applications. The continuum robot has a flexible and adaptable construction that imitates the movements of natural organisms. The robot’s unique structure comprises disk members, springs, and a continuum backbone member, enabling it to bend, contract, and deform in complex ways. The robot is operated by pulling tendons, giving it the agility and flexibility necessary to bend in confined spaces. This study discusses the main design considerations and challenges in creating a tendon-driven continuum robot, including the kinematics of the four-tendon mechanism. The developed tendon-driven continuum robot is categorized into two modules: the distal end and the proximal end. The distal end consists of the continuum robot structure, whereas the proximal module consists of the actuating unit that actuates the distal end. The experimental results demonstrate the continuum robot’s ability to be used in medical fields and pipe inspections because of the miniaturized design of the distal end, which allows it to enter confined spaces. This paper provides valuable insights into the design, kinematics, and appropriate materials to build a tendon-driven continuum robot; its bending and deformation capabilities can be used in many fields, especially surgical applications and confined space explorations.

Research on and the demand for hydrogen-powered vehicles have grown significantly over the past two decades as a solution for sustainable transportation. Bibliometric analysis helps to assess research trends, key contributions, and the impact of studies focused on the safety and reliability of hydrogen-powered vehicles. This study provides a novel methodology for bibliometric analysis that systematically evaluates the global research landscape on hydrogen-powered vehicle reliability using Scopus-indexed publication data (1965 to 2024). Eighteen key parameters were identified for this study that are often used by researchers for the bibliometric analysis of hydrogen-related studies. Data analytics, VOSviewer-based visualization, and research impact indicators were integrated to comprehensively assess publication trends, key contributors, and citation networks. The analysis revealed that hydrogen-powered vehicle reliability research has experienced significant growth over the past two decades, with leading contributions from high-impact journals, renowned institutions, and influential authors. The present study emphasizes the significance of greater funding as well as open-access distribution. Furthermore, while major worldwide institutions have significant institutional relationships, there are gaps in real-world hydrogen infrastructure evaluations, large-scale experimental validation, and policy-driven research.

This paper describes a simulation study on the hard turning of AISI 52100 alloy steel with coated carbide tools under minimal cutting fluid conditions using the commercial software AdvantEdge. A finite element analysis coupled with adaptive meshing was carried out to accurately capture temperature gradients. To minimise the number of experiments while optimising the cutting parameters along with fluid application parameters, a cutting speed (v) of 80 m/min, feed rate (f) of 0.05 mm/rev, depth of cut (d) of 0.15 mm, nozzle stand-off distance (NSD) of 20 mm, jet angle (JA) of 30°, and jet velocity (JV) of 50 m/s were observed to be the optimal process parameters based on the combined response’s signal-to-noise ratios. The effects of each parameter on machined surface temperature, cutting force, cutting temperature, and tool–chip contact length were determined using ANOVA. The depth of cut affected cutting force, while cutting speed and jet velocity affected cutting temperature and tool–chip contact length. Cutting speed influenced machined surface temperature significantly, whereas other parameters showed minimal effect. Nozzle stand-off distance exhibited less significant effect. Taguchi optimisation determined the optimal combination of process parameters for minimising thermal effects during hard turning. Cutting temperature and cutting force simulation results were found to be highly consistent with experimental results. On the other hand, the simulated results for the tool–chip contact length and machined surface temperature were very close to the values found in the literature. The result validated the finite element model’s ability to accurately simulate thermal behaviour during hard-turning operations.

A Heating, Ventilation, and Air Conditioning (HVAC) system is often utilized in passenger vehicles to enhance the comfort of both the driver and the passengers. The reliability of an HVAC system refers to the probability that a component within the system will fulfil its intended function during a specified timeframe while operating according to the predefined operational and environmental conditions. Conducting a reliability analysis for the HVAC system of a passenger vehicle is crucial to ensure safety, comfort, cost-effectiveness, and a positive standing. A methodology for analyzing the reliability analysis of a HVAC system using field failure data were developed to identify the critical failure modes, components, and subsystems. A detailed Pareto analysis was applied at subsystem and failure mode levels in order to prioritize them accordingly to their failure frequency. The analysis showed that the A/C evaporator and blower front sides were observed to be the most critical subsystems, contributing to approximately 50% of all failures. Furthermore, the leakages at the joints and vibrations are the primary failure modes of the HVAC system. The Weibull++ software package (version 2021) was used to estimate the best-fit probability distributions for each subsystem and system reliability modelling using a Reliability Block Diagram. The results show that the exponential distribution fits well for several subsystem’s Time-To-Failure (TTF) data and show that the failures were random and due to external reasons.

Purpose Reliability, maintainability and availability of modern complex engineered systems are significantly affected by four basic systems or elements: hardware, software, organizational and human. Computerized Numerical Control Turning Center (CNCTC) is one of the complex machine tools used in manufacturing industries. Several research studies have shown that the reliability and maintainability is greatly influenced by human and organizational factors (HOFs). The purpose of this paper is to identify critical HOFs and their effects on the reliability and maintainability of the CNCTC. Design/methodology/approach In this paper, 12 human performance influencing factors (PIFs) and 10 organizational factors (OFs) which affect the reliability and maintainability of the CNCTC are identified and prioritized according to their criticality. The opinions of experts in the fields are used for prioritizing, whereas the field failure and repair data are used for reliability and maintainability modeling. Findings Experience, training, and behavior are the three most critical human PIFs, and safety culture, problem solving resources, corrective action program and training program are the four most critical OFs which significantly affect the reliability and maintainability of the CNCTC. The reliability and maintainability analysis reveals that the Weibull is the best-fit distribution for time-between-failure data, whereas log-normal is the best-fit distribution for Time-To-Repair data. The failure rate of the CNCTC is nearly constant. Nearly 66 percent of the total failures and repairs are typically due to the hardware system. The percentage of failures and repairs influenced by HOFs is nearly only 16 percent; however, the failure and repair impact of HOFs is significant. The HOFs can increase the mean-time-to-repair and mean-time-between-failure of the CNCTC by nearly 65 and 33 percent, respectively. Originality/value The paper uses the field failure data and expert opinions for the analysis. The critical sub-systems of the CNCTC are identified using the judgment of the experts, and the trend of the results is verified with published results.

PurposeThe demand of sewage treatment plants is increasing day by day, especially in the countries like India. Biological and chemical unit of such sewage treatment plants are critical and needs to be designed and developed to achieve desired level of reliability, maintainability and availability.Design/methodology/approachThis paper investigates and optimizes the availability of biological and chemical unit of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process. A set of Chapman–Kolmogorov differential equations are derived for the model and a generalized solution is discovered using soft computing techniques namely genetic algorithm (GA) and particle swarm optimization (PSO).FindingsNature-inspired optimization techniques results of availability function depicted that PSO outperforms GA. The optimum value of the availability of biological and chemical processing unit is 0.9324 corresponding to population size 100, the number of evolutions 300, mutation 0.6 and crossover 0.85 achieved using GA while PSO results reflect that optimum achieved availability is 0.936240 after 45 iterations. Finally, it is revealed that PSO outperforms than GA.Research limitations/implicationsThis paper investigates and optimizes the availability of biological and chemical units of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process.Originality/valueAvailability model of biological and chemical units of a sewage treatment is developed using field failure data and judgments collected from the experts. Furthermore, availability of the system has been optimized to achieve desired level of reliability and maintainability.