Additively manufactured conductive polymers enable the development of material specimens with an intrinsic sensing capability, where mechanical loading can be inferred directly from electrical response without the use of discrete sensors. Among these materials, carbon-filled conductive polylactic acid (PLA) filaments provide a low-cost and accessible platform for exploring piezoresistive self-sensing concepts at the material level. This work presents an experimental assessment of the piezoresistive self-sensing capability of 3D-printed conductive PLA specimens manufactured by material extrusion. Standard tensile specimens were produced and subjected to uniaxial loading using a universal testing machine, while the electrical resistance was monitored simultaneously during mechanical deformation. The experimental study focuses on verifying the existence, stability, and repeatability of the electromechanical response within the elastic deformation regime. The results reveal a clear and consistent correlation between applied mechanical loading and electrical resistance variation, with resistance changes closely following the imposed deformation cycles. This behavior confirms that the printed conductive PLA specimens exhibit a stable piezoresistive response suitable for strain-dependent signal acquisition at the material level. Rather than pursuing a comprehensive material characterization, this study provides a proof of concept demonstrating the feasibility of using 3D-printed conductive PLA specimens as self-sensing material elements. The findings contribute experimental evidence supporting the use of additively manufactured piezoresistive polymers as embedded sensing media in future mechanically loaded components and robotic structures.

Additive manufacturing (AM), also referred to as Rapid Prototyping (RP), encompasses advanced manufacturing technologies that fabricate components layer by layer and has gained significant attention across diverse engineering applications. Topology optimization (TO) has emerged as an effective design approach for reducing weight while maintaining structural integrity, particularly for components traditionally produced through casting or machining. In this study, topology optimization of key robotic arm components, namely the base cylinder, lower link, and upper link, is performed to achieve substantial mass reduction. The Functional Generative Design (FGD) module is employed for topology optimization, while ANSYS Workbench is used to validate the optimized designs through structural analysis. The optimization objective targets a minimum mass reduction of 40% relative to the original designs. The optimized results demonstrate weight reductions of 73% for the base cylinder, 50.66% for the lower link, and 46.89% for the upper link, while induced stresses remain below the ultimate tensile strength of ABS material. The optimized components are fabricated using Fused Deposition Modelling (FDM) and successfully assembled. The findings confirm that the proposed approach effectively achieves significant weight reduction without compromising structural performance.

Introduction. Robotic firefighting systems equipped with manipulator-actuated fire monitors (water/foam nozzles) enable remote suppression in hazardous environments while reducing personnel exposure. However, accurate jet aiming and stable operation are challenged by strong, rapidly varying disturbance loads caused by jet recoil, flow-rate changes, hose/line dynamics, and platform motion. These factors must be explicitly considered when justifying manipulator design parameters and selecting a control architecture that maintains pointing accuracy and operational safety.

Methods. A coupled dynamic model of the manipulator–fire–monitor assembly was formulated, incorporating joint friction, actuator limits, and a recoil load model expressed through nozzle operating variables (pressure/flow) and monitor orientation. Design parameter justification was performed by evaluating worst-case combinations of required slewing/positioning maneuvers and recoil disturbances to derive bounds for joint torques, speeds, transmission ratios, stiffness, and admissible mass-inertia properties of the end-effector. The control system was synthesized as a two-layer scheme: a feedforward compensation term based on the estimated recoil moment and desired motion profile, and a feedback loop for tracking and disturbance rejection (computed-torque or robust PID structure with anti-windup and saturation handling). Safety constraints were enforced through bounded acceleration/jerk commands and workspace limitations.

Results. The proposed framework yields parameter maps linking nozzle operating regimes and aiming dynamics to required actuator capabilities and structural margins. Simulation-based verification demonstrates stable tracking under abrupt flow changes and external disturbances, with reduced overshoot and faster settling compared to non-compensated control. The controller maintains bounded pointing error while avoiding actuator saturation across the considered operating envelope.

Conclusions. The presented approach provides a systematic justification of manipulator design parameters and control structure for manipulator-actuated fire monitors. It supports evidence-based sizing and tuning to improve aiming stability, robustness to recoil disturbances, and overall safety of robotic firefighting systems.

Automated machine systems play a critical role in plant biology by enabling precise and continuous monitoring of growth and physiological stress under varying environmental conditions. This study presents a systematic evaluation of previously developed machine architectures integrating mechanical positioning units, optical and environmental sensors, and automated data acquisition platforms for high-throughput plant phenotyping. Configurations for measuring morphological traits and physiological indicators, such as leaf area, chlorophyll concentration, and water status, are critically analyzed based on insights from published studies. The performance of these systems is assessed in terms of measurement accuracy, repeatability, adaptability to dynamic plant growth, and sensitivity to environmental fluctuations. Key challenges related to mechanical precision, sensor calibration, and long-term operational stability are discussed. While existing systems provide valuable understanding of plant responses, limitations remain in scalability, adaptability across species, and integration under variable environmental conditions. Based on this evaluation, original perspectives are proposed for next-generation automated monitoring platforms, emphasizing modular mechanical design, adaptive sensor fusion, and intelligent data-processing algorithms. Integration of real-time feedback and machine-learning-based anomaly detection is highlighted as a promising approach for early identification of physiological stress and optimization of growth conditions. This work highlights the significance of engineering-driven machine solutions in biological monitoring and provides a conceptual framework synthesizing insights from current technological developments, guiding interdisciplinary research at the interface of automation, mechatronic systems, and plant biology.

Human–robot collaboration (HRC) is increasingly being introduced into construction and demolition activities to improve efficiency and help reduce human exposure to hazardous tasks. Construction sites pose unique hazards regarding close human–robot interaction during demolition operations. This presentation discusses the development of a simulated virtual environment designed to examine potential hazards and subsequent human responses while working alongside a demolition robot.

The study explores various risk factors that may cause operators of demolition robots to situate themselves within the hazard zone of the robot, that is, within an area near the robot where they can be hit, pinned, or crushed by any part of the robot. Consequently, the study evaluates participants’ situational awareness and response to sudden and unexpected robot behaviors, including outrigger and arm swing motions, as well as structural hazards such as roof and floor collapses. While participants conduct different demolition tasks, the established virtual environment enables controlled manipulation of hazard types, environmental conditions, and spatial configurations while monitoring human perception, reaction, and decision-making under varying risk conditions. Participant responses are evaluated in terms of reaction time, spatial behavior, perceived risk, and task performance. Results from this study provide quantitative and qualitative insights into critical safety parameters for construction HRC. These findings will inform the development of preliminary safety thresholds for HRC demolition tasks, contributing to the foundation of future safety standards in construction robotics.

The increasing complexity of printed circuit board assembly (PCBA) processes has driven the adoption of automated solutions aimed at improving quality, repeatability, and production efficiency. In this context, this paper presents the experimental development and functional validation of the Underfill N950U project, which focuses on the study, selection, and validation of technologies for automated underfill adhesive application in N950U PCBAs used in the Cal-Comp assembly line. The current manual underfill application process presents limitations related to adhesive volume variability, high ergonomic effort, operational risks, and low process repeatability.

The proposed solution replaces the manual operation with an automated system based on a SCARA-type robotic manipulator with a 600 mm reach, integrated with a high-precision dispensing system, motion control, and vision-assisted positioning. The project includes the development of an offline prototype intended for the comparative evaluation of different underfill application technologies, enabling controlled testing to determine optimal process parameters such as adhesive volume, application speed, repeatability, dimensional stability, and cycle time.

Based on time and motion analysis, the proposed architecture was designed to support an estimated production of 2,400 PCBAs per day, with an approximate cycle time of 2 minutes and 30 seconds per carrier, while ensuring serial-number traceability and compliance with industrial safety requirements, particularly NR-12. The expected outcomes include reduced process variability, improved ergonomics, increased productivity, and the consolidation of incremental technological advancements, contributing to enhanced digital maturity and overall manufacturing efficiency.

This paper presents the experimental development and functional validation of SMT-CoCell, a collaborative automated cell designed for handling the Smart Card Connector ICA-697, a component widely used in Surface Mount Technology (SMT) lines for assembling printed circuit board assemblies (PCBAs) applied to point-of-sale (POS) systems. In the current manual process, operators remove sets of connectors from plastic trays and reposition them into feeding trays compatible with a Panasonic SMT mounting machine: an operation required due to the limited reach of the machine’s Cartesian robot. This manual activity is repetitive, time-consuming, and prone to variability.

The proposed solution introduces a hybrid collaborative work cell in which the operator supplies the system with trays from inventory and empty trays destined for the SMT machine, while a collaborative robotic manipulator automatically performs the pick-and-place operations of the ICA-697 connectors. A computer vision system is integrated to inspect the positioning of each connector in the destination tray, ensuring dimensional conformity and process quality prior to SMT assembly. The cell also includes automated tray flow control, guaranteeing proper orientation, synchronization, and compatibility with Panasonic SMT equipment.

Experimental results indicate that the proposed architecture can reduce the total cycle time of the tray exchange and manipulation process by up to 50% when compared to the manual operation, while improving positioning accuracy and repeatability. From a digital transformation perspective, the project contributes to increasing the digital maturity of the SMT line from Stage 1 (Computerization) to Stage 3 (Visibility), according to the ACATECH Industry 4.0 maturity model.

Hydrostatic guideways are widely used in precision and ultra-precision machine tools due to their high load-carrying capacity, stiffness, and superior vibration isolation capabilities. This study presents a comprehensive computational fluid dynamics (CFD) investigation of the influence of different recess shapes and restrictor types on the key performance indicators of hydrostatic guideways. A three-dimensional CFD model is developed using ANSYS Fluent to simulate lubricant flow through the restrictor, recess, and land regions under steady-state operating conditions. Multiple recess geometries are analysed while maintaining a constant recess-to-land area ratio, and both capillary and orifice restrictors are modelled with appropriate flow characteristics. The performance of the hydrostatic pad is evaluated in terms of recess pressure, load- carrying capacity, stiffness, and mass flow rate over a range of operating fluid film thicknesses. The results indicate that recess geometry has a significant impact on pressure uniformity and load distribution, while the restrictor type plays a dominant role in overall performance. Orifice restrictors consistently exhibit higher load-carrying capacity and reduced mass flow rate compared to capillary restrictors, indicating improved hydraulic efficiency. The findings provide practical design insights for optimizing hydrostatic guideways in high-precision manufacturing and advanced machine tool applications.

Furthermore, the analysis reveals that specific recess configurations minimize flow recirculation zones, thereby stabilizing the fluid film pressure profile. This stabilization is critical for reducing micro-vibrations during machining processes. The study highlights that while capillary restrictors offer linear flow characteristics, the non-linear behaviour of orifice restrictors provides superior stiffness compensation under varying loads. Consequently, selecting an optimal recess shape with an orifice restrictor enhances damping characteristics, leading to superior surface finish quality. These numerical predictions enable designers to balance hydraulic power consumption with mechanical stability, facilitating the development of energy-efficient machine tools capable of achieving nanometric positioning accuracy in challenging, dynamic environments.

The increasing complexity of surface-mount technology (SMT) processes for printed circuit board assemblies (PCBAs) has intensified the demand for advanced inspection and data analysis solutions capable of enhancing quality levels, operational efficiency, and industrial predictability. In this context, this study presents the experimental development and functional validation of an artificial intelligence (AI)-based solution applied to an automated optical inspection (AOI) production line, with a focus on predicting recurrent failures in the SMT assembly process, such as component misalignment and short circuits.

The proposed solution is based on the use of real production data collected from a Yamaha YSi-V AOI system, which undergo preprocessing, intelligent data treatment, and classification stages to ensure consistency and reliability for AI model training. Multi-Layer Perceptron (MLP) neural networks are developed and trained to identify operational patterns associated with the most critical assembly defects, enabling both the automatic classification of detected failures and the estimation of failure occurrence probabilities in future production batches.

The project also includes the implementation of an automated prototype for segregating approved (OK) and rejected (NG) products, consisting of a six-axis robotic manipulator, an adjustable gripper, and a PCBA storage system, thereby integrating the physical layer with the digital intelligence of the system. The results are presented through a graphical visualization interface, providing key performance indicators, trend analysis, and predictive insights to support engineering decision-making.

Experimental validation in an industrial environment demonstrates that the proposed approach enables the evolution of the AOI line to Stage 5 (Predictive Capacity) of the ACATECH Industrie 4.0 maturity model, consolidating a robust technological foundation for data-driven digital manufacturing.

Thermal deformation is a major source of positioning error in CNC machines due to nonuniform heat generation and complex temperature variations across different machine components. Accurate prediction of thermal error is challenging because temperature data collected from multiple sensors exhibit nonlinear, noisy, and long-term dependent behavior. In this work, a thermal error prediction framework is proposed using an extended Long Short-Term Memory (xLSTM) network, where critical model hyperparameters are optimized using an Adaptive Grey Wolf Optimizer (AGWO) before model training. The bio-inspired AGWO algorithm adaptively balances exploration and exploitation by dynamically adjusting its control parameters, enabling efficient global search of the hyperparameter space. Temperature data acquired from multiple sensors placed at different locations of the CNC machine are used as input features to capture spatial and temporal thermal effects. The optimized hyperparameters are then employed to train the xLSTM model using gradient-based learning. Experimental results demonstrate that the AGWO-optimized xLSTM model achieves significantly lower thermal prediction error compared to conventional manually tuned and gradient-only approaches. The proposed method improves prediction accuracy, convergence stability, and generalization capability, making it suitable for real-time thermal error compensation in high-precision CNC machining applications. After compensating for the thermal error, the diametral deviation reduces to 95 % and improves the thermal stability of the machine tool. The prediction model capability is proven to be improved using the proposed approach.