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Smart Manufacturing, or Industry 4.0, has gained significant attention in recent decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As modern production methods continue to increase in complexity, there is a greater need to consider what variables can be physically measured. This advancement necessitates the use of physical sensors to comprehensively and directly gather measurable data on industrial processes; specifically, these sensors gather data that can be recontextualized into new process information. For example, artificial intelligence (AI) machine learning-based soft sensors can increase operational productivity and machine tool performance while still ensuring that critical product specifications are met. One industry that has a high volume of labor-intensive, time-consuming, and expensive processes is the semiconductor industry. AI machine learning methods can meet these challenges by taking in operational data and extracting process-specific information needed to meet the high product specifications of the industry. However, a key challenge is the availability of high quality data that covers the full operating range, including the day-to-day variance. This paper examines the applicability of soft sensing methods to the operational data of five industrial etching machines. Data is collected from readily accessible and cost-effective physical sensors installed on the tools that manage and control the operating conditions of the tool. The operational data are then used in an intelligent data aggregation approach that increases the scope and robustness for soft sensors in general by creating larger training datasets comprised of high value data with greater operational ranges and process variation. The generalized soft sensor can then be fine-tuned and validated for a particular machine. In this paper, we test the effects of data aggregation for high performing Feedforward Neural Network (FNN) models that are constructed in two ways: first as a classifier to estimate product PASS/FAIL outcomes and second as a regressor to quantitatively estimate oxide thickness. For PASS/FAIL classification, a data aggregation method is developed to enhance model predictive performance with larger training datasets. A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. For large datasets with high quality data that enable model training for more complex tasks, regression models that predict the oxide thickness of the product are also developed. Two types of models with different loss functions are tested to compare the effects of the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) loss functions on model performance. Both the classification and regression models can be applied in industrial settings as they provide additional information regarding the process outcome. Individually, these models can reduce the number of metrology steps in semiconductor factories, and when developed further, can empower the development of advanced process control strategies.

Efficiently shaping femtosecond, transverse Gaussian laser beams to flat-top beams with flat wavefronts is critical for large-scale material processing and manufacturing. Existing beam shaping devices fall short either in final beam homogeneity or efficiency. We present an approach that uses refractive optics to perform the majority of the beam shaping and then uses a fine-tune device (spatial light modulator) to refine the intensity profile. For the beam that we selected, circularly asymmetric with intensity fluctuations, our method achieved a uniformity of 0.055 within 90% of the beam area at 92% efficiency. The optimization involved an iterative beam shaping process that converged to optimum within 10 iterations.

The current research aims to predict the residual stress accumulation and evolution in the powder bed fusion processed multi-layer thin wall structures through a conforming mesh modeling approach. It involves the discrete element method (DEM) interfaced with the volume of fluid (VOF) method using computational fluid dynamics (CFD) coupled with the finite element method (FEM). The conforming mesh approach developed in the research predicts multi-physics, its induced porosity, and the cumulative effect on the residual stress in the powder bed fusion processed Ti-6Al-4V thin wall structures. The results of the residual stress in the multi-layered component from this method were further quantitatively compared with the non-conforming finite element method. The results show the conforming mesh approach was not only effective in capturing the layer geometry, and defects induced during the printing, but also predicted the residual stress in the region of the defect more accurately than the non-conforming mesh methods.

The properties and microstructure of Type 304L stainless steel produced by two additive manufacturing (AM) methods—directed energy deposition (DED) and powder bed fusion (PBF)—are evaluated and compared. Localized heating and steep temperature gradients of AM processes lead to significant residual stress and distinctive microstructures, which may be process-specific and influence mechanical behavior. Test data show that materials produced by DED and PDF have small differences in tensile strengths but clear differences in residual stress and microstructural features. Measured fatigue crack growth rates (FCGRs) for cracks propagating parallel to and perpendicular to the build directions differ between the two AM materials. To separate the influences of residual stress and microstructure, K-control test procedures with decreasing and constant stress intensity factor ranges are used to measure FCGRs in the near-threshold regime (crack growth rates ≤ 1 × 10−8 m/cycle). Residual stress is quantified by the residual stress intensity factor, Kres, measured by the online crack compliance method. Correcting the FCGR data for differences in Kres brings results for specimens of the two AM materials into agreement with each other and with results for wrought specimens, when the latter are corrected for crack closure. Differences in microstructure and tensile strength have an insignificant influence on FCGRs in these tests.

Defects in microarchitected materials exhibit a dual nature, capable of both unlocking innovative functionalities and degrading their performance. Specifically, while intentional defects are strategically introduced to customize and enhance mechanical responses, inadvertent defects stemming from manufacturing errors can disrupt the symmetries and intricate interactions within these materials. In this study, we demonstrate a nondestructive optical imaging technique that can precisely locate defects inside microscale metamaterials, as well as provide detailed insights on the specific type of defect.

Motivated by the need to harness the properties of renewable and biodegradable polymers for the design and manufacturing of multi-scale structures with complex geometries, we have employed our additive manufacturing platform that leverages molecular self-assembly for the production of metre-scale structures characterized by complex geometries and heterogeneous material composition. As a precursor material, we used chitosan, a chemically modified form of chitin, an abundant and sustainable structural polysaccharide. We demonstrate the ability to control concentration-dependent crystallization as well as the induction of the preferred orientation of the polymer chains through the combination of extrusion-based robotic fabrication and directional toolpathing. Anisotropy is demonstrated and assessed through high-resolution micro-X-ray diffraction in conjunction with finite element simulations. Using this approach, we can leverage controlled and user-defined small-scale propagation of residual stresses to induce large-scale folding of the resulting structures.

The incorporation of boron into a diamond lattice holds the potential to advance X-ray optics, offering the capability to manipulate various parameters of the lattice. This includes enhancing near-infrared absorption relative to pure diamond, thereby enabling Q-switchable optics. The use of MeV boron implantation emerges as a promising method for precisely doping the diamond lattice. However, for these optics to function effectively as Bragg-reflecting mirrors, ion implantation must be executed with meticulous attention to maintaining a strain-free, perfect diamond lattice. This study aimed to investigate the feasibility of utilizing a 9 MeV ion beam for high energy boron implantation. Different areas of a high-pressure, high-temperature (HPHT) diamond sample were subjected to irradiation with 9 MeV Boron ions, ranging in fluences from 5×1015 to 2.5×1016ions/cm2. Following boron implantation, high-temperature vacuum annealing was performed to restore the diamond lattice. Our assessment utilized X-ray rocking curve imaging, surface profilometry, and micro-Raman spectroscopy, with additional observations on near-infrared transmission properties. Our measurement of high-quality Bragg reflection through X-ray rocking curve imaging, sensitive to implantation-induced strain and defects, served as an key diagnostic for the effectiveness of this ion-implanted sample as a Bragg-reflecting optic.

The measuring accuracy of coordinate measuring machines (CMMs) will be affected by the different geometrical and dynamic errors, including the deviations associated to the axis displacement, the working table and the part to be measured. This work is focused on the analysis of the influence of the position errors, repeatability errors and reversibility errors in 3-axis FXYZ coordinate measuring machines, and it will be developed by a numerical model that is known as EE-based stochastic model. This model implements a new error index that is named equivalent error (EE), which will integrate the totality of machine errors of the CMMs and will allow a global description of all these error sources by means of a unique error parameter. The results obtained by this numerical model have been compared with the application of a traditional method, and it was probed that the EE-based model makes possible an increase of a 13.29% in the linear modelling of the performance of CMMs from the machine errors considered in this work, which implies a relevant improvement for the analysis and description of the effect of the distinct error sources on the achievable measuring accuracy of CMMs. For this reason, the EE-based model will be of special interest for industrial applications such as the quality control to be applied inside the production systems dedicated to manufacture mechanical components of high dimensional accuracy. [ABSTRACT FROM AUTHOR]

A novel approach for determining triaxial residual stress states by coupling energy dispersive X-ray diffraction and contour method measurements is provided and validated in a Ti‐5Al‐5V‐5Mo‐3Cr additive manufactured (AM) bridge sample. Synchrotron X-ray diffraction (SXRD) can provide relatively fine spatial resolution (on the order of 10–100 µm) for mapping 3D elastic strain fields within a sample. However, for samples with dimensions larger than one or two centimeters the path length can get prohibitive as both constant wavelength and energy dispersive SXRD are based upon transmission measurements. As an example, for a plate-like sample geometry where the thickness is limited (less than a centimeter) it is trivial to measure the longitudinal and height direction elastic strain components with excellent in-plane spatial resolution (∼100 µm) and a somewhat lower through-thickness resolution (10–15 times larger) but obtaining the through thickness component is often not possible as the beam path must be parallel to one of the large dimensions of the plate. While small samples of low Z-number alloys (e.g., Ti or Al) allow determination of the three elastic strain components, this is often not the case for higher Z alloys (e.g., Fe or Ni) or large samples where some strain components can be indeterminable. To overcome these limitations, this work applies a combination of synchrotron X-ray diffraction and the contour method (a mechanical relaxation technique), to determine the triaxial stress state. This novel combination is demonstrated and validated in a relatively small additive manufactured sample where all three orthogonal strain components are accessible via X-ray diffraction for stress determination. The paper also explores methods for determining the strain-free lattice parameter, typically obtained from a small stress-free reference sample. This work shows that a small size AM sample is not stress free, producing unreliable magnitudes of strain and stress. Instead, a strain-free lattice parameter is determined using residual stress equilibrium conditions, which gives consistent strain and stress trends for both X-ray diffraction (alone) and the new diffraction-contour coupling technique. This demonstrates that the coupling technique can be confidently applied to samples to determine stress when path length (size or Z-number) prohibit determination of three orthogonal strain components via diffraction.

We have developed a statistical model-based approach to the quality analysis (QA) and quality control (QC) of a gas micro pre-concentrator chip (μPC) performance when manufactured at scale for chemical and biochemical analysis of volatile organic compounds (VOCs). To test the proposed model, a medium-sized university-led production batch of 30 wafers of chips were subjected to rigorous chemical performance testing. We quantitatively report the outcomes of each manufacturing process step leading to the final functional chemical sensor chip. We implemented a principal component analysis (PCA) model to score individual chip chemical performance, and we observed that the first two principal components represent 74.28% of chemical testing variance with 111 of 118 viable chips falling into the 95% confidence interval. Chemical performance scores and chip manufacturing data were analyzed using a multivariate regression model to determine the most influential manufacturing parameters and steps. In our analysis, we find the amount of sorbent mass present in the chip (variable importance score = 2.6) and heater and the RTD resistance values (variable importance score = 1.1) to be the manufacturing parameters with the greatest impact on chemical performance. Other non-obvious latent manufacturing parameters also had quantified influence. Statistical distributions for each manufacturing step will allow future large-scale production runs to be statistically sampled during production to perform QA/QC in a real-time environment. We report this study as the first data-driven, model-based production of a microfabricated chemical sensor.

High-accuracy metrology is vitally important in manufacturing ultra-high-quality free-form mirrors designed to manipulate X-ray light with nanometer-scale wavelengths. The current capabilities and possibility for improvements in X-ray mirror manufacturing are limited by inherent imperfections of the integrated metrology tools. In the case of Fizeau interferometry, metrology tools are currently calibrated with super-polished flat test-standard/reference mirrors. This is acceptable for measuring slightly curved X-ray optics. However, for even moderately curved aspherical X-ray mirrors the flat-reference calibration is not sufficiently accurate and stitching Fizeau interferometer-based surface metrology is used to mitigate the problem. But still, the retrace and aberration errors, as well as the limited spatial resolution, described with the instrument transfer function (ITF), can be transferred into the optical surface topography of X-ray mirrors obtained in stitching metrology. For ITF calibration, we have developed an original technique, based on test standards structured as two-dimensional (2D) highly-randomized (HR) binary pseudo-random arrays (BPRAs). The technique employs the unique properties of the HR BPRA patterns in the spatial frequency domain., i.e. the inherent 2D power spectral density of the HR BPRA pattern has a deterministic white-noise-like character that allows direct determination of the ITF with uniform sensitivity over the entire spatial frequency range and field-of-view of an instrument. Here, we explore technological, metrological, and analytical aspects essential for calibration of the retrace and aberration errors of Fizeau interferometers using different types of tilted test samples, including a super-polished reference mirror for the re-trace calibration and the uniformly redundant array (URA) BPRA standards for the geometrical distortion (aberration) calibration. While the first method was previously demonstrated by researchers at DIAMOND Light Source, a method based on the URA BPRA is described and demonstrated here for the first time. We outline the design and fabrication process used in fabrication of URA BPRA test standards, and present the results of application of the URA BPRA standards demonstrating the high efficacy of our approach to geometrical distortion calibration of Fizeau interferometers. We also discuss the possible sources of unexpected peculiarities of the systematic errors, including an astigmatic character of the retrace error, observed with Fizeau interferometers at the Advanced Light Source X-Ray Optics Laboratory.

Livestock grazing in North American rangelands has the capacity both to promote and control the spread of undesirable plant species. Within California annual grasslands, desirable forage peaks in spring and supports considerable livestock grazing. However, spring grazing appears to promote the invasion and spread of two late-season and unpalatable non-native annual grasses, Aegilops triuncialis and Elymus caput-medusae. We tested the hypothesis that grazing reduces the leaf area and water use of early-spring annuals, thus increasing residual soil water availability for the late-season species. We used grazing-exclosure experiments to examine the interactive effects of simulated grazing (i.e., clipping) and competition on soil moisture availability, and on physiological, phenological, and demographic responses. When compared to unclipped controls, spring clipping significantly increased late-season volumetric soil moisture by 13–24% in the top 7 cm of soil, and 8–11% in the top 20 cm of soil (p

The Na-metal-free manufacturing approach can improve both the manufacturing and performance of Na metal solid-state batteries. While significant research has been dedicated to Li-metal-free manufacturing, the exploration of Na-based counterparts remains relatively nascent. Similar to Li-metal-free manufacturing, achieving uniform Na deposition remains a challenge when using a solid-state electrolyte. In this work, we demonstrated the ability to plate a uniform 2.0 mAh cm-2 Na metal anode by the simple placement of an Al foil-based current collector on NASICON solid-state electrolyte. The Na anode uniformity was dramatically improved by functionalizing the Al CC with a ∼96 nm thick layer of Au. It was shown that the in situ plated Na is homogeneous and dense and can be reversibly stripped and plated at a Coulombic efficiency of >90%. Our findings advance the understanding of Na nucleation and growth and offer insights into streamlining manufacturing processes for future all-solid-state Na anodes.

The over 15 million metric tonnes of carbon black produced annually emit carbon dioxide in the range of 29–79 million metric tonnes each year. With the renaissance of carbon black in many new renewable energy applications as well as the growing transportation sector, where carbon black is used as a rubber reinforcement agent in car tires, the carbon black market is expected to grow by 66% over the next 9 years. As such, it is important to better understand energy intensity and carbon dioxide emissions of carbon black production. In this work, the furnace black process is studied in detail using process models to provide insights into mass and energy balances, economics, and potential pathways for lowering the environmental impact of carbon black production. Current state-of-the-art carbon black facilities typically flare the tail gas of the carbon black reactor. While low in heating value, this tail gas contains considerable amounts of energy and flaring this tail gas leads to low overall efficiency (39.6%). The efficiency of the furnace black process can be improved if the tail gas is used to produce electricity. However, the high capital investment cost and increased operating costs make it difficult to operate electricity generation from the tail gas economically. Steam co-generation (together with electricity generation) on the other hand is shown to substantially improve energy efficiency as well as economics, provided that steam users are nearby. Steam co-generation can be achieved via back-pressure steam turbines so that the low-pressure exhaust steam (∼2 bar/120 °C) can be used locally for heating or drying purposes. Furthermore, the potential of utilizing hydrogen to reduce carbon dioxide emissions is investigated. Using hydrogen as fuel for the carbon black reactor instead of natural gas is shown to reduce the carbon dioxide footprint by 19%. However, current prices of hydrogen lead to a steep increase in the levelized cost of carbon black (47%).

Abstract: In concurrent work, we introduced a novel robotic package delivery system latching intelligent modular mobility system (LIMMS). Each LIMMS end effector requires a small, lightweight latching mechanism for pre-manufactured containers, such as cardboard boxes. In order to effectively process a high volume of packages, aligning the latching mechanism quickly and reliably is critical. Instead of depending on highly accurate controllers for alignment, we propose a novel self-aligning rotational mechanism to increase the system’s tolerance to misalignment. The radial latching design consists of evenly spaced blades that rotate into slots cut into the box. When misaligned, the blades contact the edges of the engagement slots, generating a self-correcting force that passively centers the blades with the slot pattern. This paper introduces a mathematical framework with closed form expressions to quantify error tolerance for these mechanisms. Through our mathematical and optimization analyses, it is shown that a two-blade design can tolerate a maximum misalignment of three times the radius to the blade tips, much larger than commonly used designs with three or more blade-like contacts. Our approach can be generalized for a class of rotational latching mechanisms with any number of blades. Utilizing this theory, a design process is laid out for developing an optimal self-aligning rotational latching mechanism given desired parameters and task constraints. With this methodology, we designed, manufactured, and verified the effectiveness of both two-blade and three-blade self-aligning in practical experiments.

Strengthening materials via conventional "top-down" processes generally involves restricting dislocation movement by precipitation or grain refinement, which invariably restricts the movement of dislocations away from, or towards, a crack tip, thereby severely compromising their fracture resistance. In the present study, a high-entropy alloy Al0.5CrCoFeNi is produced by the laser powder-bed fusion process, a "bottom-up" additive manufacturing process similar to how nature builds structures, with the microstructure resembling a nano-bridged honeycomb structure consisting of a face-centered cubic (fcc) matrix and an interwoven hexagonal net of an ordered body-centered cubic B2 phase. While the B2 phase, combined with high-dislocation density and solid-solution strengthening, provides strength to the material, the nano-bridges of dislocations connecting the fcc cells, i.e., the channels between the B2 phase on the cell boundaries, provide highways for dislocation movement away from the crack tip. Consequently, the nature-inspired microstructure imparts the material with an excellent combination of strength and toughness.

Laser-powder bed fusion additive manufacturing (LPBF-AM) of metals is rapidly becoming one of the most important materials processing pathways for next-generation metallic parts and components in a number of important applications. However, the large parametric space that characterizes laser-based LPBF-AM makes it challenging to understand what are the variables controlling the microstructural and mechanical property outcomes. Sensitivity studies based on direct LPBF-AM processing are costly and lengthy to conduct, and are subjected to the specifications and variability of each printer. Here we develop a fast-throughput numerical approach that simulates the LPBF-AM process using a cellular automaton model of dynamic solidification and grain growth. This is accompanied by a polycrystal plasticity model that captures grain boundary strengthening due to complex grain geometry and furnishes the stress-strain curves of the resulting microstructures. Our approach connects the processing stage with the mechanical testing stage, thus capturing the effect of processing variables such as the laser power, laser spot size, scan speed, and hatch width on the yield strength and tangent moduli of the processed materials. When applied to pure Cu and stainless 316L steel, we find that laser power and scan speed have the strongest influence on grain size in each material, respectively.

This paper describes a synthesis technique that constrains a spatial serial chain into a single degree-of-freedom mechanism using planar six-bar function generators. The synthesis process begins by specifying the target motion of a serial chain that is parameterized by time. The goal is to create a mechanism with a constant velocity rotary input that will achieve that motion. To do this we solve the inverse kinematics equations to find functions of each serial joint angle with respect to time. Since a constant velocity input is desired, time is proportional to the angle of the input link, and each serial joint angle can be expressed as functions of the input angle. This poses a separate function generator problem to control each joint of the serial chain. Function generators are linkages that coordinate their input and output angles. Each function is synthesized using a technique that finds 11 position Stephenson II linkages, which are then packaged onto the serial chain. Using pulleys and the scaling capabilities of function generating linkages, the final device can be packaged compactly. We describe this synthesis procedure through the design of a biomimetic device for reproducing a flapping wing motion.

Compact, rechargeable batteries in the capacity range of 1-100 mAh are targeted for form-factor-constrained wearables and other high-performance electronic devices, which have core requirements including high volumetric energy density (VED), fast charging, safety, surface-mount technology (SMT) compatibility, and long cycle life. To maximize the VED, anodeless solid-state lithium thin-film batteries (TFBs) fabricated by using a roll-to-roll process on an ultrathin stainless-steel substrate (10-75 μm in thickness) have been developed. A high-device-density dry-process patterning flow defines customizable battery device dimensions while generating negligible waste. The entire fabrication operation is performed in a conventional, humidity-controlled cleanroom, eliminating the need for a costly dry-room environment and allowing for simplified, lower-cost manufacturing. Such scale-up using an anodeless architecture also enables a thermal-budget-compatible packaging and metallization scheme targeted at industry-compatible SMT processes. Further manufacturability improvements, such as the use of high-speed tests, add to the overall range of elements necessary for mass production.

High-accuracy metrology is vitally important in manufacturing ultra-high-quality free-form mirrors designed to manipulate X-ray light with nanometer-scale wavelengths. However, surface topography measurements are instrument dependent, and without the knowledge of how the instrument performs under the practical usage conditions, the measured data contain some degree of uncertainty. Binary Pseudo Random Array (BPRA) “white noise” artifact are effective and useful for characterizing the Instrument Transfer Function (ITF) of surface topography metrology tools and wavefront measurement instrument. BPRA artifact contains features with all spatial frequencies in the instrument bandpass with equal weight. As a result, power spectral density of the patterns has a deterministic white-noise-like character that allows direct determination of the ITF with uniform sensitivity over the entire spatial frequency range. The application examples include electron microscopes, x-ray microscopes, interferometric microscopes, and large field-of-view Fizeau Interferometers. Furthermore, we will introduce the application of BPRA method to characterizing the ITF of Cylindrical Wavefront Interferometry (CWI), by developing the BPRA artifact which matches the radius of curvature of the cylindrical wavefront. The data acquisition and analysis procedures for different applications of the ITF calibration technique developed are also discussed.

Chromium (Cr)-coated Zircaloy fuel cladding has been considered a promising candidate materials system for accident tolerant fuels. In this work, two types of Cr coatings produced by cold sprayed (CS) and physical vapour deposited (PVD) methods were studied. In particular, a novel combination of C-ring compression tests at room temperature (RT) and 345 °C in an inert gas environment and real-time X-ray micro-computed tomography (XCT) imaging was adopted to investigate the failure processes. Before testing, the crystal structure and local properties were fully characterized; post testing, ex situ scanning electron microscope (SEM) imaging were conducted to complement the XCT measurements in crack density. It was found that the failure processes in both coatings vary with temperature, as discussed in detail. The hoop strength of first coating cracks’ formation of CS materials were higher than the PVD materials due to their higher interfacial roughness and distribution of splatted grains in CS coating. Based on a calculation of the first Dundurs’ parameter from the measured local properties and observed crack arrest/deflection at coating/substrate interface, it was found that the cold sprayed coating-cladding material system has a higher interfacial toughness in terms of critical strain energy release rate due to its interlocking interfacial structure.

A Clear Zone (CZ), the unobstructed roadside area with highly compacted soil, naturally accumulates high concentrations of pollutants from traffic activities. These pollutants are washed off by road runoff and enter waterways. In situ, treatment of polluted runoff from the CZ could not only protect water resources but also provide an opportunity to recharge groundwater. However, the soil in the CZ requires compaction, which limits the natural infiltration and treatment of road runoff. In this study, we examine whether and how amending the soil in the CZ with sand, a common bulking agent used in road design, and Expanded Shale, Clay, and Slate (ESCS) aggregates, a novel light-weight engineered bulking agent, could help treat stormwater in situ. ESCS-amended soil media infiltrated 220% more water than sand-amended soil under compaction, indicating that the addition of ESCS would make the CZ better at treating road runoff generated during high-intensity rainfall. Compared to sand-amended soil in the CZ, ESCS-amended soil provided 58% more plant-available water during prolonged drying, indicating that ESCS addition would help maintain vegetation, thereby minimizing maintenance needs. Finally, replacing sand with ESCS improved the soil capacity in the CZ to remove pollutants, including heavy metals and E. coli, indicating the performance life of ESCS-amended soil would be longer than that of sand-amended soil in the CZ. Collectively, these results indicate that the addition of ESCS as an alternative bulking agent to sand in compacted soil in the CZ could potentially treat road runoff in situ and prevent pollution originating from road infrastructure.

Aluminum alloys used in monolithic parts for aerospace applications are subjected to distortion and residual stress (RS) generated by milling, affecting the product fatigue life. Particularly, the change in RS with depth (z) has a characteristic distribution with a maximum compressive RS at a z several tens of micrometers from the surface; however, the RS value depends on the measurement method used. In this study, the RS distribution with z from the surface after milling was measured for the AA7050-T7451 aluminum alloy by two-dimensional X-ray diffraction (2D method). The results were compared with those of four prior measurement methods, and the validity of 2D method was verified. The changes in subsurface RS with z showed similar distributions under all measurement conditions except when cos(α)-XRD was employed. The 2D method provides high repeatability. The in-plane RS distribution was also measured using 2D method to investigate the effect of milling conditions on this distribution. The RS values varied markedly depending on the measurement position, particularly at a small collimator diameter of 0.146 mm, allowing detection of localized extreme RS values. The maximum RS at z = 0 mm was − 85.6 MPa at a cutting speed of vc = 200 m/s and feed per tooth of fz = 0.05 mm, while it was − 16 MPa for vc = 450 m/s and 6.8 MPa for fz = 0.2 mm, revealing that the compressive RS changes to tensile RS as vc and fz increase.

The complex thermal gradients of additive manufacturing (AM) result in residual stress and distinctive grain morphologies that influence mechanical performance and contribute to concern regarding the fatigue properties of AM parts. In this study, residual stress, microstructure, and fatigue crack growth rate (FCGR) results were compared in AM Type 304L stainless steel produced by laser powder bed fusion (PBF) on different systems using similar process parameters. Residual stress measured in the build direction was remarkably consistent in all PBF builds. Backscatter electron large area images revealed similar grain morphologies in the different builds, all of which exhibited elongated grains in the build direction and inhomogeneous grain size and shape. Fatigue crack growth investigated both parallel and perpendicular to the build direction revealed higher measured crack growth rates in the near-threshold regime in both orientations of the PBF material compared to wrought material. The difference in near-threshold fatigue crack growth rates is attributed primarily to the influence of processing-induced residual stress quantified by the residual stress intensity factor. These values revealed consistent FCGR effects in each orientation across specimens extracted from different builds and were then used to reveal a convergence of the corrected FCGR data of all PBF and wrought specimens.

A new generation of adaptive x-ray optics (AXO) is being installed on high-coherent-flux x-ray beamlines worldwide to correct and control the optical wavefront with sub-nm precision. These ultra-smooth mirrors achieve high reflectivities at glancing angles of incidence and can be hundreds of mm long. One type of adaptive x-ray mirror relies on piezoelectric ceramic strips which are segmented into channels and actuated to induce local, longitudinal bending, generating one-dimensional shape changes in the mirror substrate. A recently described mirror model uses a three-layer geometry with parallel actuators on the front and back surfaces of a thicker mirror substrate. By analogy to a solved problem in the thermal actuation of a tri-metal strip, we show that the achievable bending radius varies approximately as the square of the substrate thickness. We provide an analytic solution and simulate bending using a finite-element model.

A neural-network machine learning model is developed to control a bimorph adaptive mirror to achieve and preserve aberration-free coherent X-ray wavefronts at synchrotron radiation and free electron laser beamlines. The controller is trained on a mirror actuator response directly measured at a beamline with a real-time single-shot wavefront sensor, which uses a coded mask and wavelet-transform analysis. The system has been successfully tested on a bimorph deformable mirror at the 28-ID IDEA beamline of the Advanced Photon Source at Argonne National Laboratory. It achieved a response time of a few seconds and maintained desired wavefront shapes (e.g., a spherical wavefront) with sub-wavelength accuracy at 20 keV of X-ray energy. This result is significantly better than what can be obtained using a linear model of the mirror's response. The developed system has not been tailored to a specific mirror and can be applied, in principle, to different kinds of bending mechanisms and actuators.

Abstract: Conventional mobile robots have difficulty navigating highly unstructured spaces such as caves and forests. In these environments, a highly extendable limb could be useful for deploying hooks to climb over terrain, or for reaching hard-to-access sites for sample collection. This article details a new form of a multimodal mobile robot that utilizes a novel tape spring limb named EEMMMa (elastic extending mechanism for mobility and manipulation). Its innovative U-shaped tape structure allows it to handle loads in tension as well as compression. It can also bend using mechanical multiplexing for a lightweight and compact design that is well suited for mobile robots. For mobility, the limb can extend prismatically to deploy grappling hook anchors to suspend and transport the main body, or even serve as legs. For manipulation, the limb can morph its shape to bend around or over obstacles, allowing it to retrieve distant objects or position cameras around corners. The EEMMMa-1 prototype detailed in this article successfully demonstrates climbing ladders and shelves in 1.5 body lengths per second, and can bend up to 100 deg. A simplified model of the bending kinematics is developed and analyzed. This article concludes by detailing future EEMMMa applications and theories to strengthen the model in future studies.

A Twinning Induced Plasticity (TWIP) steel with a nominal composition of Fe-16.4Mn-0.9C-0.5Si-0.05Nb-0.05V was deformed to an engineering strain of 6%. The strain around the deformation twins were mapped using the 4D-STEM technique. Strain mapping showed a large average elastic strain of approximately 6% in the directions parallel and perpendicular to the twinning direction. However, the large average strain comprised of several hot spots of even larger strains of up to 12%. These hot spots could be attributed to a high density of sessile Frank dislocations on the twin boundary and correspond to shear stresses of 1–1.5 GPa. The strain and therefore stress fields are significantly larger than other materials known to twin and are speculated to be responsible for the early thickness saturation of TWIP steel nanotwins. The ability to keep twins extremely thin helps improve grain fragmentation, i.e. the dynamic Hall–Petch effect, and underpins the large elongations and strain hardening rates in TWIP steels.

Localized heating and resulting temperature gradients during additive manufacturing (AM) create significant residual stress that influences mechanical behavior, such as fatigue performance. To quantify residual stress effects on fatigue crack growth in AM materials, crack growth rates parallel and perpendicular to the build direction in directed energy deposition (DED) Type 304L austenitic stainless steel were measured. The on-line crack compliance method was used to determine the residual stress intensity factor, Kres, while simultaneously collecting fatigue crack growth rate (FCGR) data. Constant applied alternating stress intensity factor (constant ΔKapp) tests revealed the primary influence on measured FCGR is the orientation dependent Kres. Critical analysis of the compliance data from decreasing ΔKapp tests was used to quantify Kres, which was then used to correct FCGR data in the near-threshold regime. Results demonstrated that the fatigue response of DED Type 304L is inherently similar to that of annealed wrought Type 304/304L.

Owing to the continuous usage of centrifugal pump machines (CPM), health-monitoring systems are important for improving the hydraulic machine industry. While operating the CPM, supervision is carried out by either the operator or the online health-monitoring system. The Health Monitoring System (HMS) helps to understand the conditions of the pump and maintenance prospectus. For breakdown and accidents in the CPM industry, the prime reasons are impeller failure, bearing failure, cavitation, and shaft misalignment; therefore, it is necessary to supervise the parameters to provide a better life to the plant and enhance its efficiency using Data Acquisition (DAQ). The independent features and dependent features are selected and experimentally investigated to examine the characteristics of the CPM plant. The feature selection and extraction process should be compatible with the DAQ system which will help to reduce the curse of data biases and data preparation. Based on that three different DAQ systems based on Arduino, Raspberry Pi, and Dewsoft FFT are used in the experiments and compared based on 5-Point summary, Standard Deviation, cost analysis, flexibility, and maintenance. This paper presents the various techniques and procedures for acquiring the data from CPM using all three DAQ systems and selecting one of them through a statistical approach.

Gigahertz (GHz) femtosecond (fs) lasers have opened possibilities for enhancing and controlling the laser machining quality to engineer the physicochemical properties of materials. However, fundamental understanding of laser-material interactions by GHz fs laser has remained unsolved due to the complexity of associated ablation dynamics. Here, we study the ablation dynamics of copper (Cu) by GHz fs bursts using in situ multimodal diagnostics, time-resolved scattering imaging, emission imaging, and emission spectroscopy. A combination of probing techniques reveals that GHz fs bursts rapidly remove molten Cu from the irradiated spot due to the recoil pressure exerted by following fs pulses. Material ejection essentially stops right after the burst irradiation due to the limited amount of remnant matter, combined with the suppressed heat conduction into the target material. Our work provides insights into the complex ablation mechanisms incurred by GHz fs bursts, which are critical in selecting optimal laser conditions in cross-cutting processing, micro/nano-fabrication, and spectroscopy applications.

Stainless steel vessels see widespread use in superconducting magnets for particle accelerator applications. Their function varies in different magnet designs: they always provide the necessary liquid helium containment, but in some cases are also used to provide azimuthal prestress and can also be welded to the magnet end plate to provide additional longitudinal stiffness. A magnet designed with the bladder and key technology does not rely on the structural role of the vessel. They are structurally supported using azimuthally prestressed aluminum shells, and the longitudinal constraint by rods. In this case, the magnet designer would generally like to minimize the interaction between the magnet and the stainless-steel vessel and to minimize the coil stress variation due to the vessel. The stress state in the vessel and in the coil is a function of the circumferential interference, defined as the vessel azimuthal length minus the magnet circumference. The vessel and the magnet azimuthal length machining tolerances are relatively large resulting in significant stress variations in the superconducting coils. In this paper we introduce an interference-control shim, which can significantly limit the stress variation of the coils for a given variation of the interference. The effectiveness of the interference-control shim is evaluated numerically on the MQXF, the low-β quadrupole for the High Luminosity LHC.

In this article, a dual-stage positioning system for precisely tracking continuous scan and step scan profiles to meet the high-precision dynamic positioning requirements of space optical instruments is reported. A piezoelectric walking stage provides a long-stroke platform to which a short-stroke voice coil-actuated shuttle is mounted. The shuttle corrects for submicrometer deviations in the walking stage position and is designed with a low resonant frequency to passively reject disturbances produced by the walking stage. Both the walking stage and shuttle are instrumented with high-resolution interferometric encoders and a feedback system regulates the shuttle's position to achieve less than 7 nm RMS position error for constant velocity references of up to 0.5 mm/s. The shuttle is also capable of tracking step scan profiles with 10 ms settling times and 1.1 nm RMS position errors for steps up to 500 nm.

In this paper, the generalized SEIHRDP (susceptible-exposed-infective-hospitalized-recovered-death-insusceptible) fractional-order epidemic model is established with individual migration. Firstly, the global properties of the proposed system are studied. Particularly, the sensitivity of parameters to the basic reproduction number are analyzed both theoretically and numerically. Secondly, according to the real data in India and Brazil, it can all be concluded that the bilinear incidence rate has a better description of COVID-19 transmission. Meanwhile, multi-peak situation is considered in China, and it is shown that the proposed system can better predict the next peak. Finally, taking individual migration between Los Angeles and New York as an example, the spread of COVID-19 between cities can be effectively controlled by limiting individual movement, enhancing nucleic acid testing and reducing individual contact.

Abstract: The study presented in this paper applies hidden Markov modeling (HMM) to uncover the recurring patterns within a neural activation dataset collected while designers engaged in a design concept generation task. HMM uses a probabilistic approach that describes data (here, fMRI neuroimaging data) as a dynamic sequence of discrete states. Without prior assumptions on the fMRI data's temporal and spatial properties, HMM enables an automatic inference on states in neurocognitive activation data that are highly likely to occur in concept generation. The states with a higher likelihood of occupancy show more activation in the brain regions from the executive control network, the default mode network, and the middle temporal cortex. Different activation patterns and transfers are associated with these states, linking to varying cognitive functions, for example, semantic processing, memory retrieval, executive control, and visual processing, that characterize possible transitions in cognition related to concept generation. HMM offers new insights into cognitive dynamics in design by uncovering the temporal and spatial patterns in neurocognition related to concept generation. Future research can explore new avenues of data analysis methods to investigate design neurocognition and provide a more detailed description of cognitive dynamics in design.

Using a new Bayesian Optimization algorithm to guide the design of mechanical metamaterials, we design nonhomogeneous 3D structures possessing the Cauchy symmetry, which dictates the relationship between continuum and atomic deformations. Recent efforts to merge optimization techniques with the design of mechanical metamaterials has resulted in a concentrated effort to tailor their elastic and post elastic properties. Even though these properties of either individual unit cells or homogenized continua can be simulated using multi-physics solvers and well established optimization schemes, they are often computationally expensive and require many design iterations, rendering the validation stage a significant obstacle in the design of new metamaterial designs. This study aims to provide a framework on how to utilize miniscule computational cost to control the elastic properties of metamaterials such that specific symmetries can be accomplished. Using the Cauchy symmetry as a design objective, we engineer structures through the strategic arrangement of 5 different unit cells in a 5×5×5 cubic symmetric microlattice structure. This lattice design, despite constituting a design space with 510 3D lattice configurations, can converge to an effective solution in only 69 function calls as a result of the efficiency of the new Bayesian optimization scheme. To validate the mechanical behavior of the design, the lattice structures were fabricated using multiphoton lithography and mechanically tested, revealing a close correlation between experiments and simulated results in the elastic regime. Ultimately, a similar methodology can be utilized to design metamaterials with other material properties, aspiring to control properties at different length scales, an endeavor that requires inordinate computation cost.

Acoustic patterning of micro-particles has many important biomedical applications. However, fabrication of such microdevices is costly and labor-intensive. Among conventional fabrication methods, photo-lithography provides high resolution but is expensive and time consuming, and not ideal for rapid prototyping and testing for academic applications. In this work, we demonstrate a highly efficient method for rapid prototyping of acoustic patterning devices using laser manufacturing. With this method we can fabricate a newly designed functional acoustic device in 4 hours. The acoustic devices fabricated using this method can achieve sub-wavelength, complex and non-periodic patterning of microparticles and biological objects with a spatial resolution of 60 μm across a large active manipulation area of 10 × 10 mm2.

We suggest a new method of making ultra-low blaze angle gratings for synchrotron application. The method is based on reduction of the blaze angle of a master grating by replication followed by a plasma etch. A master blazed grating with a relatively large blaze angle is fabricated by anisotropic wet etching of a Si single crystal substrate. The surface of the master grating is replicated by a polymer material on top of a quartz substrate by nanoimprinting and then transferred into quartz by a plasma etch. Then a 2nd nanoimprint step is applied to transfer the saw-tooth surface into a resist layer on top of a Si grating substrate. The plasma etch through the patterned resist layer provides transfer of the grooves into the Si substrate and results in reduction of the blaze angle due to the difference in etch rates of the resist and Si. We investigated the impact of the replication process on the groove shape, facet surface roughness, and diffraction efficiency of the fabricated 200 lines/mm low blaze angle grating.