Abstract Operational condition monitoring of manufacturing equipment for predictive maintenance is crucial to enable early detection of abnormal behavior and prevention of costly downtime. In industry settings, normal operation data are abundant, whereas abnormal or failure data are rarely available, limiting the development of reliable data-driven diagnostic models. Generative modeling provides a promising approach to address this imbalance by learning the latent distribution of normal operation and defining the boundary of potential anomalies. This study proposes a generative latent-space framework for sound-based condition monitoring of a stamping machine using an internal sound sensor (ISS). The framework incorporates MTConnect for machine context as conditional information to guide the generative process. Sound sensing offers a low-cost, nonintrusive, and high-resolution modality, while MTConnect context provides synchronized operational states. The latent representation of sound features is organized around part-level representative signals, forming structured regions that capture characteristic acoustic patterns of normal operation. This structure guides the generative process toward realistic variations of normal sound while limiting deviations toward implausible samples. The proposed framework integrates two complementary generative architectures, a conditional variational autoencoder (CVAE) and a conditional diffusion model (CDM), to construct a hybrid augmented normal dataset. Experimental results show that retraining the encoder with the synthesized data enhances separation of part-specific sound representations and improves discrimination between normal and abnormal operation states. These findings demonstrate that the proposed framework effectively leverages normal sound data to define anomaly boundaries and strengthen the robustness of sound-based predictive maintenance systems for stamping operations.
Abstract NASA's in-situ resource utilization (ISRU) initiative aims to eliminate Earth-dependency by developing sustainable, on-site manufacturing solutions for deep-space endurance and colonization missions to the Moon and Mars, and even beyond. Regolith–resin composites, which combine abundant extraterrestrial soil with UV-curable polymers, represent a promising class of materials for lightweight, energy-efficient, and on-site fabrication. This research investigates the feasibility and performance of regolith–resin composites with solid loadings up to 85 wt%, using Lunar Highlands Simulant (LHS-1) Regolith and Mars Global Simulant (MGS-1) Regolith as fillers. Two UV-assisted approaches, pressure compaction and blade coating, were employed to fabricate composite specimens in a layer-by-layer style. The fabricated samples were then evaluated for producing robust, thermally insulating, and radiation-shielding components suitable for extraterrestrial environments. Mechanical testing demonstrated compressive strengths up to 8.65 MPa and tensile strengths up to 1.63 MPa, with pressure compaction generally yielding higher strength due to improved compaction. Thermal conductivity measurements ranged from 0.05 to 0.107 W m−1 K−1, indicating strong heat insulation properties. Radiation-shielding simulations using NASA's OLTARIS tool showed that the composites achieved dose-equivalent performance. This research demonstrates the potential of regolith–resin composites as a practical solution for in-space manufacturing, combining high regolith utilization with favorable multifunctional performance for deep-space missions and long-term missions.
Abstract Precision grinding is a key technology for thinning silicon carbide substrates. However, the surface accuracy often deteriorates due to total thickness variation (TTV), which critically affects subsequent device fabrication and packaging. Existing research primarily focuses on correcting preset geometric alignment, such as the tilt between vacuum chucks and grinding wheels, while insufficiently considering the influence of force-induced structural deformation during grinding. In this article, a mechanics-based model is developed to predict TTV in silicon carbide substrate grinding based on an equivalent wheel-deflection model. The model quantitatively incorporates grinding process parameters and the material and geometric properties of the grinding wheel and substrate. By equivalently representing the force-induced compliance of the grinding system as wheel deflection, the model captures the resulting change in effective inclination angle and its influence on non-uniform material removal. Experimental validation shows that the model achieves high accuracy, with an average deviation of less than 4%. This work provides a mechanics-based basis for surface-form prediction and parameter-sensitive TTV compensation in precision grinding.
Abstract This article presents a new framework for modeling machine tool dynamics to enable their integration into machine tool digital twins. The framework is formulated as a dynamic Bayesian network, in which model parameters, sensor and process observations, and decision variables are represented explicitly and updated sequentially using Bayesian inference. The framework incorporates a library of candidate models and enables model selection, uncertainty quantification, and physical system state estimation through probabilistic inference. These properties enable scalable digital twin creation at both the machine and fleet levels. The proposed framework is designed to be broadly adoptable across a wide range of machine tool dynamics problems. In this article, we demonstrate its implementation through a case study in feed-drive dynamics, where the generated model interfaces with a 3D printer’s controller to adaptively produce optimal trajectories that compensate for structural vibrations during printing. Bidirectional digital–physical communication loop is established and experimentally validated, confirming the framework’s ability to predict and adapt to system variations. Results indicate improved vibration compensation and enhanced surface quality under varying dynamic conditions in this case study, while computational challenges for enabling general implementation are discussed.
Plasmonic color arises from the resonance interaction between light and the metallic surface of nanostructures. Unlike conventional dye-based technologies, plasmonic color offers several advantages, including sub-wavelength resolution, vibrant hues, and long-term stability without fading. These properties make plasmonic color promising for applications in multifunctional pattern coloration, anti-counterfeiting labels, and high-density data storage. However, current plasmonic color-printing techniques often rely on costly and complex equipment. In this study, we present a novel, reversible plasmonic color-printing technology that combines laser shock processing with heat treatment, providing a highly efficient method for color printing. The results demonstrate that vivid plasmonic colors can be dynamically and reversibly controlled by modulating the geometrical dimensions and crystallinity of the nanostructure. Moreover, through molecular dynamics simulations, the underlying physical mechanism behind the reversible reshaping of metallic nanoparticles is explored in detail. This innovative approach holds significant potential for applications in plasmonic sensors, energy harvesters, and nanolithography systems.
Abstract Sustainability assessment in biobased manufacturing is commonly performed as a retrospective, static analysis, limiting its usefulness for operational decision-making and stakeholder engagement. This study advances cyber-physical sustainability assessment as an emerging paradigm in which life cycle-based sustainability evaluation is embedded within digitally connected manufacturing systems. A cloud-enabled sustainability assessment tool is developed that integrates automated life cycle inventory importation, life cycle impact assessment using Brightway2-based module, Industry 4.0-informed scenario modeling, and interactive decision support through a web-based dashboard. The approach enables multi-pillar evaluation across environmental, economic, and social dimensions while supporting near-real-time updates, transparent computation, and stakeholder-accessible visualization. The proposed system is demonstrated for the industrial hemp straw production and decortication process, assessing fiber and hurd production under cradle-to-gate system boundaries. Environmental indicators include global warming potential, acidification, cumulative energy demand, land use, and water use; economic performance is evaluated through energy, labor, logistics, and total production costs; and social performance is represented using operational metrics such as labor hours, overtime, training, and safety incidents. By coupling open-source life cycle modeling with cloud-based data infrastructure and actionable visualization, this work demonstrates how sustainability assessment can transition from post hoc reporting to a cyber-physical decision-support capability for biobased manufacturing. The proposed approach improves transparency, responsiveness, and stakeholder engagement, offering a scalable foundation for data-driven sustainability management in emerging biobased value chains.
A hybrid welding process that combines high-frequency pulses (HFP) with double-wire median-pulsed gas metal arc welding (GMAW) was proposed to retain the welding efficiency of double-wire welding while simultaneously enhancing weld quality. The effects of HFP amplitude on arc characteristics and weld formation in double-wire median-pulsed GMAW were systematically investigated. The arc electrical load characteristics and arc profile features under different HFP amplitudes were analyzed using voltage-current (U-I) characteristic curves and high-speed images, respectively. The experimental results demonstrated that the arc load remained stable throughout the welding process, and the double arcs consistently exhibited an overall V-shaped morphology under different HFP amplitudes. However, as the HFP amplitude increased, the proportion of the arc core region gradually increased, and the average current density exhibited an increasing trend. Based on the double arc pressure equation, HFP enhanced the axial arc pressure. Furthermore, the results showed that HFP exerted a significant influence on weld formation and porosity. With increasing HFP amplitude, the intensified longitudinal vibration and convergent flow within the weld pool accelerated internal heat transfer, thereby increasing weld penetration while reducing weld width. In addition, the enhanced fluid flow facilitated pore escape from the weld pool, resulting in reduced porosity.
Abstract The expansive and highly coupled process space inherent in binder jetting additive manufacturing presents significant challenges to purely data-driven machine learning models, which often lack the physical interpretability and generalizability required for reliable process optimization and high-quality fabrication. This study proposes a hybrid modeling framework PINN-XGBoost for accurately predicting the sintered density of binder-jetted Inconel 625 by integrating physics-informed neural networks with residual learning based on XGBoost. A comprehensive experimental investigation was conducted to evaluate the effects of binder saturation, layer thickness, and sintering temperature on green part quality, densification behavior, and microstructural evolution. The results reveal strongly coupled relationships among process parameters, with sintering temperature being the dominant factor, while binder saturation and layer thickness affect densification through their influence on green part formation. By embedding physical constraints into the learning process and correcting residuals using XGBoost, the proposed framework demonstrates improved predictive accuracy and generalization over conventional data-driven models. This approach provides a reliable and interpretable tool to support process optimization and promote the practical application of binder jetting additive manufacturing for nickel-based superalloys.
Photopolymerization-based additive manufacturing (PAM) has emerged as a powerful technique for fabricating complex three-dimensional (3D) structures with high precision and resolution. However, current methods face challenges related to several manufacturing constraints. In particular, PAM often faces mass transport limitations that restrict resin replenishment between cured layers, leading to prolonged printing times and potential defects during large-area fabrication. Meanwhile, the separation forces generated during the formation of wide solid cross sections frequently induce delamination or incomplete printing, further constraining scalability. To address these limitations, this study presents a novel Nozzle-Assisted Continuous Additive Manufacturing (NCAM), which combines nozzle-driven material deposition with continuous photopolymerization to accelerate resin refilling, thereby enabling the fabrication of parts with wide cross sections without compromising the printing speed and surface quality. The underlying printing mechanism is investigated through computational modeling and experimental validation, and the process capabilities are demonstrated via the fabrication of diverse mesoscale 3D models featuring solid, hollow, and complex cross-sectional geometries. Systematic evaluation of printing speed, surface finish, and dimensional accuracy confirms that NCAM-printed parts exhibit superior mechanical integrity and reduced build times compared to conventional layer-by-layer techniques. Moreover, the NCAM platform enables single-step multimaterial fabrication, integrating distinct materials volumetrically and on the surface within a continuous process, thereby eliminating the need for additional hardware. Overall, these findings establish NCAM as a versatile and scalable Additive Manufacturing platform for the rapid manufacturing of high-quality mesoscale multimaterial components, with broad applicability in aerospace, biomedical, and mechanical engineering.
Abstract Zirconia (ZrO2) ceramics are widely used in 5G-enabled devices owing to their excellent performance. However, their high hardness and brittleness during ultrasonic polishing lead to complex behavior that is influenced by the setup and abrasive size. This study presents Fresnel-structured transducer (FST)-based ultrasonic polishing (FSTUP), which employs a Fresnel-structured transducer into ultrasonic polishing to enhance the surface quality and material removal rate (MRR) of ZrO2 ceramics. The improved polishing performance is attributed to the combined effects of acoustic streaming, abrasive impact, and cavitation collapse. Cavitation collapse leads to surface erosion, thereby decreasing surface quality, unlike the other two methods. Therefore, it is essential to control this phenomenon. We investigated the effects of transducer structure, polishing distance, and abrasive particle size on the surface roughness (Ra) and MRR of ZrO2 ceramics. White light interferometry and laser confocal microscopy were used to characterize surface morphologies, and comsol simulations were performed to verify the sound field focusing capability of the FST. Under the optimized conditions (polishing distance D = 1.5 mm, abrasive size A = 2.5 µm, and polishing time T = 30 min), we obtained an Ra of 5.22 nm and an MRR of 6.45 nm/min. This research is applied to the polishing processing of 5G device back covers.
Laser impact welding (LIW) is a high-speed, small-scale, solid-state metal joining process in which a thin flyer collides with a target, producing localized severe deformation and heating that could activate dynamic recrystallization (DRX). Although DRX has been studied in other high-strain-rate impact processes, its systematic investigation in LIW is limited. This work examines predicted DRX activity in LIW, factors leading to its occurrence, and the influence of the flyer and target surface roughness. DRX evaluation is important since microstructural refinements during impact ultimately affect the strength of the welded interface. An Eulerian numerical model is formulated to determine strain, strain rate, temperature, and related fields during LIW based on a Johnson-Cook flow stress model coupled to a Mie-Gr & uuml;neisen equation of state to account for strain, temperature, and shock-pressure effects. DRX is evaluated via a postprocessing analysis based on the Zener-Hollomon parameter and Johnson-Mehl-Avrami-Kolmogorov relations. Two surface morphology interface conditions are examined, including idealized smooth flyer/target surfaces and experimentally measured rough surfaces. Results show that the rough flyer/target interfaces produce greater local deformation and temperature rise, leading to increased DRX and more refined grains. The temporal sensitivity to local strain and temperature variations highlights a limitation of postprocess DRX predictions and demonstrates a need for fully coupled microstructure simulation. This study lays the foundation for predicting and understanding DRX in LIW and suggests that surface morphology plays a critical role in the associated DRX behavior.
Abstract Electromagnetic blank holding offers high energy efficiency, flexibility, and high precision in control. By discretizing the electromagnetic blank holder force, the system achieves improved energy efficiency and control performance. However, the influence of this discretization on forming quality remains unclear. To address this issue, a model is established to calculate the discretized blank holder force (DBHF) across different areas based on strain energy conversion, and the mechanism of DBHF discretization is analyzed. The DBHF design for the complex part was proposed through collaboration between an optimization algorithm and finite element analysis to improve forming quality. With the optimal discretization level and the corresponding magnitude, the DBHF can be applied to form the approached multi-channel electromagnetic loading. To validate effectiveness, a car door prototype with various complex features was selected for forming by simulation and experiments. Results showed that the design is efficient for finding the optimal DBHF, reducing the maximum thinning ratio by 3.9%, the maximum thickening ratio by 13.6%, and the maximum strain on the FLC by 43.9% compared with a constant blank holder force. The forming qualities of the part at different discretization levels show an initial increase followed by a decrease. This work contributes to identifying the optimal DBHF for electromagnetic blank holding, thereby improving the quality of manufactured parts.
Abstract Calendering serves as a multifunctional step in dry electrode processing that not only densifies the electrode but also induces polytetrafluoroethylene (PTFE) fibrillation and reorganizes the microstructure. These coupled effects are essential for achieving electrical connectivity and sufficient cohesion, yet they also introduce trade-offs, such as active material particle fracture, pore collapse, and excessive porosity loss, that can hinder ionic transport. This research systematically maps the calendering parameter space for LiNi0.6Mn0.2Co0.2O2 (NMC622) dry cathodes with a target thickness of ∼100 µm and porosity of ∼30% by varying roll gaps, roll temperature, roll speed, and the number of passes. A practical processing window for this formulation and electrode architecture is identified that achieves sufficient PTFE fibrillation and strong interfacial contact while minimizing particle fracture and preserving the porosity required for efficient ionic transport. In particular, gradual-gap calendering with moderate per-pass compression mitigates fracture and pore collapse while still reaching the target thickness with reasonable throughput, and slower roll speeds with modest roll temperatures further reduce mechanical damage. These results provide actionable guidance for scaling NMC622-based thick dry-processed cathodes.
In this study, the effects of feed and tool rake angle on surface pit formation and arithmetic mean roughness Sa of a triaminotrinitrobenzene (TATB)-based polymer-bonded explosive (PBX) simulant were systematically investigated through theoretical modeling, ultra-precision cutting experiments, and surface topography measurements. A feed-induced indentation fracture model for TATB particles was developed based on indentation fracture theory and contact mechanics to quantitatively predict the key characteristic parameters of surface pit formation. On this basis, a comprehensive predictive model for the arithmetic mean roughness Sa was established by integrating the surface pit component predicted by the above fracture model with the matrix residual profile component and the component associated with other influencing factors. Ultra-precision cutting experiments were performed using single-crystal diamond tools with different rake angles at feeds ranging from 1 mu m/r to 16 mu m/r, and surface topographies were measured using white light interferometry. The results indicated that surface pit depth increased monotonically with feed and stabilized at higher feed values, in agreement with theoretical predictions. The tool rake angle primarily influenced Sa by controlling plastic side flow in the matrix, with the -15-deg rake angle tool yielding optimal cutting performance. The predicted Sa values showed good agreement with experimental measurements, with an average relative error of approximately 5.42%, confirming the validity and reliability of the proposed models and providing a theoretical basis for process parameter optimization in the ultra-precision cutting of TATB-based PBX materials.
Abstract Flexible pressure sensors are essential components in modern biosensing platforms, enabling real-time monitoring of mechanical stimuli in wearable health systems, human–machine interfaces, and soft robotic skins. Currently, most flexible pressure sensors enhance performance by optimizing material composition and fabrication processes, while systematic and comparative studies on the influence of internal structure remain relatively limited. Existing sensor architectures are predominantly planar or bulk in form, neglecting the impact of three-dimensional geometries on strain distribution, electrical response, and mechanical reliability. To address this gap, this article draws design inspiration from natural marine sponges, reporting a flexible pressure sensor featuring a bio-inspired scaffold structure. The sensor employs a porous soft lattice resembling sponge skeletons, endowing it with high compressibility and elasticity. Through systematic design and finite element simulation of various lattice shapes, the influence of unit-cell lattice structures on sensor sensitivity is verified. Simulation results indicate that compared to P, G, and IWP-type triply periodic minimal surfaces (TPMS) structures, the D-type TPMS structure exhibits optimal sensitivity, reaching 11.3468%. These simulation results confirm the effectiveness of this design methodology. The sensor is fabricated through a sacrificial molding process. A water-soluble polyvinyl alcohol (PVA) mold of the optimal scaffold design is 3D-printed, and then a conductive silicone elastomer is cast into the mold. After thermal curing, the PVA mold is dissolved in water to yield the standalone conductive silicone scaffold. Electrical testing indicates that sensors with different lattice geometries exhibit varying sensitivities. Compared to TPMS structures with P, G, and IWP-type surfaces, the TPMS structure with a D-type surface demonstrates optimal sensitivity, reaching up to 9.28% in experiments. This further validates the accuracy of the simulation and the effectiveness of the structural design. Finally, we fabricate the sensors into wearable devices and demonstrate their application as pressure sensors. This work demonstrates that the design of a sensor's internal spatial structure is a critical factor determining sensor performance, alongside material selection and fabrication techniques. It offers a promising strategy for optimizing the performance of flexible sensors in wearable and biomedical applications.
Abstract Titanium alloy microchannel heat sinks provide an ideal solution for thermal management of high heat-flux devices. Nevertheless, the fabrication of titanium alloy microchannels in common micromilling method by conventional micromilling tools with continuous cutting edges were limited by the large cutting forces, poor surface qualities, and dimensional accuracy. To address the above issues, this work proposed a micro discrete staggered edge milling tool (DSEMT) with a diameter of 0.5 mm for the fabrication of Ti6Al4V microchannels. The continuous right-handed cutting edges of conventional helical micromilling tool (CHMT) were separated into multiple discrete right-handed and left-handed cutting edges for the DSEMT. The left-handed cutting edges of the DSEMT were designed to be of a spoon-shaped cross section with a positive rake angle. This design enhanced their sharpness of the tool and shifted the cutting mode from the traditional plowing process of conventional arc-shaped cutting edges to the shearing process of spoon-shaped ones. Benefiting from the reverse cutting effect of left-handed cutting edges and improved flow of cutting fluid and chip evacuation by the multiple interconnected flow areas, the DSEMT reduced cutting forces by up to 54% and decreased the bottom surface roughness by up to 44% and sidewall surface roughness by up to 50% compared to the CHMT. The burrs in Ti6Al4V microchannels and tool wear were mitigated considerably, and the dimensional accuracy of the microchannels was also improved by the DSEMT.
Biomass materials are agricultural byproducts generated following seasonal harvesting. Their unique fibrous structure imparts low thermal conductivity, high porosity, and resilience, making biomass a sustainable and low-cost alternative to conventional synthetic insulation materials. Additive manufacturing (AM) has shown promise in fabricating biomass-based functional structures due to its flexibility in accommodating irregular feedstock and its ability to preserve the hierarchical porous microstructures during the layer-by-layer deposition. While AM is effective in prototyping, the scalability of AM techniques such as the extrusion-based process has long been a bottleneck for industry-scale productions due to low deposition rate and interlayer delamination. Mitigating the scalability challenge requires a new manufacturing process that enables continuous, high-throughput deposition while maintaining material uniformity and structural integrity. In this work, we develop a roll-to-roll (R2R) high-fiber manufacturing platform to fabricate insulation panels directly from wheat straw fiber slurries. A novel deposition mechanism that leverages a multi-level manifold and slot-die nozzles is implemented to achieve high-throughput, planar slurry deposition. The uniformity of fabricated panels is improved through both material formulation and process design. Rheological studies are conducted to characterize the viscoelastic behavior of the slurry, while computational fluid dynamics simulations are used to optimize the slot-die geometry for uniform flow distribution. The R2R configuration enables continuous manufacturing and increases throughput to 78 cm(3)/s. The fabricated insulation panels demonstrate comparable performance to existing synthetic products, along with improved mechanical strength. This new manufacturing process demonstrates a sustainable pathway to repurpose agricultural waste into value-added, environmentally friendly building materials.
Melt electrowriting (MEW) enables the fabrication of microscale fibrous scaffolds through controlled melt extrusion under an electric field. The quality of the printed structures is sensitive to several parameters, including voltage, printing pressure, melt temperature, nozzle-to-substrate gap, and collector speed. Within this parameter set, collector speed has a dominant effect on fiber path control, fiber diameter consistency, deposition accuracy, and the final scaffold architecture. In this study, the use of dynamic collector speed (DCS), whereby the collector speed is varied during printing, is introduced as a means to enhance structural precision. Multiple DCS patterns, including linear, stepwise, and alternating-speed modes, were implemented and compared to constant speed controls to assess their impact on scaffold fidelity. This study focuses on spindle-shaped scaffolds, selected for their anisotropic geometry, which makes them a suitable model for structures requiring directional mechanical properties such as muscle tissue. Results show that employing DCS significantly improves the symmetry of spindle scaffolds, particularly in the vertical (side view) direction, where gravitational effects at low speeds often cause deviation. In addition to improving geometric fidelity, DCS also amplified the spindles' 4D printing performance, increasing their electroresponsive shape change. Overall, our findings demonstrate that DCS is a versatile and easily implementable parameter for improving MEW scaffold fidelity and enabling more complex, high-precision architectures.
Dynamic chemical vapor deposition (CVD) with rapid thermal processing (RTP) offers a powerful means to independently control catalyst preparation and growth stages in carbon nanotube (CNT) manufacturing. However, catalyst deactivation caused by diffusion into oxide supports continues to limit yield and process robustness. In this study, we establish a quantitative framework for characterizing catalyst-support diffusion kinetics using spectroscopic ellipsometry coupled with cross-sectional scanning transmission electron microscopy (STEM), X-ray diffraction (XRD), and scanning electron microscopy (SEM). A multilayer optical model was developed to extract the thickness and composition of the evolving iron-alumina interface during thermal pretreatment at 700 degrees C and 900 degrees C under reducing conditions. The fitted parameters reveal measurable differences in both the rate of subsurface diffusion and the loss of surface catalyst, enabling nondestructive quantification of interface evolution. These ellipsometry-based results correlate with independent electron microscopy evidence and provide process-relevant metrics for assessing catalyst stability. By linking optical signatures to interfacial diffusion behavior, this approach introduces a generalizable metrology method for process monitoring, model validation, and design of stable, repeatable dynamic CVD recipes for nanocarbon manufacturing.
Laser powder bed fusion (LPBF) enables the fabrication of complex metallic components with high precision and flexibility. However, LPBF-manufactured materials often exhibit substantial scatter in fatigue behavior due to process-induced defects and microstructural variability. This scatter, combined with limited testing budgets and frequent runouts, leads to high uncertainty in stress-life (S-N) curve estimation, particularly in estimating the endurance limit. When analyzing multiple manufacturing conditions in LPBF, fitting each condition independently can be unreliable with sparse data, while pooling all data into a single curve can obscure process-specific effects. To address this challenge, this work proposes an integrated framework that combines a Bayesian hierarchical censored S-N model with Fisher information matrix-based D-optimal experimental design. The hierarchical model shares information across manufactured samples while preserving sample-specific S-N curves and endurance limits. The D-optimal design selects stress levels for testing that emphasize high-information regions. Together, these components improve uncertainty quantification and support more efficient fatigue testing.
