Against the background of the unprecedented advancements related to Industry 4.0 and beyond, transitioning from classical mathematical models to fully embodied digital twins represents a critical change in the planning, monitoring, and optimization of complex industrial systems. This work outlines the subject within the broader field of applied mathematics and computational simulation while highlighting the critical role of sound mathematical foundations, numerical methodologies, and advanced computational tools in creating data-informed virtual models of physical infrastructures and processes in real time. The discussion includes examples related to smart manufacturing, additive manufacturing technologies, and cyber–physical systems with a focus on the potential for collaboration between physics-informed simulations, data unification, and hybrid machine learning approaches. Central issues including a lack of scalability, measuring uncertainties, interoperability challenges, and ethical concerns are discussed along with rising opportunities for multi/macrodisciplinary research and innovation. This work argues in favor of the continued integration of advanced mathematical approaches with state-of-the-art technologies including artificial intelligence, edge computing, and fifth-generation communication networks with a focus on deploying self-regulating autonomous digital twins. Finally, defeating these challenges via effective collaboration between academia and industry will provide unprecedented society- and economy-wide benefits leading to resilient, optimized, and intelligent systems that mark the future of critical industries and services.

This research evaluates a novel, modular, open-source, and low-cost educational robotic platform in Educational Robotics and STEM Education. It is the sequel of an action research cycle on which the development of this robot is based. The impetus for the need to develop this came from the evaluation of qualitative and quantitative research data collected during an educational robotics event with significant participation of students in Athens, which showed an intense interest in students in participating in educational robotics activities, but—at the same time—recorded their low involvement due to the high cost of educational robots and robotic platforms. Based on the research’s findings, this robot was designed to suit the whole educational community; its specifications came from its members’ needs and the processing and analysis of qualitative and quantitative data. This paper presents an evaluation of the robot using the Technology Acceptance Model. The robot was exposed to 116 undergraduate students attending a pedagogical university department to evaluate its handling according to the model’s factors. Research results were promising and showed a high degree of acceptance of the robot by these students and future teachers, providing the impetus for further research.

All kinds of vessels consist of dozens of complex machineries with rotating parts and electric motors that operate continuously in harsh environments with excess temperature, humidity, vibration, fatigue, and load. A breakdown or malfunction in one of these machineries can significantly impact a vessel’s operation and safety and, consequently, the safety of the crew and the environment. To maintain operational efficiency and seaworthiness, the shipping industry invests substantial resources in preventive maintenance and repairs. This study presents the economic and technical benefits of predictive maintenance over traditional preventive maintenance and repair by replacement approaches in the maritime domain. By leveraging modern technology and artificial intelligence, we can analyze the operating conditions of machinery by obtaining measurements either from sensors permanently installed on the machinery or by utilizing portable measuring instruments. This facilitates the early identification of potential damage, thereby enabling efficient strategizing for future maintenance and repair endeavors. In this paper, we propose and develop a convolutional neural network that is fed with raw vibration measurements acquired in a laboratory environment from the ball bearings of a motor. Then, we investigate whether the proposed network can accurately detect the functional state of ball bearings and categorize any possible failures present, contributing to improved maintenance practices in the shipping industry.

In every business, the production of knowledge, coming from the process of effective information, is recognized as a strategic asset and source of competitive advantage. In the field of railways, a vast amount of data are produced, which is necessary to be assessed, deployed in an optimum way, and used as a mechanism, which will lead to making the right decisions, aiming at saving resources and maintain the fundamental principle of the railways which is the passengers’ safety. This paper uses stored-inactive data from a Greek railway company, and uses the method of data mining and applies machine learning techniques to create strategic decision support and draw up a risk and control plan for trains. We make an effort to apply Machine Learning open source software (Weka) to the obsolete procedures of maintenance of the rolling stock of the company (hand-written work orders from the supervisors to the technicians, dealing with the dysfunctions of a train unit by experience, the lack of planning and coding of the malfunctions and the maintenance schedule). Using the J48 and M5P algorithms from the Weka software, data are recorded, processed, and analyzed that can help monitor or discover, with great accuracy, the prevention of possible damage or stresses, without the addition of new recording devices—monitoring on trains, with the aim of predicting the diagnosis of the train fleet. The innovative method is capable of being used as a tool for the optimization of the management’s performance of the trains to provide the appropriate information for the implementation of planning and the technical ability of the trains in order to achieve the greatest target of importance for the railways, which is the passengers’ safety.

The integration of 3D printing with smart infrastructure presents a transformative opportunity in urban planning, construction, and engineering, enhancing efficiency, flexibility, and sustainability. By leveraging additive manufacturing alongside digitalization, artificial intelligence (AI), and the Internet of Things (IoT), this technology enables the creation of customized, lightweight, and sensor-embedded structures. This work analyzes both the advantages and challenges of applying 3D printing in smart infrastructure, focusing on material optimization, rapid prototyping, and automated fabrication, which significantly reduce construction time, labor costs, and material waste. Applications such as 3D-printed bridges, modular housing, and IoT-integrated urban furniture exhibit its potential in contributing towards resilient and resource-efficient cities. However, despite these benefits, significant challenges hinder large-scale adoption. Issues of scalability, particularly in the fabrication of large and load-bearing structures, remain unresolved, requiring advancements in high-speed printing techniques, material reinforcement strategies, and hybrid construction methods. Furthermore, regulatory uncertainties and the absence of standardized guidelines create barriers to implementation. The lack of comprehensive building codes, certification protocols, and quality assurance measures for 3D-printed structures limits their widespread acceptance in mainstream construction. Overcoming these limitations necessitates research into AI-driven process optimization, multi-material printing, and international standardization efforts. By assisting towards overcoming these challenges, 3D printing has the potential to redefine urban development, making infrastructure more adaptive, cost-effective, and environmentally sustainable. This work provides a critical evaluation of the current capabilities and limitations of 3D printing in smart infrastructure towards achieving full-scale implementation and regulatory compliance.

The integration of 3D printing technologies in automated manufacturing systems marks a significant progression in the manufacturing industry, enabling elevated degrees of customization, efficiency, and sustainability. This paper explores the synergy between 3D printing and automation by conducting a critical literature review combined with case study analysis, focusing on their roles in enhancing production lines within the framework of Industry 4.0 and smart factories. Key opportunities presented by this integration include mass customization at scale, reduced material waste, and improved just-in-time manufacturing processes. However, challenges related to quality control, scalability, and workforce adaptation remain critical issues that require careful consideration. The study also examines the emerging role of hybrid manufacturing systems that combine additive and subtractive processes, alongside the growing need for standardized regulations and frameworks to ensure consistency and safety. Case studies are highlighted, showcasing real-world applications of automated 3D printing technologies and AI-driven print optimization techniques. In conclusion, this paper contributes to advancing the scholarly understanding of automated 3D printing by synthesizing technical, organizational, and regulatory insights and outlining future trajectories for sustainable and agile production ecosystems.