Wireless communication technology has transformed connectivity across industries, but its widespread adoption comes with significant challenges. The purpose of paper is to identify and analyze the most critical obstacles affecting the efficiency, reliability, and scalability of wireless communication systems. This research paper mainly demonstrates to determine the most effective challenges for wireless communication technology. In recent times, it is really very significant and demanding work of this technology-based society. Interference, security vulnerabilities, bandwidth limitations, signal attenuation, and latency concerns etc. are the basic factors of this challenging work. This study explores the application of multi-criteria decision making (MCDM) techniques using intuitionistic fuzzy numbers (IFNs) to evaluate this. We apply the weighted MCDM method, i.e., Entropy in this paper. The decisions of multiple decision makers (DMs) are considered into account when collecting this problem related data and IFNs are utilised as mathematical tools to handle uncertainty. In order to address the ambiguity and inconsistency of the system, we finally conclude to conduct the analysis here with final result.
A comprehensive statistical analysis was conducted to investigate the causes and prioritization of failure modes within a production line manufacturing leather covers for automotive interiors. The study was grounded in a Process Failure Mode and Effects Analysis (PFMEA), with a dual emphasis on evaluating the traditional Risk Priority Number (RPN) approach and the more contemporary Action Priority (AP) methodology, which has been increasingly adopted to enhance risk assessment sensitivity. Failure modes were classified and prioritized using both approaches, revealing notable differences in the ranking outcomes. To further elucidate the underlying contributors to these failure modes, causal factors were systematically categorized in accordance with the 5M+1E framework—Man, Machine, Method, Material, Measurement, and Environment—commonly employed in quality and reliability engineering. A cause-and-effect diagram was constructed to visualize the distribution of root causes across these categories. Descriptive statistics and correlation analyses were employed to quantify the relationship between each category and the prioritized failure modes. Particular attention was paid to examining the interdependencies among the core PFMEA parameters—Severity, Occurrence, and Detection—in order to determine their respective contributions to the variability in failure mode rankings. It was found that Severity exerted the most substantial influence on the prioritization outcomes under the AP model, while Occurrence was more dominant when the RPN method was applied. These findings suggest that the choice of prioritization method significantly alters the interpretation of risk and resource allocation for corrective actions. The integration of 5M+1E categorization with PFMEA metrics offers a structured pathway to enhance the diagnostic capability of reliability assessments and improve decision-making in failure prevention strategies. This approach is proposed as a more robust alternative to traditional analysis, enabling more precise targeting of corrective and preventive measures in high-precision manufacturing environments.
In modern foundry operations, the reliability and operational continuity of sand molding systems are pivotal to maintaining productivity, safety, and competitive advantage. In this study, Failure Mode, Effects, and Criticality Analysis (FMECA) has been employed to systematically evaluate and optimize the performance of a pneumatic molding cell utilized in the production of sand molds. Particular focus has been directed toward the pusher subsystem, which is frequently subjected to high mechanical loads and cyclic stress, rendering it susceptible to recurrent failures that compromise both uptime and process efficiency. Potential failure modes were exhaustively identified, categorized, and prioritized based on their severity, occurrence, and detectability. Critical components, including servo motors, pneumatic actuators, and gearbox assemblies, were found to pose substantial risk to system reliability due to wear-induced degradation, misalignment, and lubrication failure. For each high-priority failure mode, targeted mitigation strategies were proposed, encompassing enhanced condition monitoring, retrofitting of wear-resistant materials, and redesign of high-stress interfaces. Furthermore, failure detection mechanisms were improved through the integration of predictive maintenance protocols and sensor-based diagnostics. Implementation of these recommendations has resulted in measurable reductions in unplanned downtime, repair frequency, and maintenance overhead. This investigation demonstrates that FMECA, though underutilized in conventional foundry environments, offers a structured, data-driven methodology for uncovering latent failure risks and implementing preventive measures in complex industrial systems. By embedding FMECA within routine maintenance frameworks, a substantial improvement in operational resilience and equipment lifespan can be achieved. The findings support the strategic integration of reliability engineering methodologies into sand molding operations, contributing not only to cost efficiency but also to the broader adoption of systematic risk management practices in process-driven manufacturing sectors.
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