Abstract The adsorption of plasma proteins like albumin on dental implant surfaces is crucial for effective osseointegration and is strongly influenced by the surface texture. Most of the studies in the literature focused on albumin concentrations well below the physiological values in plasma, thus limiting their biomedical relevance. Here, we study the adsorption of albumin from phosphate-buffered saline at physiologically relevant concentrations on Ti alloy surfaces of different textures by combining contact angle measurements, advanced confocal and scanning electron microscopy and texture surface analysis. Results consistently demonstrated the impact of surface texture—assessed through areal mean roughness, skewness and surface-developed area—on albumin adsorption. One of the main results is that upon incubation, albumin at 4 wt% concentration formed a thick, compact surface layer of several micrometres on the roughened samples, which was retained after droplet removal. Conversely, albumin adsorption from a 0.4 wt% solution had little effect on the surface texture. These findings highlight the importance of considering albumin layer formation at physiological concentrations, as it may influence subsequent stages of osseointegration. Optimizing roughness and skewness by taking into account the presence of a thick albumin layer could enhance protein retention and improve implant performance.

This work introduces an initial finite element (FE) framework for modelling particle-substrate interaction during fluidized bed surface finishing of Laser Powder Bed Fusion (L-PBF) components. Due to the complexity of as-built surface morphology and the difficulty of experimentally observing high-speed particle impacts, the mechanisms governing material removal remain poorly understood. The proposed 3D explicit FE model simulates the impact of stainless-steel particles on representative AlSi10Mg asperities using Johnson-Cook plasticity model and damage formulations. Results show that erosion occurs mainly through localized brittle-like detachment rather than extensive plastic deformation. Sequential impacts and oblique trajectories significantly increase internal energy absorption, enhancing asperity fragmentation and the surface smoothing level. The framework provides a foundation for future optimization of Fluidized Bed Finishing (FBF) parameters for improved finishing of additively manufactured metal parts.

The development of multifunctional soft devices requires materials that combine compliance with integrated sensing, yet most existing solutions rely on rigid sensors or on conductive filaments where anisotropic filler networks and infill-based porosity limit reproducibility. Previous work on foamed PLA has shown density reduction via additive manufacturing, but without functional integration, while conventional conductive TPU remains confined to bulk or patterned infill structures with poor electromechanical consistency. Here we present the first demonstration of Foam Additive Manufacturing (FAM) applied to carbon-black-filled thermoplastic polyurethane (TPU) for the fabrication of lightweight, sensor-integrated soft actuators. By tuning solubilization, desorption, and extrusion conditions, we generate homogeneous microcellular foams with up to 42.2% density reduction while preserving mechanical integrity. The resulting conductive foams exhibit stable and repeatable piezoresistive responses under load. Comparative tests on phalanx- and finger-like specimens confirm enhanced compliance and real-time resistance feedback during grasping and release, outperforming both bulk and infill-matched references. This work establishes FAM as a new route to directly print soft actuators that integrate structure and sensing in a single material, overcoming the anisotropy and scalability limits of previous foamed PLA and conductive TPU approaches. Potential applications span soft robotics, prosthetics, and wearable systems.

A measurement framework for the evaluation and online quality estimation of the wire arc additive manufacturing process based on cold metal transfer (WAAM-CMT) is presented here. Although several monitoring approaches have been proposed for WAAM, the identification of reliable noninvasive indicators capable of capturing process stability in real time remains challenging due to the highly nonstationary nature of arc current signals. A noninvasive monitoring strategy based on electrical current acquisition was implemented, using a clamp-type sensor to acquire the electrical current signal during the welding process. The acquired current signals were analyzed in both the time and frequency domains. To address the inherent variability of the current waveform, a wavelet coherence (WC) approach was adopted, enabling localized time-frequency correlation analysis between the measured current and a reference waveform. From this representation, a mean global WC (MGWC) index was derived as a quantitative indicator of process stability. The proposed methodology showed effectiveness in distinguishing between stable and unstable deposition regimes, linking signal irregularities to the geometric quality of the deposited tracks. In addition, measurement uncertainty was explicitly evaluated by combining probability density functions derived from instrument specifications with Monte Carlo propagation, enabling the estimation of the confidence level associated with the monitoring indicator. The resulting framework integrates electrical current sensing, time-frequency signal analysis, and uncertainty quantification, providing a scalable and noninvasive solution for real-time monitoring and quality assessment in WAAM-CMT processes.

Wire Arc Additive Manufacturing (WAAM) is a promising technology for producing large, high-performance metallic components, though process stability and geometric control remain critical challenges. The present work investigates the influence of deposition trajectory on the geometry, surface quality, and microhardness of wire arc additive manufactured Inconel 625 walls, produced by a Cold Metal Transfer (CMT) process. A conventional linear double-pass strategy is directly compared with a single-pass triangular weave trajectory under equivalent heat input per unit length, in order to isolate the effect of the torch path from other process variables. Single-layer and multi-layer walls were fabricated and characterized in terms of geometry, dimensional stability, surface waviness, and Vickers microhardness. The results show that the weave trajectory leads to improved geometric consistency, reduced variability, and significantly lower surface waviness compared to the linear strategy, while maintaining comparable mean wall width and microhardness. These findings demonstrate that appropriate trajectory design can enhance geometric stability and near-net-shape capability in WAAM-CMT without altering thermal input or material properties.

Foam Additive Manufacturing (FAM) couples gas-assisted foaming with material extrusion to produce lightweight PLA components, yet the combined influence of solubilization and extrusion variables remains poorly quantified. We analyze an experimental PLA–CO2 FAM dataset and model strand density ρ as a function of six controllable parameters (Pa,ta,td,Te,Se,Dn). Six regressors were compared under a common held-out test partition and a standardized map-generation protocol: polynomial regression, PCA + polynomial regression, SVR-RBF, Random Forest, RF-distilled polynomial surrogate, and Bayesian-regularized MLP. On the common test set, the MLP and Random Forest achieved comparable best performance, with the MLP marginally attaining the lowest errors (MAE=0.1058gcm−3,RMSE=0.1421gcm−3,R2=0.7895), closely followed by Random Forest (MAE=0.1063gcm−3,RMSE=0.1435gcm−3,R2=0.7853). The RF-distilled polynomial surrogate provides a closed-form differentiable approximation useful for exploratory sensitivity inspection, although it does not improve predictive accuracy over the direct polynomial baseline. The resulting maps quantify interactions between Te and Se at fixed (Pa,ta,td), revealing empirical trends that are qualitatively consistent with expected foaming behavior. Independent external-validation experiments further support the use of these maps as a basis for future uncertainty-aware process planning in FAM.

This study presents a multi-sensor monitoring strategy for dissimilar friction stir lap welding (FSW), combining tri-axial MEMS-based acceleration measurements with motor power consumption and thermocouple data. The approach aims to detect and characterize process instabilities and defect formation, including porosity, tunnel voids, and excessive flash, without requiring destructive testing. Experimental campaigns were conducted on AA 2024-T3 and AA 7075-T6 aluminum alloys using six welding conditions, intentionally generating both sound and defective joints. The results reveal distinct power and acceleration trends across the plunging, dwell, and welding phases, which are directly linked to the nature of the defects. The analysis revealed that stable welds were associated with motor power near 1200 ± 8 W, RMS acceleration below 0.10 m/s2, and vibration frequencies around 150–220 Hz. In contrast, defective joints exhibited power deviations up to ± 250 W, RMS acceleration as high as 0.24 m/s2, and dominant frequencies shifting toward 300–400 Hz, often correlating with tool wear and porosity formation. These results demonstrate the effectiveness of the proposed method in enabling early detection of welding anomalies through sensor-based signal analysis, contributing to process optimization and enhanced weld quality. This approach has significant implications for industrial applications, offering a scalable and reliable framework for sustainable manufacturing.

Foam Additive Manufacturing (FAM) couples gas-assisted foaming with material extrusion to produce lightweight PLA components, yet the combined influence of solubilization and extrusion variables remains poorly quantified. We analyze 528 experiments on PLA foamed with CO₂ and model strand density (ρ) as a function of six controllable parameters (Pₐ, tₐ, t_d, Tₑ, Sₑ, Dₙ). We compare six regressors (polynomial, PCA + polynomial, SVR-RBF, Random Forest, RF-distilled polynomial surrogate, and MLP) using an identical 80/20 held-out test partition and a standardized map-generation protocol. The Bayesian-regularized MLP attains the highest nominal accuracy(MAE = 0.0925 g/cm³, RMSE = 0.1216 g/cm³, R² = 0.832),while the Random Forest provides the most reliable non-neural baseline(R² = 0.41, RMSE = 0.141 g/cm³). To reconcile accuracy and interpretability, we distill the Random Forest into a degree-2 polynomial surrogate that yields a closed-form, differentiable mapping with competitive error and enables analytic process maps and gradient-based optimization. The resulting maps quantify interactions among Tₑ and Sₑ at fixed (Pₐ, tₐ, t_d), revealing physically consistent trends (density decreases with increasing Sₑ; mild increase with Tₑ). This study delivers a hybrid framework—ensemble accuracy plus polynomial transparency—for predictive design and multivariable control in FAM, and establishes reproducible benchmarks and artifacts (code, splits, figures) to support deployment in digital twins and closed-loop process planning.

Wire-arc additive manufacturing (WAAM) provides numerous benefits, including high deposition rates, cost-effective equipment, efficient material usage, and environmental sustainability. These characteristics render WAAM a suitable technology for applications in fashion and jewellery production. Nevertheless, achieving complete protection from oxygen poses challenges, leading to surface characteristics that differ from those of the bulk material. The presence of thermal oxide and alpha case layers increases brittleness, limiting suitability for direct application or aesthetic treatments such as anodization. Although chemical treatments efficiently remove oxides from complex-shaped titanium components, they frequently rely on highly polluting acids. This study investigated the feasibility of using oxalic acid, a naturally occurring organic acid, for the chemical machining of Ti6Al4V samples produced via WAAM. The efficacies of organic solutions at 60 °C, 75 °C, and 90 °C were evaluated and compared with those of conventional treatments based on hydrofluoric and nitric acids. Oxalic acid treatment at 90 °C for 24 h completely removed the oxide and alpha case layers, whilst the conventional treatment achieved the same result in 30 min, resulting in a thickness reduction of approximately 4

Electrochemical machining represents a viable approach for enhancing the surface quality of additively manufactured Nitinol components, which typically exhibit excessive roughness. In this study, the electrochemical behavior of Nitinol was examined in chloride- and nitrate-based solutions to evaluate their effectiveness in material removal and surface refinement. Potentiodynamic polarization tests indicated that these solutions facilitate alloy dissolution, with diffusion-controlled kinetics observed at elevated potentials. Preliminary ECM experiments demonstrated that the incorporation of Na2EDTA into chloride-based solutions increased material removal, likely due to the formation of highly soluble nickel and titanium complexes. Surface analysis revealed that chloride-containing solutions promoted selective nickel dissolution, while the combined chloride-nitrate solution mitigated process selectivity and reduced surface oxidation. These findings underscore the potential of ECM as an effective post-processing technique for improving the surface characteristics of Nitinol components fabricated via additive manufacturing.

Laser-directed energy deposition (L-DED) is a highly versatile additive manufacturing technology that supports both the fabrication of near-net-shape components and the repair of critical parts, thereby extending service life, enhancing material utilization, and reducing overall production costs. These advantages are particularly significant in die and mold applications, which demand materials capable of withstanding severe thermal and mechanical loads. In this context, AISI H13 hot-work tool steel is among the most widely adopted materials due to its superior hardness, wear resistance, and thermal stability, making it an ideal candidate for L-DED technology. The present study aims to comprehensively investigate the influence of deposition strategy, overlap distance, and two sets of laser power, powder-feed rate, and scanning speed on the quality of L-DED H13 single-layer depositions. A systematic evaluation of surface waviness, microhardness, microstructure, and defects was carried out. The findings indicate that an overlap of 60% results in the least waviness, while the implementation of a unidirectional scanning approach enhances surface uniformity by approximately 25% compared to a bidirectional strategy. Microhardness values up to 720 HV were achieved, exceeding those of conventionally manufactured H13. The findings highlight process windows that enable defect-free depositions without substrate preheating, providing practical guidelines for optimizing L-DED of H13 tool steel components.

Wire Arc Additive Manufacturing (WAAM) is a Directed Energy Deposition (DED) technology widely recognized for its capability to produce large-scale components, repair damaged parts, and achieve high deposition rates. Among WAAM processes, the Cold Metal Transfer (CMT) technique has garnered significant attention due to its low heat input and stable short-circuiting behavior, resulting in reduced spatter and enhanced control. This study aims to develop robust monitoring strategies to evaluate process quality and detect potential anomalies during the WAAM-CMT process. Single tracks of ER70S-6 steel were deposited, and a comprehensive monitoring system was implemented, incorporating two types of MEMS accelerometers and a digital microphone to capture acceleration, vibration, and acoustic emissions during the printing process. The proposed approach demonstrated its effectiveness in identifying potential defects and anomalies, contributing to the early detection of process instabilities. The findings provide a reliable framework for real-time monitoring and quality assurance in WAAM applications, highlighting the potential of sensor-based methodologies to enhance manufacturing outcomes.

Wire Arc Additive Manufacturing (WAAM) is an emerging technology that enables the production of high-integrity, semifinished metallic components using both conventional and ad-hoc welding machines. However, such a technology presents some challenges, mainly related to obtaining optimal deposition parameters. This experimental study investigated the impact of wire feed speed (WFS) and travel speed (TS) on the geometric properties of WAAM-CMT deposited ER70S single-tracks. The bead width (W) and height (H) were analyzed as response variables. Experimental results revealed that increasing WFS significantly increased both W and H, while increasing TS reduced them. Statistical analysis confirmed the significance of both WFS and TS for W, while only WFS significantly influenced H. These findings highlight the critical role of process parameters in optimizing WAAM geometry, paving the way for enhanced quality in additively manufactured components.

The growing demand for environmentally friendly and lightweight solutions in structural applications highlights the importance of research for innovative materials and design methods to satisfy rigorous performance requirements. This research investigates the potential of the Foam Additive Manufacturing (FAM) process to create sustainable, high-impact-resistant materials. Foamed PLA specimens with tailored core densities were evaluated under low-velocity impact conditions, demonstrating up to 127 _2 as a physical blowing agent, and the mono-material design, which facilitates recycling at the end of the product lifecycle. Additionally, the lightweight nature of the foamed structures reduces material consumption and enhances energy efficiency during use, particularly in applications requiring weight minimization. Detailed failure mode analyses revealed that foamed specimens absorb energy more efficiently due to mechanisms such as core indentation and foam cell collapse, achieving specific energy absorption ( SEA_ρ ) values up to 0.06 [Jm3 kg−1] for low-density specimen. Furthermore, based on the correlation between impact energy and sandwich density, the failure mode map highlights the superior impact resistance and progressive failure mechanisms of foamed specimens. These findings demonstrate the potential of FAM to bridge the gap between sustainability and performance, paving the way for innovative applications in fields such as packaging, automotive, and aerospace.

This study investigates the recycling of polyethylene terephthalate (PET) water bottles by material extrusion (MEX), an additive manufacturing (AM) technique, with a focus on characterising energy consumption and mechanical properties throughout the recycling process. The process encompasses shredding of the bottles, filament production, and the printing of tensile specimens. A full factorial design of experiment (DoE) was used to investigate the impact of various process parameters on product quality from an energy-saving perspective. The results provide important insights into energy efficiency and mechanical performance, identifying the optimal production conditions that balance environmental sustainability and material functionality. The results show that by optimizing printing parameters, energy consumption can be reduced by up to 30%, while the tensile strength of the printed samples can be increased by 20%. This research contributes to a broader understanding of the potential for AM in PET recycling, providing a pathway towards more localized and sustainable manufacturing practices.

Friction stir welding (FSW) has received great response in both academia and industry. Extensive research has been carried out to investigate the feasibility of the FSW process for different alloys and configurations. However, the growing demand for sustainable and cost-effective manufacturing, coupled with the pursuit of high-quality products, has led to the need for continuous monitoring of the manufacturing process and machine health. Therefore, the development of robust monitoring methods to assess the condition of the welding during the process becomes crucial for sustaining high production efficiency and ensuring quality standards. In this context, this study aims to provide methods for properly monitoring each phase characterizing the FSW process. Power consumption, vibrations, and temperatures were measured and evaluated in dissimilar friction stir lap welding of aluminum alloys.

The widespread adoption of foam additive manufacturing (FAM) in industries ranging from biomedical to aerospace hinges on the precise control of foam morphology, a capability not fully realized with current technologies. This study explores the potential of the FAM process, employing polylactic acid (PLA) and carbon dioxide (CO2) as a blowing agent, to finely tune the microstructural characteristics of foamed materials through controlled manipulation of process parameters. By systematically varying the pressure of the blowing agent, the time of absorption and desorption, extrusion temperature, speed, and nozzle diameter, we provide a detailed analysis of their individual and collective impact on foam morphology at both macroscopic and microscopic levels. Our findings reveal how specific parameter adjustments can significantly influence the density, diameter, and bubble size distribution within the foamed strands. These insights not only bridge a critical knowledge gap in FAM process optimization but also empower designers and engineers across various sectors to engineer foams with tailored properties for enhanced performance in lightweighting, insulation, and shock absorption applications. This research serves as a foundational guide for advancing the practical utility and scientific understanding of FAM technologies in producing next-generation foamed materials.