Designs, Volume 9, Issue 4 (August 2025) – 16 articles

Total knee replacement (TKR) is a common procedure for pain relief and restoration of the mobility of the knee joint in patients with severe knee joint problems. Despite this, some patients still suffer from stiffness, instability, or pain caused by soft tissue imbalance, malalignment, or implant-related issues. Previously, surgeons have had to use their experience and visual judgment to balance the knee, which has resulted in variability of outcomes. Smart knee implants are addressing these issues by using sensor technology to provide real-time feedback on joint motion, pressure distribution, and loading forces. This enables more accurate intra-operative adjustment, enhancing implant positioning and soft tissue balance and eliminating post-operative adjustment. These implants also enable post-operative monitoring, simplifying the ability to have more effective individualized rehabilitation programs directed at optimizing patient mobility and minimizing complications. While the patient pool for smart knee implantation remains not commonly documented, it was found in a study that 83.6% of the patients would opt to have the monitoring device implemented, and nearly 90% find reassurance in monitoring their healing indicators. As the number of knee replacements is likely to rise due to aging populations and the rising prevalence of joint disease, smart implants are a welcome development in orthopedics, optimizing long-term success and patient satisfaction. Smart knee implants are built with embedded sensors such as force, motion, temperature, and pressure detectors placed within the implant structure. These sensors provide real-time data during surgery and recovery, allowing earlier detection of complications and supporting tailored rehabilitation. The design aims to improve outcomes through better monitoring and personalized care. Full article

Nowadays, the circular economy is driving the construction industry towards greater sustainability for both environmental and financial purposes. One prominent area of research with significant contributions to circular economy is the reuse of steel from decommissioned structures in new construction projects. This approach is deemed far more efficient than ordinary steel recycling, due to the fact that it contributes towards reducing both the cost of the new project and the associated carbon emissions. Along these lines, the feasibility of utilizing steel wind turbine towers (WTTs) as part of a new structure is investigated herein, considering that wind turbines are decommissioned after a nominal life of approximately 25 years due to fatigue limitations. General principles of structural steel reuse are first presented in a systematic manner, followed by two case studies. Realistic data about the geometry and cross-sections of previous generation models of WTTs were obtained from the Greek Center for Renewable Energy Sources and Savings (CRES), including drawings and photographic material from their demonstrative wind farm in the area of Keratea. A specific wind turbine was selected that is about to exceed its life expectancy and will soon be decommissioned. Two alternative applications for the reuse of the tower were proposed and analyzed, with emphasis on the structural aspects. One deals with the use of parts of the tower as a small-span pedestrian bridge, while the second addresses the transformation of a tower section into a water storage tank. Several decision factors have contributed to the selection of these two reuse scenarios, including, amongst others, the geometric compatibility of the decommissioned wind turbine tower with the proposed applications, engineering intuition about the tower having adequate strength for its new role, the potential to minimize fatigue loads in the reused state, the minimization of cutting and joining processes as much as possible to restrain further CO₂ emissions, reduction in waste material, the societal contribution of the potential reuse applications, etc. The two examples are briefly presented, aiming to demonstrate the concept and feasibility at the preliminary design level, highlighting the potential of decommissioned WTTs to find proper use for their future life. Full article

The increase in the number of wildfires in recent years in different parts of the world has caused growing concern among the population, since the consequences of these fires go beyond the destruction of the ecosystem. With the growing relevance of the Internet of Things (IoT) industry, developing solutions for the early detection of fires is of critical importance. This paper proposes a low-cost network based on Long-Range (LoRa) technology to autonomously assess the level of fire risk and the presence of a fire in rural areas. The system consists of several LoRa nodes with sensors to measure environmental variables such as temperature, humidity, carbon monoxide, air quality, and wind speed. The data collected is sent to a central gateway, where it is stored, processed, and later sent to a website for graphical visualization of the results. In this paper, a survey of the requirements of the devices and sensors that compose the system was made. After this survey, a market study of the available sensors was carried out, ending with a comparison between the sensors to determine which ones met the objectives. Using the chosen sensors, a study was made of possible power solutions for this prototype, considering the expected conditions of use. The system was tested in a real environment, and the results demonstrate that it is possible to cover a circular area with a radius of 2 km using a single gateway. Our system is prepared to trigger fire hazard alarms when, for example, the signals for relative humidity, ambient temperature, and wind speed are below or equal to 30%, above or equal to 30 °C, and above or equal to 30 m/s, respectively (commonly known as the 30-30-30 rule). Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

Silos are strategic structures widespread in the industrial sectors for post-harvest preservation purposes. Current standards on the seismic design of silos are understandably based on approximate and simplified assumptions, leading intentionally to conservative design-oriented formulae. However, unjustified over-estimation might lead to unnecessary economic losses. As part of the authors’ analytical and experimental ongoing research on the complex seismic behavior of filled silo systems, in this short paper, an in-depth reading of the theoretical framework originally proposed during the 1970s and 1980s is provided to present a better understanding of the unexplained design-oriented formula of the seismic additional pressure in the European standard. A conceptual incongruence in the Eurocode EN1998-4:2006 is pointed out and discussed regarding the dynamic overpressure formula in the case of ground-supported flat-bottom circular silos subjected to seismic excitation. Specifically, a potential miscounting of the geometrical aspect in circular silos, with respect to rectangular ones, leads to an inconsistent amplification of the additional pressures in the range 1.65–2, depending on the filling aspect ratio of the silo. This inconsistency provides the reason for several unexplained results recently published in the scientific literature. A proposal for a physically based correction, retaining the current assumptions made by the EN1998-4, is finally given. Full article

Although Unmanned Aircraft Systems (UASs) offer valuable services, they also introduce certain risks—particularly to individuals on the ground—referred to as third-party risk (TPR). In general, ground-level TPR tends to rise alongside the density of people who might use these services, leading current regulations to heavily restrict UAS operations in populated regions. These operational constraints hinder the ability to gather safety insights through the conventional method of learning from real-world incidents. To address this, a promising alternative is to use dynamic simulations that model UAS collisions with humans, providing critical data to inform safer UAS design. In the automotive industry, the modelling and simulation of car crashes has been well developed. For small UAS, this dynamical modelling and simulation approach has focused on the effect of the varying weight and kinetic energy of the UAS, as well as the geometry and location of the impact on a human body. The objective of this research is to quantify the effects of UAS material and shape on-ground TPR through dynamical modelling and simulation. To accomplish this objective, five camera–drone types are selected that have similar weights, although they differ in terms of airframe structure and materials. For each of these camera–drones, a dynamical model is developed to simulate impact, with a biomechanical human body model validated for impact. The injury levels and probability of fatality (PoF) results, obtained through conducting simulations with these integrated dynamical models, are significantly different for the camera–drone types. For the uncontrolled vertical impact of a 1.2 kg UAS at 18 m/s on a model of a human head, differences in UAS designs even yield an order in magnitude difference in PoF values. Moreover, the highest PoF value is a factor of 2 lower than the parametric PoF models used in standing regulation. In the same scenario for UAS types with a weight of 0.4 kg, differences in UAS designs even considered yield an order when regarding the magnitude difference in PoF values. These findings confirm that the material and shape design of a UAS plays an important role in reducing ground TPR, and that these effects can be addressed by using dynamical modelling and simulation during UAS design. Full article

This paper presents an engineering design framework for integrating Variational Quantum Circuits (VQCs) into industrial control systems via real-time quantum emulation on embedded hardware. In this work, we present a novel framework for fully embedded real-time quantum machine learning (QML), in which a four-qubit, four-layer VQC is both emulated and trained in situ on an FPGA-based embedded platform (dSPACE MicroLabBox 1202). The system achieves deterministic microsecond-scale response at a closed-loop frequency of 100 kHz, enabling its application in latency-critical control tasks. We demonstrate the feasibility of online VQC training within this architecture by approximating nonlinear functions in real time, thereby validating the potential of embedded QML for advanced signal processing and control applications. This approach provides a scalable and practical path toward real-time Quantum Reinforcement Learning (QRL) and other quantum-enhanced embedded controllers. The results validate the feasibility of real-time quantum emulation and establish a structured engineering design methodology for implementing trainable quantum machine learning (QML) models on embedded platforms, thereby enabling the development of deployable quantum-enhanced controllers. Full article

Agricultural research and propagation systems often suffer due to a lack of access to affordable, adaptable, and well-structured technological solutions. Traditional plant growth devices typically rely on ad hoc construction, which limits their scalability, reuse, and adaptability. This study employs a user-centered conceptual design methodology based on product platform development and modular architecture to design a growth chamber for plant cuttings. The approach followed three main phases: (i) identification and classification of user needs, (ii) functional modeling of the base system and its variants, and (iii) architectural modularization through heuristic principles. Interviews with researchers yielded 55 functional requirements, of which 26 were defined as essential. Functional models were developed for both a base system and two variant systems incorporating alternative irrigation and sensing technologies. Heuristic analysis identified independent modules, such as irrigation, lighting, environmental monitoring, and control. Subsequently, block diagrams were used to translate functional logic into spatially coherent conceptual designs. The resulting architecture supports modular integration, reconfiguration, and scalability for diverse experimental needs. This work demonstrates that structured design methodologies, which are commonly used in industrial contexts, can be effectively applied in agricultural research settings to produce solutions that are versatile, low-cost, and have enduring value, offering a pathway for innovation, reproducibility, and technology transfer in resource-limited environments. Full article

Although 3D-printed concrete (3DPC) offers advantages such as faster construction, reduced labour costs, and minimized material waste, concerns remain about its long-term durability. This review examines these challenges by assessing how the unique layer-by-layer manufacturing process of 3DPC influences key material properties and overall durability. The formation of interfacial porosity and anisotropic microstructures can compromise structural integrity over time, increasing susceptibility to environmental degradation. Increased porosity at layer interfaces and the presence of shrinkage-induced cracking, including both plastic and autogenous shrinkage, contribute to reduced durability. Studies on freeze–thaw performance indicate that 3DPC can achieve durability comparable to cast concrete when proper mix designs and air-entraining agents are used. Chemical resistance, particularly under sulfuric acid exposure, remains a challenge, but improvements have been observed with the inclusion of supplementary cementitious materials such as silica fume. In addition, tests for chloride ingress and carbonation reveal that permeability and resistance are highly sensitive to printing parameters, material composition, and curing conditions. Carbonation resistance, in particular, appears to be lower in 3DPC than in traditional concrete. This review highlights the need for further research and emphasizes that optimizing mix designs and printing processes is critical to improving the long-term performance of 3D-printed concrete structures. Full article

The integration of intelligent voice-control systems represents a critical pathway for enhancing driver comfort and reducing cognitive distraction in modern vehicles. Currently, voice assistants capable of accessing real-time vehicular data (e.g., engine parameters) or controlling actuators (e.g., door locks) remain exclusive to premium brands. While aftermarket solutions like Amazon’s Echo Auto provide multimedia functionality, they lack access to critical vehicle systems. To address this gap, we develop a novel architecture leveraging the OBD-II port to enable voice-controlled telematics and actuation in mass-production vehicles. Our system interfaces with a Toyota Hilux (2020) and Mazda CX-3 SUV (2021), utilizing an MCP2515 CAN controller for engine control unit (ECU) communication, an Arduino Nano for data processing, and an ESP01 Wi-Fi module for cloud transmission. The Blynk IoT platform orchestrates data flow and provides user interfaces, while a Voiceflow-programmed Alexa skill enables natural language commands (e.g., “unlock doors”) via Alexa Auto. Experimental validation confirms the successful real-time monitoring of engine variables (coolant temperature, air–fuel ratio, ignition timing) and secure door-lock control. This work demonstrates that high-end vehicle capabilities—previously restricted to luxury segments—can be effectively implemented in series-production automobiles through standardized OBD-II protocols and IoT integration, establishing a scalable framework for next-generation in-vehicle assistants. Full article

In recent years, advancements in semiconductor technologies have significantly transformed Radio Frequency Power Amplifiers (RFPAs), enhancing their efficiency, size, and performance. Despite these advancements, the design of RFPAs remains intrinsically linked to the specific applications for which they are intended. What proves effective in one context, such as communication technologies, may not be equally suitable in others, such as scientific instruments. This discrepancy highlights the lack of a systematic approach to RFPA design that can be applied across different applications. This paper delves into the fundamental concepts of RFPA design, adopting a comprehensive perspective. It further introduces an alternative categorization of RFPAs, thereby providing a generalized design approach. Full article

This study presents a novel flake powder metallurgy approach for fabricating Al/CNT composites, combining the dual-matrix (DM) method with shift-speed ball milling (SSBM) to optimize mechanical performance. Samples prepared via DM-SSBM were systematically compared to those produced by conventional high-speed ball milling (HSBM), single-stage SSBM, and dual-matrix (DM) routes. Tensile testing revealed that the DM₁MR₅₀-SSBM composite achieved a superior balance of strength and ductility, with an ultimate tensile strength of ~267 MPa, elongation of ~9.9%, and the highest energy absorption capacity (~23.4 MJ/m³) among all tested samples. In contrast, the HSBM sample, while achieving the highest tensile strength (~328 MPa), exhibited limited elongation (~4.7%), resulting in lower overall toughness. The enhanced mechanical response of the DM-SSBM composites is attributed to improved CNT dispersion, refined cold-welding interfaces, and pure Al matrix softness, which together facilitate superior load transfer and hinder crack propagation under tensile stress. In the final consolidated state, aluminum forms a continuous matrix embedding the CNTs, justifying the use of the term “aluminum matrix” to describe the composite structure. These findings highlight the DM-SSBM approach as a promising method for developing lightweight, high-toughness aluminum composites suitable for energy-absorbing structural applications. Full article

This article presents a methodology for developing a three-dimensional terrain model based on numerical data in the form of a point cloud, with an emphasis on reducing mesh surface errors and using a surface smoothing factor. Initial surface generation was based on a point cloud with a square mesh, and an adopted algorithm for mesh conversion to the input form for the computer aided design (CAD) environment was presented. The use of a bilinear interpolation algorithm was proposed to reduce defects in the three-dimensional surface created in the reverse engineering process. The terrain mapping accuracy analyses were performed for three samples of different geometry using two available options in the Siemens NX program. All obtained surfaces were subjected to shape deviation analysis. For each of the analyzed surfaces, changing the smoothing factor from 0% to 15% did not cause significant changes in accuracy depending on the method adopted. For flat regions, in the Uniform Density (UD) method, the size of the area outside the tolerance was 6.16%, and in the Variable Density (VD) method, it was within the range of 5.01–6%. For steep regions, in the UD method, it was 6.25%, and in the VD method, it was within the range of 5.39–6.47%, while for concave–convex regions, in the UD method, it was 6.5% and in the VD method, it was within the range of 4.96–6.36%. For a smoothing factor value of 20%, a sudden increase in the inaccuracy of the shape of the obtained surface was observed. For flat regions, in the Uniform Density (UD) method, the size of the area outside the tolerance was 69.84%, and in the Variable Density (VD) method, it was 71.62%. For steep regions, in the UD method, it was 76.07%, and in the VD method, it was 80.94%, while for concave–convex regions, in the UD method, it was 56.08%, and in the VD method, it was 62.38%. The developed methodology provided high accuracy in the reproduction of numerical data that can be used for further analyses and manufacturing processes, such as 3D printing. Based on the obtained data, three fused deposition model (FDM) prints were made, presenting each of the analyzed types of terrain geometry. Only FDM printing was used, and other technologies were not verified. Full article

Stained glass has played important roles in heritage building construction, however, conventional fabrication techniques have become economically prohibitive due to both capital costs and energy inefficiency, as well as high-level artistic and craft skills. To overcome these challenges, this study provides a new design methodology for customized 3D-printed polycarbonate (PC)-based stained-glass window alternatives using a fully open-source toolchain and methodology based on digital fabrication and hybrid crafts. Based on design thinking and open design principles, this procedure involves fabricating an additional insert made of (i) a PC substrate and (ii) custom geometries directly 3D printed on the substrate with PC-based 3D printing feedstock (iii) to be painted after the 3D printing process. This alternative is intended for customizable stained-glass design patterns to be used instead of traditional stained glass or in addition to conventional windows, making stained glass accessible and customizable according to users’ needs. Three approaches are developed and demonstrated to generate customized painted stained-glass geometries according to the different users’ skills and needs using (i) online-retrieved 3D and 2D patterns; (ii) custom patterns, i.e., hand-drawn and digital-drawn images; and (iii) AI-generated patterns. The proposed methodology shows potential for distributed applications in the building and heritage sectors, demonstrating its practical feasibility. Its use makes stained-glass-based products accessible to a broader range of end-users, especially for repairing and replicating existing conventional stained glass and designing new customizable products. The developed custom patterns are 50 times less expensive than traditional stained glass and can potentially improve thermal insulation, paving the way to energy efficiency and economic savings. Full article

This review explores the intersection of Artificial Intelligence (AI) and Generative Design (GD) in engineering within the mechanical, industrial, civil, and architectural domains. Driven by advances in AI and computational resources, this intersection has grown rapidly, yielding over 14,000 publications since 2016. To map the research landscape, this review employed semantic search and Natural Language Processing, parsing 14,355 publications to ultimately select the 88 most relevant studies through clustering and topic modelling. These studies were categorised according to AI and GD techniques, application domains, benefits, and limitations, providing insights into research trends and practical implications. The results reveal a significant growth in the integration of advanced generative AI methods, notably Generative Adversarial Networks for direct design generation, alongside the continued use of genetic algorithms and surrogate models (e.g., Convolutional Neural Networks and Multilayer Perceptrons) to manage computational complexity. Structural and aerodynamic applications were the most common, with benefits including improvements in computational efficiency and design diversity. However, barriers remain, including data generation costs, model accuracy, and interpretability. Research opportunities include the development of generalisable foundation surrogate models, the integration of emerging generative methods such as diffusion models and large language models, and the explicit consideration of manufacturability constraints within generative processes. Full article

This study presents a methodology for optimizing the design of the fossa component in temporomandibular joint (TMJ) prostheses, particularly in cases requiring replacement due to severe pathology or trauma. Leveraging advancements in 3D printing, the research aims to align prosthetic function with natural jaw movements. A multibody simulation model was used to evaluate different designs based on key performance indicators: range of motion, condylar trajectory accuracy, and contact force magnitudes. Three designs were analyzed: a compact design fossa (CDF) with a spherical condyle, an enhanced design fossa (EDF) with a more anatomically realistic structure, and a simulation-driven design (MEDF) derived from condylar motion patterns. The results indicate that CDF could lead to dislocation at 13° of mouth opening. In contrast, EDF and MEDF safely enabled full opening (20°), closely replicated natural condyle trajectories (with deviations under 2.5 mm in all directions), and reduced contact forces, which can contribute to a longer prosthesis lifespan. MEDF showed the lowest peak contact force (−21% compared to EDF). The study successfully established a framework for evaluating and guiding patient-specific TMJ prosthetic designs, enhancing both functional rehabilitation and mechanical durability by minimizing wear through optimized contact dynamics. Full article