Jakub Kopowski

Shape-memory alloys (SMAs) offer low mass, compact integration, quiet operation, and high work density, but their hysteresis and coupled thermal–mechanical behavior complicate repeatable control. This study develops a modular architecture in which natural-language command interpretation is separated from motion-profile generation and from actuator-specific models and controllers. The implemented pilot path was evaluated on a five-servo robotic hand, while the common comparison layer defines interfaces for technology-specific numerical models reserved for the next implementation stage. A locally executed large language model (LLM) converted user commands into port:angle pairs, and the same semantic front-end was coupled to either a fixed-step or zero-order Sugeno motion-profile generator. The semantic layer was evaluated using 60 recorded commands: 27 supported commands and 33 commands requiring rejection. Exact target generation was obtained in 25 of 27 supported cases (92.6%), while 17 of 33 invalid or unsupported commands were correctly rejected (51.5%), giving a strict overall result of 42 of 60 cases (70.0%). Direct observation confirmed the expected physical outcome in 23 of 27 supported cases (85.2%). The mean local response time was 25.30 s, the median was 16.71 s, and the 95th percentile was 66.73 s, confirming that the LLM is suitable only for the supervisory semantic layer. Because the pilot setup did not use an external position sensor, the fuzzy module is interpreted as a command-profile generator rather than a closed-loop position controller. The results validate the layered interface and establish an experimental and computational basis for a subsequent quantitative comparison of SMA and servo actuation.

This article discusses advanced artificial intelligence (AI)-based strategies for the design and personalization of three-dimensionally (3D) fabricated hand exoskeletons, with a focus on adaptive, data-driven methodologies. It highlights the crucial role of intelligent personalization in improving user comfort, functional performance, and rehabilitation outcomes, particularly in medical and care settings. The proposed approach integrates biomechanical modeling, high-resolution 3D scanning, and machine learning (ML) algorithms to create exoskeleton systems tailored to the unique anatomical and motor characteristics of individual users. This article presents both a theoretical framework and practical implementation of AI-based adaptation, addressing key challenges such as precise anatomical fit, ergonomic optimization, and real-time responsiveness. Specific emphasis is placed on AI-based feedback mechanisms that enable continuous, dynamic adjustment of control parameters during device operation. Case studies illustrate the effectiveness of these techniques in improving performance and rehabilitation progress for individual users. By combining intelligent modeling, adaptive control, and additive manufacturing, this research advances the field of wearable robotics and points the way to more accessible, efficient, and fully personalized assistive technologies.

Composite materials, particularly polymers reinforced with carbon, glass, and aramid fibers, enable the development of lightweight yet mechanically robust structures that enhance user comfort and functional performance. Their high strength-to-weight ratio and fatigue resistance make them ideal for applications requiring repetitive movements in rehabilitation and assistive robotics. However, challenges remain related to cost-effective production, durability under complex loading conditions, and ergonomic fit to human anatomy. Recent advances in materials science and smart materials are expanding the possibilities of multifunctional composites with embedded sensors. Furthermore, machine learning methods are increasingly being used to optimize material selection and structural design. Future advances are expected to improve scalability, personalization, and system integration, positioning composite fibers as a key assistive technology in next-generation robotic systems.

3D bioprinting of hydrogel–cell composites requires simultaneous consideration of the biomechanical properties of the printed structures, the construct’s geometric stability, and conditions conducive to cell survival and function. Hydrogel cross-linking techniques and their kinetics play a key role in this process, determining the time of shape fixation, the mechanical strength of the structures, and the mechanical environment in which the cells are located immediately after printing. The relationships between bioprinting parameters, material properties, cross-linking strategies, and the presence of cells are highly nonlinear and often investigated through trial and error, leading to significant time and material costs. This paper proposes an approach based on artificial intelligence-assisted simulation, focusing on computer modeling of the biomechanical and functional parameters of hydrogel–cell composites produced by 3D bioprinting. The methodology is based on data generated from computer simulations and allows for analysis of the impact of printing parameters and different cross-linking strategies on mechanical strength, time-dependent geometric stability, and limitations related to cellular function, including exposure time to non-cross-linked matrices. The use of artificial intelligence methods allows for the integration of simulation results and predictive assessment of material behavior, providing a basis for future optimization of bioprinting parameters and process costs prior to experimental validation.

Automation and sustainability are closely intertwined in the evolution of Industry 4.0 and 5.0, where artificial intelligence (AI) plays a key role in transforming energy consumption and production efficiency. For Industry 4.0, AI-based automation has optimized production, logistics, and resource management, reducing waste and improving throughput through predictive analytics and intelligent control systems. These systems have enabled energy-efficient production lines by automatically adjusting processes to minimize downtime and energy consumption. However, the increasing use of AI and digital infrastructure has also led to an increase in demand for computing energy, raising concerns about data center efficiency and carbon footprint, leading to the division between Green AI and Red AI. Industry 5.0 expands this paradigm, focusing on human–machine collaboration and sustainable design, where AI supports personalization, circular economy practices, and the integration of renewable energy. Generative AI and digital twins (DTs) enable real-time energy modeling, helping companies simulate outcomes and choose the most sustainable paths. Automation also enables predictive maintenance, extending machine life and reducing material waste. At the same time, AI is contributing to the development of decentralized energy systems, such as smart grids and microgrids, which increase resilience and reduce emissions. A key challenge is balancing the energy efficiency benefits of automation with the sustainability of the AI infrastructure itself, which requires innovation in energy-efficient computing and green algorithms. From this perspective, AI-based automation represents both a solution and a challenge: it accelerates the achievement of sustainable development goals while requiring responsible technological management to ensure long-term ecological sustainability.

3D-printed hand exoskeletons are important because they enable the creation of affordable, lightweight, and highly customizable assistive and rehabilitation devices tailored to individual patient needs. Their rapid production and design flexibility accelerate innovation, improve access to therapies, and accelerate functional recovery for people with hand impairments. This article discusses the development of a hand exoskeleton using advanced additive manufacturing. It highlights how Industry 4.0 principles such as digital design, automation, and smart manufacturing enable precise prototyping and efficient use of materials. Moving on to Industry 5.0, the study highlights the role of human–machine collaboration, where customization and ergonomics are prioritized to ensure user comfort and rehabilitation effectiveness. The integration of AI-based generative design and digital twins (DTs) is explored as a path to Industry 6.0, where adaptive and self-optimizing systems support continuous improvement. The perspective of personal experience provides insight into practical challenges, including material selection, printing accuracy, and wearability. The results show how technological optimization can be used to reduce costs, improves efficiency and sustainability, and accelerates the personalization of medical devices. The article shows how evolving industrial paradigms are driving the design, manufacture, and refinement of 3D-printed hand exoskeletons, combining technological innovation with human-centered outcomes.

Soft–rigid interfaces in exoskeletons are key to balancing flexibility and structural support, providing both comfort and function. In our experience, combining Bioflex material with a rigid filament improves mechanical properties while allowing the exoskeleton to adapt to complex hand movements. Flexible components provide adaptability, reducing pressure points and discomfort during prolonged use. At the same time, rigid components provide the stability and force transfer necessary to support weakened grip strength. A key challenge in this integration is achieving a smooth transition between materials to prevent stress concentrations that can lead to material failure. Techniques for providing adhesion and mechanical locking are essential to ensure the durability and longevity of soft and rigid interfaces. One issue we have observed is that rigid filaments can restrict movement if not strategically placed, potentially leading to unnatural hand movement. On the other hand, excessive softness can reduce the force output needed for effective rehabilitation or assistance. Optimizing the interface design requires iterative testing to find the perfect balance between flexibility and mechanical support. In some prototypes, material fatigue in soft sections led to early failure, requiring reinforced hybrid structures. Addressing these issues through better material bonding and geometric optimization can significantly improve the performance and comfort of hand exoskeletons. The aim of this study was to investigate the transition between rigid and soft materials for exoskeletons.

This review article examines the role of additive manufacturing (AM) in increasing energy efficiency and sustainability within the evolving framework of Industry 5.0 and 6.0. This review highlights the unique ability of additive manufacturing to deliver mass-customized products while minimizing material waste and reducing energy consumption. The integration of smart technologies such as AI and IoT is explored to optimize AM processes and support decentralized, on-demand manufacturing. Thisarticle discusses different AM techniques and materials from an environmental and life-cycle perspective, identifying key benefits and constraints. This review also examines the potential of AM to support circular economy practices through local repair, remanufacturing, and material recycling. The net energy efficiency of AM depends on the type of process, part complexity, and production scale, but the energy savings per component can be significant if implemented strategically.AM significantly improves energy efficiency in certain manufacturing contexts, often reducing energy consumption by 25–50% compared to traditional subtractive methods. The results emphasize the importance of innovation in both hardware and software to overcome current energy and sustainability challenges. This review highlights AM as a key tool in achieving a human-centric, intelligent, and ecological manufacturing paradigm.

Machine learning (ML) is transforming the evaluation of 3D printing materials, enabling more efficient and accurate assessment of material properties, including their sustainable life cycle. ML algorithms can analyze vast amounts of data from previous printing processes to predict the performance of different materials (including those used in multi-material printing) under different conditions. This predictive ability helps in selecting the most suitable materials for specific printing tasks, optimizing the mechanical, chemical, and overall quality of the final product. Furthermore, by integrating real-time data from sensors during the printing process, ML can continuously monitor and adjust parameters, ensuring optimal material utilization and reducing waste. ML models can identify and correct defects in printed materials by recognizing patterns associated with defects, thus improving the reliability of 3D-printed objects. This approach reduces the need for expensive and time-consuming physical tests. This accelerates the pace of 3D printing development but also increases the precision of material selection and processing, contributing to more efficient use of materials and energy for printing.

W obliczu dynamicznych zmian technologicznych zrozumienie i rozwijanie kompetencji cyfrowych staje się nie tylko potrzebą, ale i koniecznością, również dla młodych ludzi, których uczestnictwo w procesach społeczno-politycznych jest jednym z elementów budowania społeczeństwa obywatelskiego. Celem artykułu jest analiza kompetencji cyfrowych studentów, ze szczególnym uwzględnieniem umiejętności bezpiecznego korzystania z internetu i urządzeń elektronicznych oraz reakcji na zagrożenia występujące w sieci. W oparciu o badania ilościowe przeprowadzone w grupie studentów Uniwersytetu Kazimierza Wielkiego w Bydgoszczy (N=735) zweryfikowano, czy techniczny lub nietechniczny typ wykształcenia różnicuje te kompetencje. Wnioski z badań wskazują, że w zakresie podstawowych zasad bezpieczeństwa w sieci i reagowania na zagrożenia studenci nie różnią się, jednak zarówno zaawansowane umiejętności, jak i doświadczenia negatywnych zdarzeń w sieci częściej dotyczą studentów kierunków technicznych.