Abstract This brief review explores the transformative convergence of data science and mechanical engineering, emphasizing its growing relevance for both industry and small to medium enterprises (SMEs). The authors argue that data science integrating mathematics, statistics, machine learning, and domain expertise enhances traditional engineering practices by enabling the development of predictive, adaptive, and intelligent systems. Mechanical engineers, with their strong foundation in modeling, physics, and... This brief review explores the transformative convergence of data science and mechanical engineering, emphasizing its growing relevance for both industry and small to medium enterprises (SMEs). The authors argue that data science integrating mathematics, statistics, machine learning, and domain expertise enhances traditional engineering practices by enabling the development of predictive, adaptive, and intelligent systems. Mechanical engineers, with their strong foundation in modeling, physics, and systems thinking, are well positioned to adopt data science methods to improve design, analysis, and maintenance processes. Key applications highlighted include predictive maintenance, generative and simulation driven product design, process and supply chain optimization, real time quality control, and energy management. The paper outlines a standard data science workflow data collection, preparation, exploration, modeling, and communication and illustrates how each phase supports engineering decision making. Notably, the authors emphasise that SMEs can leverage affordable cloud platforms, open source tools, and IoT devices to implement these techniques without substantial investment. The integration fosters a data driven mindset, turning raw operational data into strategic assets that drive efficiency, innovation, and sustainability. Ultimately, the fusion of data science with mechanical engineering represents a paradigm shift toward evidence based, intelligent engineering systems capable of addressing 21st-century industrial challenges. Read More Read Less

Abstract This work presents a multidisciplinary framework for modeling and managing operating theatre (OT) systems through a multi-agent system (MAS) integrated into an Interactive Decision Support System (IDSS). Recognizing OTs as critical bottlenecks in hospital workflows, the authors address the challenge of frequent schedule disruptions particularly from emergency cases by proposing a real-time, adaptive rescheduling architecture. The system features supervisor agents for global coordination and... This work presents a multidisciplinary framework for modeling and managing operating theatre (OT) systems through a multi-agent system (MAS) integrated into an Interactive Decision Support System (IDSS). Recognizing OTs as critical bottlenecks in hospital workflows, the authors address the challenge of frequent schedule disruptions particularly from emergency cases by proposing a real-time, adaptive rescheduling architecture. The system features supervisor agents for global coordination and service agents representing operational units (e.g., operating rooms, surgical teams). These cognitive agents negotiate, prioritize, and allocate resources dynamically using algorithms for resource allocation, unavailability handling, and information retrieval. Decision making is enhanced by the ELECTRE III multi-criteria outranking method, which resolves conflicts based on clinical urgency and resource availability. Implemented in JADE, the platform uses FIPA standards to ensure robust, interoperable agent communication. Beyond software coordination, the paper also outlines key principles for OT equipment design including ergonomics, sterility, modularity, safety, and interoperability and emphasizes human centered design, regulatory compliance (e.g., ISO 13485, IEC 60601), and sustainability. Emerging trends such as AI, IoT, and digital twins are noted as enablers of smart, resilient operating theatres. The approach aims to optimize resource utilization, reduce delays, and improve res ponsiveness in high pressure surgical environments, filling a gap left by prior domain specific MAS application that overlook emergency surgical coordination. Read More Read Less

Abstract The paper presents a mechanical engineering focused analysis of 3D printing machine design, emphasizing structural integrity, motion control, extrusion mechanics, and thermal regulation. It outlines the core mechanical subsystems structural frame, motion transmission, printing head, material feeding, and build platform and discusses their roles in ensuring print accuracy and reliability. Key design considerations include minimizing frame deflection, optimizing motion kinematics to reduce vibration, and... The paper presents a mechanical engineering focused analysis of 3D printing machine design, emphasizing structural integrity, motion control, extrusion mechanics, and thermal regulation. It outlines the core mechanical subsystems structural frame, motion transmission, printing head, material feeding, and build platform and discusses their roles in ensuring print accuracy and reliability. Key design considerations include minimizing frame deflection, optimizing motion kinematics to reduce vibration, and maintaining consistent extrusion flow and temperature. The paper contrasts early, low cost FDM printer designs with modern advancements, highlighting innovations such as multi material compatibility, integrated 3D scanning, and parameter optimization for improved print quality. It also addresses common mechanical failure modes such as layer shifting, nozzle clogging, warping, and vibration and proposes engineering solutions, including closed loop control, reinforced frames, and heated chambers. Looking ahead, the paper identifies emerging trends: hybrid CNC–AM systems, real time force and temperature feedback, lightweight composite frames, and multi axis (5-DOF) printing for complex geometries. In conclusion, the authors assert that refined mechanical design is critical to expanding 3D printing’s industrial applicability, particularly by achieving high precision, material versatility, and reliability for end use part production. Despite current challenges in stability and surface quality, ongoing innovations in machine architecture and control systems are positioned to drive the future evolution of additive manufacturing. Read More Read Less