Michael Cullinan

Abstract Copper's high electrical and thermal conductivity makes it appealing for use in various industrial products. However, challenges related to its weight and performance in extreme environments limit copper's usage in aerospace applications. Previous research has demonstrated that incorporating multilayer graphene (MLG) on a copper substrate via chemical vapor deposition (CVD) improves the performance of this conductor in high-temperature applications without incurring a weight penalty. Incorporating a high percentage of large-area graphene, needed for better performance, is difficult while fabricating wires. This work shows a method for making high-quality graphene–copper composite wires from 25 µm and 50 µm copper foils, consolidated into a wire via repeated annealing and roller drawing reductions. A copper foil wire without graphene is compared to the composite to highlight graphene's benefits. This research correlates the composite's resulting material properties to the microstructure and creation process. The final results suggest that graphene content aids in consolidation, removing one of the primary defects in manufacturing wires from foils. Reducing porosity through improved consolidation prevents early fracture under tensile loading. In addition, the specific conductivity at room temperature for bilayer graphene (BLG), few-layer graphene, and MLG samples was comparable to that of bare copper wire. Graphene content also improves the resulting high-temperature electrical properties by protecting the wire from further oxidation. Based on the data presented in this article, recommendations are provided for further reducing void defects and enhancing the quality and performance of copper–graphene composite wires.

Additive manufacturing of functional devices, especially in microelectronics, increasingly requires patterning strategies that integrate dielectric and conductive features with high spatial selectivity while maintaining process simplicity. However, existing multimaterial approaches often rely on complex process sequences, repeated resin exchange, or post-print metallization, which limit achievable resolution and reduce overall. Here, we report a silver-acrylate hybrid resin compatible with stereolithography and digital light processing that enables exposure-programmable conductive patterning within polymer structures without resin exchange. Electrical functionality is encoded directly into local exposure: low-dose irradiation produces polymerized dielectric matrices, whereas higher-dose ultraviolet exposure or thermally assisted activation induces localized silver reduction to generate conductive regions. Because functionality is defined by irradiation dose, this material system is inherently compatible with mask-defined and grayscale exposure strategies, enabling spatial encoding of electrical behavior within a single build. Resin formulation and exposure conditions were systematically tuned to control pattern fidelity, surface localization, and electrical contrast. Using post-print chrome mask exposure, conductive lines, gaps, and complex planar patterns were generated with feature sizes on the order of tens of micrometers. The approach was further extended to three-dimensional DLP-printed architectures, where conductive pathways were selectively activated on nonplanar and out-of-plane surfaces. Optical and electron microscopy, surface profilometry, and four-point-probe measurements confirm surface-confined conductive traces and strong electrical contrast relative to surrounding dielectric regions. It was found that conductivity correlates with silver domain formation driven by polymerization-induced phase separation, providing a physicochemical basis for exposure-programmable metallization. Overall, this hybrid resin framework establishes a material-level pathway for integrating lithography-inspired dose control with vat photopolymerization, offering a scalable route toward multifunctional additive manufacturing and semiconductor-adjacent prototyping.

Quantitative Evaluation of Measurement Uncertainty: Theory and Practice provides comprehensive coverage of dynamic, direct, indirect, and combined measurements, giving a complete perspective on measurement uncertainty. It features pioneering approaches to evaluating uncertainty in dynamic measurements. Responding to the critical need for standardized approaches to uncertainty evaluation, this book provides a clear pathway to ensuring that measurement results include properly calculated uncertainty intervals with genuine coverage probabilities. With detailed practical examples spanning physics, chemistry, electrical engineering, and mechanical engineering, readers will gain a thorough understanding of both statistical and non-statistical methodologies for uncertainty analysis. Bridging theoretical principles with practical applications, this book will provide engineers, researchers, and graduate students with the essential tools to assess and communicate measurement uncertainty with confidence.

Abstract Fabrication of mold inserts using additive manufacturing (AM) or 3D-printing is generalized as rapid-tooling (RT) and offers great design flexibility and potential for rapid prototyping using injection and micro-injection molding. In miniature scales, established AM methods such as material jetting and stereolithography usually suffer from surface quality defects, lack of sufficient precision and accuracy for true-microscale molds and components. Current work demonstrates a process chain where material jetting and ultraviolet light-emitting diode (UV-LED) photolithography are combined to create precise and accurate microstructures on 3D-printed and planarized RTs for microfluidic applications. The planarization step involved spin-coating with 3D-printed resins to obtain a flat surface, followed by a custom-made UV-LED mask lithography step for inducing microchannels on the planarized inserts, using noncontact and contact modes. The process repeatability was characterized by varying revolutions per minute (RPM) and laser-scanning confocal microscopy of key dimensional features. The results showed that higher RPM values are favorable for obtaining better surface quality and repeatability. Obtained microfeatures were compared with directly-printed microfeatures using a material jetting printer and significant improvements were reported in the repeatability of surface roughness metrics, with up to 50% lower coefficients of variation. This novel process offers a viable method for creating highly precise and accurate prototyping molds and is expected to be impactful for the accelerated iterations of microfluidic devices.

Abstract The microscale selective laser sintering (μ-SLS) system is a microscale powder bed fusion (PBF) technology capable of producing fine-resolution, high aspect ratio copper interconnect structures for use in semiconductor packaging and MEMS fabrication. Despite these capabilities, the feature resolution of the μ-SLS system is currently limited by unwanted heat transfer in the nanoparticle bed during laser sintering. A full part-scale thermal model has been developed to predict the thermal evolution in the sintering particle bed in response to a given laser exposure pattern and sintering duration. A thin copper particle layer is modeled atop a thick glass substrate to simulate the sintering domain, and previously developed nanoparticle property relationships allow the model to capture material property changes that accompany nanoparticle sintering. The model is used to predict both temperature change and part densification in response to a variable laser exposure pattern. These temperature and part predictions provide the information needed to preoptimize laser exposure patterns and reduce unwanted heat spread and sintered part error.

Additive manufacturing using light is commonly constrained by serial voxel-by-voxel or layer-by-layer processing, which fundamentally limits fabrication speed and scalability. Here, we introduce a single-exposure holographic three-dimensional (3D) printing approach that synthesizes an entire volumetric dose distribution optically in one step. The method combines inverse-designed microstructured phase masks with photopolymer resins engineered for controlled optical absorption. By precisely tailoring the phase-mask topography, we generate arbitrary 3D light-intensity distributions within the resin, including intentionally encoded dark regions that define hollow internal features. Simultaneously, the resin formulation is designed to balance optical penetration with sufficient local energy deposition to achieve high-fidelity polymerization throughout the volume. Using this approach, millimeter-scale architectures comprising more than $10^{6}$ addressable voxels are fabricated in a single 7.5~s exposure, corresponding to a volumetric throughput of $\sim$1~mm$^{3}$/s ($>10^{5}$~voxels/s). The demonstrated performance is presently limited by resin kinetics and illumination geometry rather than by the phase-mask framework itself. Because the volumetric information capacity scales with the space--bandwidth product of the phase mask, this approach provides a clear pathway toward substantially higher throughput, enabling scalable fabrication of micro-optical components, biomedical scaffolds, and other precision-engineered mesoscale systems.

Additive manufacturing using light is commonly constrained by serial voxel-by-voxel or layer-by-layer processing, which fundamentally limits fabrication speed and scalability. Here, we introduce a single-exposure holographic three-dimensional (3D) printing approach that synthesizes an entire volumetric dose distribution optically in one step. The method combines inverse-designed microstructured phase masks with photopolymer resins engineered for controlled optical absorption. By precisely tailoring the phase-mask topography, we generate arbitrary 3D light-intensity distributions within the resin, including intentionally encoded dark regions that define hollow internal features. Simultaneously, the resin formulation is designed to balance optical penetration with sufficient local energy deposition to achieve high-fidelity polymerization throughout the volume. Using this approach, millimeter-scale architectures comprising more than $10^{6}$ addressable voxels are fabricated in a single 7.5~s exposure, corresponding to a volumetric throughput of $\sim$1~mm$^{3}$/s ($>10^{5}$~voxels/s). The demonstrated performance is presently limited by resin kinetics and illumination geometry rather than by the phase-mask framework itself. Because the volumetric information capacity scales with the space--bandwidth product of the phase mask, this approach provides a clear pathway toward substantially higher throughput, enabling scalable fabrication of micro-optical components, biomedical scaffolds, and other precision-engineered mesoscale systems.

Optimising injection dynamics in micro-injection moulding (μIM) enhances efficiency, reduces defects, and improves repeatability. The current study examines the injection dynamics of μIM using both rapid-tooling, fabricated via material jetting, and conventional aluminium metal tooling. A 20 mg micro-moulding cavity was used to assess injection behaviour, injection pressure profiles, and process variation through in-line process monitoring and computational modelling. Results reveal significant differences between rapid and metal tooling in terms of drag, pressure build-up during injection and mechanical properties of the final products. The low thermal conductivity of rapid-tooling has led to prolonged low melt viscosity retention, resulting in significantly reduced peak injection pressures and dampened pressure overshoots, improving process repeatability. Metal tooling in contrast showed increased pressure fluctuations, making the injection dynamics more complex and affecting process repeatability. Computational modelling captured the major trends and exhibited deviations in pressure profiles, particularly for rapid-tooling, where accurate heat transfer coefficient estimation remains a challenge. Mechanical property correlations with injection dynamics further highlight the data-rich nature of μIM, with shear stress effects at higher injection rates influencing part performance. This study provides new insights into μIM process dynamics, emphasising the role of thermal properties and the challenges in modelling heat transfer effects in rapid-tooling. The findings support the optimisation of μIM for improved process control, predictive modelling, and data-driven quality monitoring for both industrial and rapid prototyping settings.

Direct laser melting systems characterize modern additive manufacturing of metal components. In this process, monitoring the melting temperature of metal powders is essential to ensure high-quality final parts, which requires complete melting of the metals. Failure to reach the melting point results in porosity in the part, while excessive temperatures can lead to the formation of keyholes. The approach proposed in this study enables the measurement of the melting temperature of each track with using IR camera and the monitoring of the laser melting process quality based on three defined quality levels: A (good), B (satisfactory), and C (unsatisfactory), which are established through a quantitative assessment of the expanded measurement uncertainty. Experimental studies have shown that at the laser powers recommended by the EOSINT M280 system manufacturer—285 W for IN718 alloy powder and 195 W for SS 316L steel powder—the temperature ranges corresponding to good quality (A) are 1312°C to 1598°C for IN718 alloy powder and 1415°C to 1628°C for SS 316L powder. The relative expanded uncertainty of the temperature measurements was found to be 9.8% for IN718 alloy powder and 7% for SS 316L powder.

Growing demand for performance-optimized copper components has increased interest in additive manufacturing (AM) to realize complex geometries and internal architectures. Among AM processes, laser powder bed fusion (LPBF) enables fabrication of pure copper. However, with near-infrared (NIR) lasers it suffers from low absorptivity and high thermal conductivity that promote lack-of-fusion (LOF) porosity. In this study, pure-copper coupons were fabricated on an EOS M280 using a design of experiments that varied scan speed, laser power, and hatch spacing at a fixed layer thickness. Post-processing comprised heat treatment (HT, 950 °C, 120 min) and hot isostatic pressing (HIP, 950 °C, 100 MPa, 120 min). Process–structure–property relationships were established through density and electrical conductivity measurements, tensile testing, X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS), and electron backscatter diffraction (EBSD). Scan speed emerged as the dominant parameter governing porosity. HIP increased density only when the as-printed density was already high. XRD indicated single-phase Cu under all conditions, while EBSD showed that post-processing drives recrystallization, grain coarsening, and abundant twins. EDS maps revealed oxygen that appeared diffuse in as-printed material becoming concentrated at residual pore walls after HIP. These microstructural changes reduce electron scattering and enhance ductility. Conductivity increased monotonically from LPBF to LPBF+HT to LPBF+HIP, while ultimate tensile strength (UTS) decreased as expected from Hall–Petch softening. The results provide practical guidance for achieving higher-quality copper on widely available NIR-LPBF systems using standard powders and targeted post-processing.

Abstract As space applications and extreme environments demand higher-performance interconnects and advanced microelectronic devices, traditional copper conductors fall short due to their susceptibility to oxidation and electrical instability at high temperatures. Copper-graphene composite (CGC) conductors with high graphene loading are a promising solution, offering enhanced electrical properties and oxidation resistance due to the protective qualities of graphene. However, a significant challenge is integrating defect-free large-area graphene into thin copper films (<1 µ m). Conventional low-pressure chemical vapor deposition cannot directly grow graphene on these substrates; dry transfer introduces defects over large areas, and wet transfer can oxidize the copper film. This work addresses these limitations by optimizing graphene transfer and annealing to achieve high graphene loading with minimal defects over a large area (∼3 cm × 2 cm). A thermally stable CGC conductor was developed with enhanced current-carrying capacity and oxidation resistance. The CGC was fabricated using six alternating layers of bilayer to few-layer graphene and copper, each 300 nm thick. A reference copper sample with identical layering and processing, but without graphene, was also prepared. Although the CGC exhibited lower electrical conductivity than the copper reference at room temperature, the difference narrows at elevated temperatures due to its lower temperature coefficient of resistance (TCR). It demonstrates a reduction of up to 7.7% in TCR, enhanced oxidation resistance with a failure temperature increase from 162 °C to 261 °C (a 61% improvement), and improved current-carrying performance, with a 3.3% increase in failure current and a 13.7% increase in power at failure. These findings highlight the potential of CGC materials for next-generation, high-performance electrical interconnects in extreme environments.

Abstract Directed Energy Deposition (DED) is an additive manufacturing technique that is increasingly useful in the repair of metallic parts. Several industries have adopted this technology for repairs such as the aerospace industry, automotive industry, and tooling industry. It is therefore important to manufacture parts of high precision and accuracy to match the whole assembly. Some ways of ensuring manufacturing accuracy in DED include in-situ monitoring methods, ex-situ product tests, and the use of advanced modeling tools. In-situ monitoring methods and ex-situ testing are cost intensive and, in most cases, iterative. Advanced modeling tools are needed to effectively predict the dimensional accuracy of parts before they are printed. This will improve the adaptability of Directed Energy Deposition to more manufacturing processes. In this research, a Multiphysics model was built to analyze the influence of scanning speeds, and deposition rate on the melt pool shape and morphology. It was found that higher scanning speeds resulted in narrower melt pools, while higher deposition rates resulted in an increase in melt pool height. The results gotten from the model agrees with existing studies which have investigated these process parameters using in-situ monitoring and ex-situ testing methods.

The paper proposes a methodology for assessing the quality of the direct laser melting process of 316L steel powder, which was tested when creating products in a construction furnace of the EOSINT M280 system at different laser powers. The methodology for evaluating the quality of the laser melting process is based on measuring the melting temperature of 316L steel powder using an infrared camera, assessing the expanded uncertainty of temperature measurements, and calculating the probabilities of the temperature falling within the established confidence limits based on type I and type II control errors (risks). The experimental investigations revealed that the melting temperature of 316L steel powder was achieved at a laser power of 195 W, with an average value of 1446 °C. It was also found that the maximum expanded measurement uncertainty for the temperature was 7%. In this case, an identification of quality indicators of the laser melting process is proposed, which has three levels: good quality (A), satisfactory quality (B), and unsatisfactory/unacceptable quality (C). The studies showed that the probability of achieving a good/high-quality (A) resulted in the laser melting process of 316L steel powder at a laser power of 195 W was 91%, while the probability of achieving satisfactory quality (B) was 0.03%. These findings contribute to enhancing in situ process monitoring in additive manufacturing, enabling the detection of deviations and adjustments to ensure consistently good quality. The proposed methodology provides a robust framework applicable across different LP-BF/M systems, improving process reliability and reproducibility in industrial and scientific applications.

Abstract Experimental studies were carried out to determine the temperature and emissivity of 316L powder steel. It was found that the emissivity of powder steel varied from 0.33 to 0.58 in the temperature range from 59 °C to 900 °C. Based on the proposed methodology for quantitative assessment of temperature measurement uncertainty, it was established that the maximum value of the relative combined uncertainty in temperature measurement does not exceed 5.5%. Using the measure of agreement (normalized deviation), it was confirmed that the obtained results demonstrated the laboratory’s sufficient technical competence.

Predicting the melt pool temperature of each melt track is a critical scientific challenge in monitoring laser powder bed fusion of metal powders in additive manufacturing. This study introduces a methodology for processing melt pool temperature data acquired with an infrared camera. The approach leverages measurement uncertainty concepts and analytical expressions that describe the Kalman filter algorithm. If the melting temperature of the metal powder is too low, it results in lack of fusion and the formation of porous, low-density components. Conversely, excessively high temperatures cause material evaporation and generate high recoil pressure. By applying this methodology, which uses Kalman filtering to determine the temperature of each melt track, the study allows for the examination of track temperatures across different laser power (LP) and enables the control of the laser powder bed fusion process within the specified limits of expanded measurement uncertainty. Experimental results on the laser melting of IN718 alloy powder reveal that optimal, complete melting occurs at an average temperature of 1455 °C ± 105 °C at a LP of 285 W. The proposed classification of laser melting quality indicators for the IN718 alloy powder facilitates the generation of control signals to adjust LP when the permissible expanded uncertainty limits are exceeded, ensuring high-quality melt pool conditions for the alloy powder.

In this study, the emissivity values of metal powder were examined by measuring reference temperature values using thermocouples, an infrared camera and aluminium foil to determine the reflection temperature. This enabled the testing of a methodology for determining emissivity in order to implement an intelligent control approach in additive manufacturing. The research established emissivity values for the surface of 316L powder steel that range from 0.33 to 0.46 in the temperature range from 50 to 600°C. The proposed approach allows the calibration of an infrared camera to accurately determine the temperature values of metal surfaces, which opens up the possibility of using the measurement results for intelligent control of laser power in additive manufacturing.

Abstract High-throughput and cost-efficient fabrication of intricate nanopatterns using top-down approaches remains a significant challenge. To overcome this limitation, advancements are required across various domains: patterning techniques, real-time and post-process metrology, data analysis, and, crucially, process control. We review recent progress in continuous, top-down nanomanufacturing, with a particular focus on data-driven process control strategies. We explore existing Machine Learning (ML)-based approaches for implementing key aspects of continuous process control, encompassing high-speed metrology balancing speed and resolution, modeling relationships between process parameters and yield, multimodal data fusion for comprehensive process monitoring, and control law development for real-time process adjustments. To assess the applicability of established control strategies in continuous settings, we compare roll-to-roll (R2R) manufacturing, a paradigmatic continuous multistage process, with the well-established batch-based semiconductor manufacturing. Finally, we outline promising future research directions for achieving high-quality, cost-effective, top-down nanomanufacturing and particularly R2R nanomanufacturing at scale.

The work used Maxwell's electromagnetic theory to quantitatively describe the emissivity of solid materials through electrical resistivity and temperature. An equation is proposed for recalculating the emissivity of smooth surfaces into powdery or rough surfaces. The obtained theoretical characteristics of the change in the emissivity of 316L powder steel were compared with experimental ones. As a result of the comparison, it was established that the experimental results obtained correlate with theoretical calculations and do not go beyond the limits of the expanded uncertainty of measurement.

High-resolution metrology is a critical area of development for nanoscale manufacturing, especially as it affects production throughput and fabrication quality. Atomic force microscopy (AFM) is one of the most popular tools for nanometrology, and high-resolution AFM often requires a significant time commitment and produces datasets of several million points. It is therefore critical for the development of data processing techniques to keep pace with the requirements of analyzing this type of data, and for these techniques to be portable as miniaturization in AFM is becoming more common. This work presents a data fitting algorithm designed for reducing the parameters of large-area data sets which utilizes well-established spline fitting techniques. In this paper we show that basis-spline fitting can be used to accurately represent large AFM data sets, including data sets with noisy data and sharp features, while achieving at least 90% parameter reduction in all test cases.

Abstract Multi-axis nanopositioning stages based on compliant mechanisms are a common topic within modern precision engineering due to their high accuracy, repeatability, and low cost compared to industry positioning stage alternatives. This paper details the design, computational optimization, and result evaluation of a two-axis flexural nanopositioning stage based on a modified version of the classic double parallelogram flexure (DPF) bearing (e.g. an underconstraint-eliminating DPF (UEDPF)). The two-axis flexural nanopositioning stage was optimized using response surface model with seven inputs and two outputs. The seven input parameters were the most sensitive geometric parameters for the UEDPF identified in previous work, and the two outputs were the flexure’s peak stress at maximum deflection and the force reaction felt by the 25mm displacement boundary condition. This paper shows that through design optimization, the first resonant mode of a long travel, two-axis flexural nanopositioning stage that has previously been reported in literature can be improved by a factor of two while still maintaining the higher-order resonant modes to be at least an order-of-magnitude higher than the fundamental mode of the stage. This is critical because increasing the fundamental mode without sacrificing the higher order modes will allow for a higher bandwidth controller to be implemented on this nanopositioning stage. The end goal of the positioning stage detailed in this paper is to be implemented within a micro-SLS 3D printer.

Abstract There is great interest in developing advanced electrical conductors with higher conductivity, lighter weight, and higher mechanical strength than copper (Cu). One promising candidate is copper‐graphene (Cu‐Gr) composite, which is hypothesized to have a higher electrical conductivity than Cu. In this work, it is shown that this is not true, supported by state‐of‐the‐art first‐principles calculations of electron transport. Particularly, contrary to the belief that graphene in the composite is more conductive than pristine Cu, it is less conductive due to increased scattering despite increased carrier concentration. On the other hand, it is found that compressive strain along the (111) plane increases the conductivity, which is confirmed experimentally, while tensile strain has little effect. The work offers new insights into understanding and developing advanced conductors.

Eliminating the need for multilayer alignment in nanoscale manufactured devices will streamline the lithography process and open up avenues for flexible substrate roll-to-roll (R2R) manufacturing. A system capable of single-exposure 3D holographic lithography with in-line metrology and real-time feedback will revolutionize micro and nano manufacturing. Work towards such developments are demonstrated to show promise in the field of nanopatterning.

Abstract The evolution and widespread presence of advanced computing has created avenues for incorporating more advanced modeling techniques into the classroom at an earlier stage of the engineering educational timeline. Since many students are now already well-versed in using technology in the classroom to enhance technical concepts, it is possible to guide students to more broad and advanced applications of computer aided design. Additionally, with the constant innovation of cheap, accessible rapid prototyping devices, it is now easier than ever to introduce students to manufacturing and prototyping to reinforce concepts and visualize the consequences of their design decisions. However, identifying outdated aspects of the course to be substituted with their modern counterparts can be challenging. In this paper, we describe the decisions made to create a more advanced design environment in an introductory-level course without losing critical engineering design foundations. This includes building a project-based curriculum focused on computer aided design of a product with considerations for multiple manufacturing methods, including 3D printing, laser cutting, and injection molding. In addition to comparing the learning outcomes, we also evaluated the student learning experience to ensure that the changes both increased classroom engagement and student-assessed value of their learning.

The acquisition of large atomic-force-microscopy (AFM) scans at nanoscale resolutions can take hours and produce datasets with millions of pixels, which is time consuming and computationally expensive to analyze. In this paper, we present an approach to speed up this process by using a computer-vision algorithm, namely the Noise2Noise algorithm, to reconstruct high-resolution, low scan speed AFM data from high-speed, noisy, sparsely sampled AFM data. This algorithm is trained on various noise types to reproduce different sources of experimental noises encountered during the acquisition of AFM data. Our results demonstrate that a sparse, uniform AFM scan of 20 × 20 μm at 128 × 128 pixel resolution can be processed within seconds, and the output image is comparable to a higher quality raw data scan which required 30 min or more to collect, reducing not only the acquisition and analysis time, but also the size of the data being collected.

This abstract discusses a novel MEMS thermal actuator that is incorporated within lattice-like structure that can change its stiffness on demand. Our experiments show that this actuator can achieve any desired stiffness, from highly negative stiffnesses all the way up to infinite positive stiffnesses, within 100 milliseconds. We also demonstrate that this design with closed loop control is robust to changes in external forces or stimuli. This discovery highlights how tunable stiffness metamaterials could be designed and fabricated practically for applications such as medical tools that adapt during surgery, car parts that reduce vibrations, and materials that work better for certain technologies like moving mirrors or switches.