Industry 4.0 marks a new phase of industrial transformation, driven by the integration of advanced technologies such as industrial robotics, the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and cyber‑physical systems (CPS). The Republic of Korea and Singapore are global frontrunners in this domain, ranking first and second worldwide in robot density per 10,000 manufacturing workers. This paper explores how the strategic integration of robotics with key Industry 4.0 technologies contributes to smart manufacturing and enhanced industrial performance. Using a comparative case study approach, the research analyzes national policies, investments in R&D and education, 5G infrastructure, and support for innovation ecosystems that have enabled these countries to develop flexible, automated, and intelligent production systems. Findings indicate that both Korea and Singapore have successfully combined robotics with IoT, big data analytics, and cloud platforms to create efficient and adaptive manufacturing environments. The study emphasizes that robotization alone is not sufficient; its effectiveness depends on alignment with broader digital transformation strategies. Based on longitudinal data from 2013 to 2023, sourced from the International Federation of Robotics (IFR), the OECD, and national innovation agencies, the research highlights how coordinated implementation of Industry 4.0 technologies fosters sustainable and globally competitive manufacturing.

The article aims to mathematically model processes that occur in non-metallic heterogeneous materials when active thermography methods were used for deep study control. Currently, the trend in industrial development is using non-metallic heterogeneous mediums as a system of various components as structural materials. Such materials are characterized by improved physical and mechanical properties, which can be adjusted by selecting compositions and the ratio of component phases and macrostructure. At the same time, they are characterized by disadvantages such as variability of volume and time properties and the presence of various defects. Effective control methods are of particular importance to ensure the quality and reliability of products made from materials of this class. In the article, the authors analyzed the capabilities of existing non-destructive testing methods for flaw detection of products made of non-metallic heterogeneous materials. When developing a new and improving an existing measurement method, the problem of establishing a set of radiation parameters was used. This allowed for describing the state of the material with the necessary accuracy and the functional connections of these parameters with the latter’s characteristics.

Integrating service robots into contemporary healthcare systems has significantly advanced the scope and complexity of robotic design, especially regarding the materials used in direct interaction with patients and sterile medical environments. This article investigates the pivotal role of biomaterials in shaping both the structural integrity and functional performance of service robots. A key focus was placed on the selection criteria, biocompatibility, sterilization potential, and adaptability of advanced biomaterials used in components that demand mechanical efficiency and safety. A key focus was also placed on the quantitative selection criteria for these materials, including mechanical strength (e.g., tensile strength of more than 50 MPa for polymeric joints), elasticity (Young’s modulus ranging from 10–1000 MPa depending on the application), and biocompatibility ratings based on the ISO 10993 standard.Particular attention was paid to integrating biocompatible polymers and composites that should withstand repeated sterilization cycles (up to 100 autoclave exposures without structural degradation) while maintaining antimicrobial surfaces and hypoallergenic properties. Additionally, the study explored the application of smart materials (e.g., stimuli-responsive hydrogels and shape-memory alloys), which showed response times under 5 s and deformation recovery rates above 90 %, enabling adaptive robotic behavior in dynamic contexts. The study also outlines current research trends, i.e., using responsive polymers, bioinspired composites, and additive manufacturing techniques that enable personalized robotic solutions. Additive manufacturing techniques were analyzed as enablers of rapid prototyping and patient-specific customization, with the article referencing case studies where 3D-printed biopolymer components reduced development time by 40 % and improved fitting precision in assistive robotic devices by 30 %.Emerging research trends were finally examined through bibliometric data, indicating 3.5 times increase in publications related to “biomaterials in medical robotics” from 2015 to 2024 in Scopus. Overall, the research critically examined the challenges associated with material certification processes, emphasizing that the average duration required to obtain regulatory approval typically spans between 18 and 24 months, posing a significant barrier to the timely deployment of advanced robotic systems in actual environments. By adopting an interdisciplinary perspective that combines materials science and robotics engineering, this study underscores the transformative impact of biomaterials in redefining the capabilities, safety, and personalization of medical service robots. The findings highlight technological advancements and future directions in robotic systems’ sustainable and intelligent deployment.

Industry 4.0 represents a new chapter in the development of manufacturing systems, where digitalization, automation, and the application of advanced technologies become key drivers of competitiveness. The textile industry, traditionally characterized by manual processes, is undergoing a profound transformation through the integration of next-generation robotics. This paper analyzes the significance and impact of robotic implementation within the Industry 4.0 framework on process efficiency, flexibility, and sustainability in textile production. Special attention is given to the application of collaborative and autonomous robots, which enable smart work organization, optimized transport and storage, and adaptive production flow management. The study highlights both the benefits brought by the adoption of advanced robotic systems and the challenges encountered during their implementation, such as the need for digital competencies among the workforce and high investment costs. Through the analysis of current trends and examples of good practice, the paper points to key development directions aimed at enhancing innovation, sustainability, and global competitiveness of the textile sector. The conclusion emphasizes the necessity of a strategic approach and continuous investment in new technologies to ensure a successful transition toward the smart factory of the future.

: It is known that in recent years there have been major changes in all branches of industry, especially in the automotive and electro-electronic industry, because new business methods are on the scene, and production processes are being transformed so that they are flexible. In the automotive and electro-electronic industry, the leading technology is robotic technology, the application of which increases the return on investment. Advanced robotics as the basic technology of Industry 4.0 in the new era of production in the automotive and electro-electronic industry plays a very important role because it enables: mobility, readiness, reliability, adaptability, transformation of production, integration with machines, increase of flexibility, improvement of quality, storage and production systems integrated as Cyber-Physical Systems, workers are freed from routine and repetitive tasks. The paper provides an overview of applied and issued patents in robotic technology, the application of robots in the World and China as the leader in the implementation of robotic technology in the world. An analysis of the implementation of industrial robots, as well as advanced robots in the automotive and electro-electronic industries of China, is given, as well as the forecast of the application in the coming years.

In recent advancements in robotics, Artificial Intelligence (AI) methods such as Deep Learning, Deep Reinforcement Learning (DRL), Transformers, and Large Language Models (LLMs) have significantly enhanced robotic capabilities. Key AI models driving advancements in robotic vision include Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), the DEtection Transformers (DETR), the YOLO family of algorithms, segmentation techniques, and 3D vision technologies. Deep Reinforcement Learning (DRL), an AI technique where agents learn optimal behaviors through trial and error interactions with their environment, enables robots to perform complex tasks autonomously. Transformers, originally developed for natural language processing, have been adapted to robotics for tasks involving sequence prediction and data understanding, improving perception and decision-making processes. LLMs leverage vast amounts of text data to enhance robot-human interaction, enabling robots to understand and generate human-like language, thus improving their communicative and collaborative abilities in various applications. The integration of these AI methods enhances the adaptability, efficiency, and overall performance of robotic systems, paving the way for more sophisticated and intelligent autonomous agents.

In the last ten years, the development and research of advanced technologies, as well as their application in all segments of society, have led to major changes and reshaping of the new world. New innovations are occurring on a daily basis, but their application is not going fast enough due to the rigid infrastructure. However, in order to secure an optimal future, we all have to adapt to the changes that are coming. The developed countries have adopted the strict implementation of advanced technologies of Industry 4.0, some of which include: Internet of Things (IoT), Big Data, Cloud Computing, smart sensors, Radio Frequency Identification (RFID), 3D printing, advanced security systems, Virtual and Augmented Reality (VAR), etc. Robotics is the basic and first technology that has been implemented since the 60s of the last century, with artificial intelligence coming in the spotlight in the last ten years. Artificial intelligence is becoming a key to the development of advanced robots, as it enables them to adapt to unpredictable situations, to learn from experience and make intelligent decisions.Robots use AI to process sensor data, navigate, recognize objects, plan paths and interact with the environment. In short, artificial intelligence enables robots to be smart, whereas robotics uses AI to create autonomous and useful devices. This symbiosis contributes to progress in many industries, including healthcare, manufacturing and transportation. Artificial intelligence (AI) and robotics are two key fields that complement each other. The paper presents the trend of applied and approved patents in artificial intelligence and robotics, as well as an example of the use of artificial intelligence in advanced robots to perform certain tasks. Artificial intelligence (AI) is having an increasing impact on robotics, opening up many possibilities.

Using AI through industries and business processes is increasingly becoming the subject of theorists and practitioners. In the HRM process, the use of AI gives companies numerous advantages in employee performance, and processes, but also presents them with organizational, financial, technical, legal, and personnel challenges. This paper explores the application of AI systems in recruitment and selection through gamification strategies, people analytics, talent intelligence, AI platforms, video interviews, and conversational AI. It provides an overview of the benefits and challenges associated with their implementation. Additionally, the paper delves into ethical considerations and legislation, focusing on the EU Act, domestic laws, and ISO AI standards. The primary goal of this paper is to provide a comprehensive understanding of AI's role in HR processes and the complexities of implementing AI solutions in recruitment and selection.

The first industrial robots appeared in the production processes of the 60s of the last century, and they are implemented to this day in all production processes in the world. The biggest application of industrial robots has been found in three industries in the world: the automotive industry, the electrical/electronic industry and the metal industry. The automotive industry is the first to implement the most industrial robots, and in recent years the electrical/electronics industry has also joined in, as these two industries in the world implement more than 60% of the total industrial robots implemented in the world. The use of industrial robots has been used to perform those tasks that are tiring and hazardous to the health of workers, which include welding, and the performance of these operations is mostly n the automotive industry. To date, the most implemented industrial robots of the first generation, which are robustly surrounded by fences (for the protection of workers), take up a lot of space and are complicated to reprogram. Development of new technologies such as: sensor technology, Internet of Things (IoT), big data, „cloud computing“, virtual and augmented reality (AR), artificial intelligence (AI), advanced security systems and others is credited with the development of robotic technology. In this paper is shown the trend of implementing industrial robots and their role in the welding process.