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Industrial production of graphene by chemical vapor deposition (CVD) requires more than the ability to synthesize large domain, high quality graphene in a lab reactor. The integration of graphene i...

Scale formation presents an enormous cost to the global economy. Classical nucleation theory dictates that to reduce the heterogeneous nucleation of scale, the surface should have low surface energy and be as smooth as possible. Past approaches have focused on lowering surface energy via the use of hydrophobic coatings and have created atomically smooth interfaces to eliminate nucleation sites, or both, via the infusion of low-surface-energy lubricants into rough superhydrophobic substrates. Although lubricant-based surfaces are promising candidates for antiscaling, lubricant drainage inhibits their utilization. Here, we develop methodologies to deposit slippery omniphobic covalently attached liquids (SOCAL) on arbitrary substrates. Similar to lubricant-based surfaces, SOCAL has ultralow roughness and surface energy, enabling low nucleation rates and eliminating the need to replenish the lubricant. To enable SOCAL coating on metals, we investigated the surface chemistry required to ensure high-quality functionalization as measured by ultralow contact angle hysteresis (3°). Using a multilayer deposition approach, we first electrophoretically deposit (EPD) silicon dioxide (SiO

Micro-transfer printing is a powerful methodology for the manufacturing of 3D heterogeneously integrated micro-systems. This paper explores an active elastomeric composite dense array stamp which improves throughput while enabling local monitoring and control of the process. This dense array stamp is a scalable 4x4 geometry design with a multiplexed interconnection scheme, ensuring a small footprint. It consists of 16 individual stamps, each with a Lead Zirconate Titanate (PZT) actuator and a strain gauge sensor. Actuation and sensing characterization and closed-loop feedback control were established, and the dense array stamp performance was validated using selective pickup and place micro-transfer printing experiments.

The present paper describes the development of a novel and comprehensive computational framework to simulate solidification problems in materials processing, specifically casting processes. Heat transfer, solidification and fluid flow due to natural convection are modeled. Empirical relations are used to estimate the microstructure parameters and mechanical properties. The fractional step algorithm is modified to deal with the numerical aspects of solidification by suitably altering the coefficients in the discretized equation to simulate selectively only in the liquid and mushy zones. This brings significant computational speed up as the simulation proceeds. Complex domains are represented by unstructured hexahedral elements. The algebraic multigrid method, blended with a Krylov subspace solver is used to accelerate convergence. State of the art uncertainty quantification technique is included in the framework to incorporate the effects of stochastic variations in the input parameters. Rigorous validation is presented using published experimental results of a solidification problem.

Significance The indirect nature of existing parallel micromachining strategies that combine sacrificial templates with top-down processes to etch 3D micro- and nanostructures inherently produces poor out-of-plane patterning fidelity. Here, the patterning fidelity of our process is measured for microscale curvilinear 3D objects to be less than 20 nm in rms averaged over features as wide as 10 µm. These results are attributed to increased pathways for diffusion, which increase the kinetics of the anodic reaction. Using this approach, arrays of nanotextured silicon lenses are deterministically imprinted to illustrate Mac-Imprint’s ability to directly pattern hierarchical micro- and nanostructures and enable fabrication of biomimetic optical designs on silicon., Scalable nanomanufacturing enables the commercialization of nanotechnology, particularly in applications such as nanophotonics, silicon photonics, photovoltaics, and biosensing. Nanoimprinting lithography (NIL) was the first scalable process to introduce 3D nanopatterning of polymeric films. Despite efforts to extend NIL’s library of patternable media, imprinting of inorganic semiconductors has been plagued by concomitant generation of crystallography defects during imprinting. Here, we use an electrochemical nanoimprinting process—called Mac-Imprint—for directly patterning electronic-grade silicon with 3D microscale features. It is shown that stamps made of mesoporous metal catalysts allow for imprinting electronic-grade silicon without the concomitant generation of porous silicon damage while introducing mesoscale roughness. Unlike most NIL processes, Mac-Imprint does not rely on plastic deformation, and thus, it allows for replicating hard and brittle materials, such as silicon, from a reusable polymeric mold, which can be manufactured by almost any existing microfabrication technique.

Surrogate neural network-based models have been lately trained and used in a variety of science and engineering applications where the number of evaluations of a target function is limited by execution time. In cell phone camera systems, various errors, such as interferences at the lens-barrel and lens-lens interfaces and axial, radial, and tilt misalignments, accumulate and alter profile of the lenses in a stochastic manner which ultimately changes optical focusing properties. Nonlinear finite element analysis of the stochastic mechanical behavior of lenses due to the interference fits is used on high-performance computing (HPC) to generate sufficient training and testing data for subsequent deep learning. Once properly trained and validated, the surrogate neural network model enabled accurate and almost instant evaluations of millions of function evaluations providing the final lens profiles. This computational model, enhanced by artificial intelligence, enabled us to efficiently perform Monte-Carlo analysis for sensitivity and uncertainty quantification of the final lens profile to various interferences. It can be further coupled with an optical analysis to perform ray tracing and analyze the focal properties of the lens module. Moreover, it can provide a valuable tool for optimizing tolerance design and intelligent components matching for many similar press-fit assembly processes.

Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlin-ear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)- based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial re-gression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.

Metallic nanostructures play an important role in micro and nanotechnology, from interconnects in electronics, electrodes in chemical sensors, batteries, plasmonic waveguides, structural color generation in sub-wavelength optics and catalysts in micro-reactor surfaces. Silver, of all the elements, possesses the highest electrical conductivity and (as a result) unique interaction with visible light when patterned at the nanoscale, making it ideal for electronics, optics and chemical sensing applications. Solid-State Superionic Stamping (S4) is an electrochemical nano-imprinting process that is capable of directly patterning silver nanostructures. In this paper, we demonstrate a simple method to scale up S4 patterning of silver using a plate-to-roll format to explore the potential of the S4 technology in a roll-to-roll configuration for high-volume manufacturing. In this format, a roller with a silver-coated polymer film wrapped around it is rolled without slip over a planar, patterned (x)AgI(1-x)AgPO3, x=0.5, stamp while creating an electrochemical cell with the silver film as the anode. This format allows us to explore the advantages of patterning with a moving line contact (as opposed to the large stationary surface contact in the imprinting/stamping format) between the stamp and roller, but without the added complexity of having to make a patterned electrolyte roller. We demonstrate the making of 6x10 mm2 patterned silver surfaces with arrays of 200 nm holes or pillars having an aspect ratio of up to 2. The effects of process parameters including rolling speed, contact pressure, patterning voltage and current on the pattern quality and the stamp surface degradation are studied.

Cloud manufacturing is quickly emerging as an infrastructure-as-a-service (IaaS), analogue for distributed production and supply chains, that integrates manufacturing providers and customers. This paper develops a cloud-based cyberphysical platform as a new service for spawning and organizing cloud manufacturing networks of computer numerical control (CNC) resources, provisioned and managed over the internet. The proposed layered architecture consists of hardware, virtualization, operating system, and network layers. Using modern web development technology, an operating system is created that services and virtualizes manufacturing resources in the network. This operating system offers a new, and much-needed model for physical resources and application software distribution in Cloud Manufacturing. The platform is designed with a micro-service architecture, that is used for manufacturing nodes and software applications or ‘assets’, and implemented in a Mongo, Express, Angular, Node (MEAN) stack.

Cloud application services are being implemented in all areas of engineering. Manufacturing engineering could benefit from the implementation of cloud-technologies that lead to an improved production and product-cost reduction. This paper proposes the development of a flexible, scalable, versatile, highly affordable and open-source cloud-platform for implementing sensor networks in support of advanced manufacturing. In particular, a two-layer-network architecture is present and explained through real applications in the area of advanced manufacturing. Two case studies present the collection, storage, and sharing of data over the network. Results show the successful integration of communication protocols across the cyber physical system as well as the reliability of the platform and its ability to log data. This architecture has the potential of generating great impact on the new area of digital manufacturing.

In this paper, we report a solid state direct writing method for fabricating copper nanostructures in seconds using atomic force microscopy. Under ambient conditions, a dendritic electrochemical reaction on the surface of a super-ionic (CuI) x (CuPO 3 ) (1- x) glass surface is demonstrated by negatively biasing an atomic force microscopy (AFM) probe relative to a Cu film counter electrode. Energy dispersive X-ray spectroscopy (EDS) is then used to characterize the dendrite and confirm electrochemical extraction of copper on the glass surface. To implement controlled electrochemical extraction, the copper growth process is then localized by realizing a ∼pA level current controlled electrochemical extraction process with a stationary AFM tip. Line patterns with ∼200 nm line-width are then generated in seconds over areas tens of microns in diameter by moving the AFM tip along the desired pattern while supplying a constant current to the tip. To the best of our knowledge, this is the first demonstration of a solid state direct writing approach for making copper nano-patterns on an ion conductive glass substrate.

This paper presents an approach to modeling the quasistatic errors of a 5-axis machine tool with one redundant axis. By introducing errors to the ideal joints and shape transforms of the kinematics of the machine, an error model is obtained. First order error characteristics are used to parameterize the introduced errors. It is found that of the 52 introduced error parameters, only 32 have a linearly independent effect on the volumetric errors observed in the machine's workspace. To identify these error parameters, the volumetric error components at 290 randomly chosen points are measured with a laser tracker. The unknown parameters are obtained by least-squares estimation, and the resulting model able to reduce average magnitude of the volumetric error vectors at these points by an average of 90% of their original values. Further, the identified model was used to predict the errors observed in two independent test point sets (each set consisting of 48 points). A 75% reduction in the average magnitude of the error vectors was observed. A large fraction of the residual errors was found to be attributable to the thermal drift of the machine during the experiments where were not conducted in a thermally controlled environment and the positioning repeatability of the machine.

This paper, the second of a set of two papers addressing parallel-kinematics MEMS stages for spatial translation, deals with fabrication, characterization and control of such devices. Double device layer SOI (silicon-on-insulator) substrates are used, providing three layers (two device layers and the handle) into which the elements of the stage can be mapped. Using the mechanism concept, realization scheme, and kinematic and dynamic models developed in the first paper of this set, this paper provides a detailed approach to fabricating these devices. The stages fabricated have a workspace cube of roughly 20 μm on the side, an in-plane stiffness of 96 N/m, and an out-of-plane stiffness of 166 N/m. Further, it characterizes the performance of the individual actuating and sensing elements, configures feedback controllers for each actuated joint, and assesses and verifies the stage’s designed performance. Finally, it demonstrates full 3-axis, closed-loop positioning of a MEMS stage.

Mesoscale electromechanical systems find applications in fields such as medical instrumentation, soft robotics, microscopy, flexible electronics and imagining. This paper implements the printed circuit MEMS (PC-MEMS) process [1] for the fabrication of a ‘pop-up’ flexure-based mesoscale system that exploits the simplicity of 2-D manufacturing techniques such as sheet-metal operations and laser cutting to realize a 3-D mechanism. The fabrication of a laminated Delta robot with prismatic actuation is presented to exemplify this process. A working device with actuation and functional components such as linear guides, stepper motors and limit switches is designed and fully realized. Because the mechanism is popped out of the plane to achieve its 3D shape, we present a stiffness analysis to arrive at the out-of-plane (or ‘pop-out’) angles that the planar system must accommodate so that constraints/limits on actuator torque/force can be can satisfied while producing an operational system. The simplicity of the processes makes it a candidate for the use in the emerging open-source hardware technologies for fabricating low-cost, complex, electromechanical systems.

Micro- and nanopositioning systems are widely used in the field of nanotechnology for probing, imaging, and increasingly for processing. This two-part set of papers presents a MEMS-scale parallel-kinematics mechanism, designed to achieve pure spatial (X, Y and Z) translation. With three independent kinematic chains connecting the end-effector to the base, a fully functional mechanism with axis actuation and displacement sensing is realized in a double device layer (“oreo”) SOI wafer using only conventional, microfabrication processes. This paper, the first in a two paper set, presents the mechanism, specially designed for scalable microfabrication. It analyzes its kinematics and dynamics, and characterizes its workspace. Part II of this set of papers describes the detailed design, fabrication, characterization and control of the devices.

In this article, we develop a generalized and scalable statistical model for accurate prediction of fouling resistance using commonly measured parameters of industrial heat exchangers. This prediction model is based on deep learning where a scalable algorithmic architecture learns non-linear functional relationships between a set of target and predictor variables from large number of training samples. The efficacy of this modeling approach is demonstrated for predicting fouling in an analytically modeled cross-flow heat exchanger, designed for waste heat recovery from flue-gas using room temperature water. The performance results of the trained models demonstrate that the mean absolute prediction errors are under 10 − 4 K W − 1 for flue-gas side, water side and overall fouling resistances. The coefficients of determination (R2), which characterize the goodness of fit between the predictions and observed data, are over 99%. Even under varying levels of measurement noise in the inputs, we demonstrate that predictions over an ensemble of multiple neural networks achieves better accuracy and robustness to noise. We find that the proposed deep-learning fouling prediction framework learns to follow heat exchanger flow and heat transfer physics, which we confirm using locally interpretable model agnostic explanations around randomly selected operating points. Overall, we provide a robust algorithmic framework for fouling prediction that can be generalized and scaled to various types of industrial heat exchangers.

Dropwise condensation of steam on hydrophobic substrates has a 10X higher heat transfer coefficient compared to filmwise condensation. To promote dropwise condensation, low surface energy hydrophobic coatings (polymers) are typically utilized. The low intrinsic thermal conductivity (k

In this paper, we demonstrate the combination of machine learning and three dimensional numerical simulations for multi-objective optimization of low pressure die casting. The cooling of molten metal inside the mold is achieved typically by passing water through the cooling lines in the die. Depending on the cooling line location, coolant flow rate and die geometry, nonuniform temperatures are imposed on the molten metal at the mold wall. This boundary condition along with the initial molten metal temperature affect the product quality quantified in terms of micro-structure parameters and yield strength. A finite volume based numerical solver is used to determine the temperature-time history and correlate the inputs to outputs. The objective of this research is to develop and demonstrate a procedure to obtain the initial and wall temperatures so as to optimize the product quality. The non-dominated sorting genetic algorithm (NSGA-II) is used for multi-objective optimization in this work. The number of function evaluations required for NSGA-II can be of the order of millions and hence, the finite volume solver cannot be used directly for optimization. Therefore, a multilayer perceptron feed-forward neural network is first trained using the results from the numerical solution of the fluid flow and energy equations and is subsequently used as a surrogate model. As an assessment, simplified versions of the actual problem are designed to first verify results of the genetic algorithm. An innovative local sensitivity based approach is then used to rank the final Pareto optimal solutions and select a single best design.

The advantages of utilizing the polymeric shape memory effect for deterministic assembly of microscale objects are explored in this paper. Assembly of building blocks, referred to as inks, by transfer printing is performed using a polymeric stamp with reversible adhesion as a manipulator. The dynamic rigidity control of a shape memory polymer (SMP) stamp afforded by heating or cooling it across its glass transition temperature allows for a dramatic increase in adhesion during ink retrieval. Furthermore, the shape-fixing and recovery property of the SMP material enables substantial freedom in stamp design, such as more complex surface patterning and heterogeneous surface features, to further reduce adhesion during release, not otherwise available. The mechanics of the stamp adhesion, including the effect of the material’s viscoelasticity and dynamic rigidity on performance, is discussed. Example structures, including a micro-motor structure with a movable rotor, are assembled in order to highlight the benefits afforded using the SMP material over existing alternatives. [2015–0026]

This paper presents the development of a novel vehicle-routing-based algorithm for optimizing component pick-up and placement on a collect-and-place type machine in printed circuit board manufacturing. We present a two-phase heuristic that produces solutions of remarkable quality with respect to other known approaches in a reasonable amount of computational time. In the first phase, a construction procedure is used combining greedy aspects and solutions to subproblems modeled as a generalized traveling salesman problem and quadratic assignment problem. In the second phase, this initial solution is refined through an iterative framework requiring an integer programming step. A detailed description of the heuristic is provided and extensive computational results are presented.

Micro-transfer printing technology is rapidly emerging as an effective pathway for large-scale heterogeneous materials integration. In its basic embodiment, micro-transfer printing is used to deterministically transfer and micro-assemble prefabricated structures/devices, referred to as “ink,” from a donor substrate to a receiving substrate using a viscoelastic elastomer stamp, usually made out of polydimethylsiloxane (PDMS). Laser Micro Transfer Printing (LMTP) is a laser-driven non-contact variant of the process that makes it independent of the receiving substrate's properties, geometry, and preparation. In this paper, an opto-thermo-mechanical model is developed to understand how the laser beam energy is converted to thermally-induced strains around the ink-stamp interface to initiate the ink delamination process. The opto-thermo-mechanical model is developed based on decoupling the optical absorption physics from the thermo-mechanical model physics. An optical absorption model for the laser beam energy absorbed by the ink is first developed and verified experimentally to estimate the heating rates of the ink-stamp system, which in turn are used as an input for a couple thermo-mechanical Finite Element Analysis (FEA) model. Further, high speed camera recordings for LMTP delamination are used to calibrate the thermo-mechanical model and verify its predictions. Besides providing a fundamental understanding of the delamination mechanism and the LMTP process capabilities, the developed opto-thermo-mechanical model is useful in selecting process parameters (laser pulse duration, stand-off distance), estimating the ink-stamp temperature rise during the LMTP process, and quantifying and decomposing the stresses at the ink-stamp interface to its main sources (Coefficient of Thermal Expansion (CTE) mismatch and thermal gradient strains).

Existing theory and data cannot quantify the contribution of phonon drag to the Seebeck coefficient (S) in semiconductors at room temperature. We show that this is possible through comparative measurements between nanowires and the bulk. Phonon boundary scattering completely quenches phonon drag in silicon nanowires enabling quantification of its contribution to S in bulk silicon in the range 25-500 K. The contribution is surprisingly large (∼34%) at 300 K even at doping of ∼3 × 10(19) cm(-3). Our results contradict the notion that phonon drag is negligible in degenerate semiconductors at temperatures relevant for thermoelectric energy conversion. A revised theory of electron-phonon momentum exchange that accounts for a phonon mean free path spectrum agrees well with the data.

A numerical model was developed to capture the charge-mass transport in electrochemical nanomanufacturing processes based on mixed-conducting solid electrolyte material systems. This model was verified by the matching of numerical predictions and experimental measurements of process parameters. The model was also used to predict parameters affecting ionic current flow, and to study the temporal and spatial transport properties of solid electrolyte silver sulfide during an electrode dissolution process. Conditions in which phase separation could occur in silver sulfide were found. Enhanced transport properties due to confinement in lateral dimensions were also observed through the developed model.

High performance motion systems play an important role in modern technology especially in the area of metrology and process equipment related to the field of micro- and nanotechnology. The purpose of this study is to show how to properly design and fabricate prototyped mechanisms that integrate metal-polymer flexural pivots for precision motion applications. Finite element simulations are used to understand the mechanical behavior of these components leading to dimensions that minimize the work done by the polymer structures. Simulations indicate that, the penetration distance of the metallic inserts into the polymer structures should be around one half of the total insert length and that the profile of the pressure interference can be modified to enhance the performance of the composite structure. A cantilever beam with a flexural pivot is used to test the performance of the design and a piezoelectric-actuated four bar mechanism is prototyped and studied as a general application of these types of structures to the precision motion field. The methodology implemented in this work poses a simple and affordable way to fabricate, assemble and customize low-cost devices for precision motion application and it applies to both, systems fabricated by polymer and metal rapid prototyping technologies.

Heat exchangers for low temperature ( 1.4 MPa internal pressure, under the normal operating conditions of the heat exchanger. Our work provides guidelines for designing macroscopic metal-polymer composites that can be adapted for specific requirements such as reduced materials costs, ease of manufacturing, and heat transfer enhancement. The optimized design of hybrid metal-polymer heat exchanger tubes potentially provides a scalable and cost-effective route toward harvesting waste heat from low temperature sources.

Metamaterials have made the exotic control of the flow of electromagnetic waves possible, which is difficult to achieve with natural materials. In recent years, the emergence of functional metadevices has shown immense potential for the practical realization of highly efficient photonic devices. However, complex and heterogeneous architectures that enable diverse functionalities of metamaterials and metadevices have been challenging to realize because of the limited manufacturing capabilities of conventional fabrication methods. Here, we show that three-dimensional (3D) modular transfer printing can be used to construct diverse metamaterials in complex 3D architectures on universal substrates, which is attractive for achieving on-demand photonic properties. Few repetitive processing steps and rapid constructions are additional advantages of 3D modular transfer printing. Thus, this method provides a fascinating route to generate flexible and stretchable 2D/3D metamaterials and metadevices with heterogeneous material components, complex device architectures, and diverse functionalities.

Microassembly of prefabricated structures and devices is emerging as key process technology for realizing heterogeneous integration and high-performance flexible and stretchable electronics. Here, we report on a laser-driven micro transfer placement process that exploits, instead of ablation, the mismatch in thermomechanical response at the interface of a transferable microstructure and a transfer tool to a laser pulse to drive the release of the microstructure from the transfer tool and its travel to a receiving substrate. The resulting facile pick-and-place process is demonstrated with the assembling of 3-D microstructures and the placement of GaN light-emitting diodes onto silicon and glass substrates. High-speed photography is used to provide experimental evidence of thermomechanically driven release. Experiments are used to measure the laser flux incident on the interface. These, when used in numerical and analytical models, suggest that temperatures reached during the process are enough to produce strain energy release rates to drive delamination of the microstructure from the transfer tool.

This paper presents the design, analysis, fabrication, and characterization of a closed-loop XY micropositioning stage. The stage design is based on a 2 degree-of-freedom parallel kinematic mechanism with linear characteristics. Integrated with sensing combs, and fabricated in SOI wafers, the design provides a promising pathway to closed-loop positioning microelectromechanical systems platform with applications in nanomanufacturing and metrology. The XY stage provides a motion range of 20 micrometers in each direction at the driving voltage of 100 V. The resonant frequency of the XY stage under ambient conditions is 600 Hz. The positioning loop is closed using a capacitance-to-voltage conversion IC and a feedback controller is used to control position with an uncertainty characterized by a standard distribution of 5.24 nm and a closed-loop bandwidth of about 30 Hz.

This paper discusses the development of a sensing and feedback–feedforward control system for Electrohydrodynamic jet (E-jet) printing. E-jet printing is a nano-manufacturing process that uses electric field induced fluid jet printing through nano-scale nozzles for achieving better control and resolution than traditional jet printing processes. The printing process is controlled by changing the voltage potential between the nozzle and the substrate. However, it is difficult to maintain constant operating conditions such as standoff height during a run of the printing process. The change in operating conditions results in fluctuating jet frequency and droplet diameter. For stabilizing the jetting frequency across a single run, a two degree of freedom (2 DOF) control algorithm is implemented. The feedforward voltage signal is used to compensate for repeatable changes in the operating conditions and is obtained using an Iterative Learning Control (ILC) algorithm. The feedback controller compensates for uncertainty in jetting operating conditions. The jetting frequency is determined in real time by recording electric current pulses when ink droplets are released from the nozzle. This frequency measurement is then used to control the voltage profile across a run to compensate for changing operating conditions. Experimental results are presented to validate the proposed control method.

We report electrohydrodynamic jet printing to deposit 2-27 μm diameter polymer droplets onto microcantilever sensors. The polymer droplets were deposited as single droplets or organized patterns, with sub-μm control over droplet diameter and position. The droplet size could be controlled through a pulse-modulated source voltage, while droplet position was controlled using a positioning stage. Gravimetry analyzed the polymer droplets by examining the shift in microcantilever resonance frequency resulting from droplet deposition. The resonance shift of 50-4130 Hz corresponded to a polymer mass of 4.5-135 pg. The electrohydrodynamic method is a precise way to deposit multiple materials onto micromechanical sensors with greater resolution and repeatability than current methods.

The advantages of utilizing the polymeric shape memory effect for deterministic assembly of microscale objects are explored in this letter. The assembly is performed by transfer printing, which involves a polymeric stamp with reversible adhesion as a manipulator. The dynamic rigidity control afforded by heating or cooling a shape memory polymer (SMP) stamp across its glass transition temperature allows for a dramatic increase in adhesion during pick-up. Furthermore, the shape-fixing and recovery property of the SMP material enables substantial freedom in stamp design, such as more complex surface patterning and heterogeneous surface features to further reduce adhesion during release not otherwise available.

Reversible control of adhesion is an important feature of many desired, existing, and potential systems, including climbing robots, medical tapes, and stamps for transfer printing. We present experimental and theoretical studies of pressure modulated adhesion between flat, stiff objects and elastomeric surfaces with sharp features of surface relief in optimized geometries. Here, the strength of nonspecific adhesion can be switched by more than three orders of magnitude, from strong to weak, in a reversible fashion. Implementing these concepts in advanced stamps for transfer printing enables versatile modes for deterministic assembly of solid materials in micro/nanostructured forms. Demonstrations in printed two- and three-dimensional collections of silicon platelets and membranes illustrate some capabilities. An unusual type of transistor that incorporates a printed gate electrode, an air gap dielectric, and an aligned array of single walled carbon nanotubes provides a device example.

Bend Me, Stretch Me In the push toward flexible electronics, much research has focused on using organic conducting materials, including light-emitting diodes (LEDs), because they are more readily processed using scalable techniques. Park et al. (p. 977 ) have developed a series of techniques for depositing and assembling inorganic LEDs onto glass, plastic, or rubber. Conventional processing techniques are used to connect the LEDs in order to create flexible, stretchable displays, which, because the active diode material only covers a small part of the substrate, are mostly transparent.

This paper presents the design, analysis, fabrication, and characterization of an active cantilever device integrated with a high-bandwidth 2-DOF translational (XY) micropositioning stage. The cantilever is actuated electrostatically through a separate electrode that is fabricated underneath the cantilever. Torsion bars that connect the cantilever to the rest of the structure provide the required compliance for the cantilever's out-of-plane rotation. The active cantilever is carried by a micropositioning stage, which enables high-bandwidth scanning to allow manipulation in three dimensions. The design of the microelectromechanical system stage is based on a parallel kinematic mechanism (PKM). The PKM design decouples the motion in the X- and Y-directions while allowing for an increased motion range with linear kinematics in the operating region (or workspace). The trusslike structure of the PKM also results in increased stiffness and reduced mass of the stage. The integrated cantilever device is fabricated on a silicon-on-insulator (SOI) wafer using surface micromachining and deep reactive ion etching processes. The actuation electrode of the cantilever is fabricated on the handle layer, while the cantilever and the XY stage are at the device layer of the SOI wafer. Two sets of electrostatic linear comb drives are used to actuate the stage mechanism in the X- and Y-directions. The cantilever provides an out-of-plane motion of 7 mum at 4.5 V, while the XY stage provides a motion range of 24 mum in each direction at the driving voltage of 180 V. The resonant frequency of the XY stage under ambient conditions is 2090 Hz. A high quality factor (~210) is achieved from this parallel kinematic XY stage. The fabricated stages will be adapted as chip-scale manufacturing and metrology devices for nanomanufacturing and nanometrology applications.

This paper reports on a novel piezo-driven, parallel-kinematic, micropositioning XY stage. This monolithic design is comprised of parallelogram four-bar linkages, flexure hinges, and piezoelectric actuators. Kinematic and dynamic analysis shows that the mechanical structure of the stage has a large work space, high bandwidth and good linearity. The stage system was run in open-loop mode to measure the step response and frequency response. The results show that the resonation frequencies of the two vibration modes are 563 and 536 Hz and the damping ratios are 0.049 and 0.0228. Two fiber optic sensors were added to the system to build a closed-loop positioning system. Linear and circular contouring performance in closed-loop mode suggests high scanning performance for such parallel-kinematic stages. The positioning resolution of the stage, limited only by the feedback sensors used, is about 20 nm.

In this paper we discuss the development and performance evaluation of a high-speed, 3-axis milling machine using a novel parallel kinematics x-y table. The x-y table is based on a an inversion of the Oldham coupling. The advantages of this kinematic configuration include low inertia, uniform kinematic conditioning, and dynamically matched axes. The design of the x-y table makes this system particularly well suited for high-speed contouring in the x-y plane. The kinematics, dynamics, and mechanical design of the system are described. Linear and circular contouring experiments are conducted to evaluate the system capabilities. The stiffness of both the mechanism as well as the direct drive actuation system are measured and reported. The experimental results demonstrate that the proposed mechanism offers an attractive combination of performance characteristics for high-speed contouring and high-speed machining.

Reliable and cost-effective machining of advanced ceramics is crucially important for them to be widely used in a number of critical engineering applications. The potential of Rotary Ultrasonic Machining (RUM) process has been recognized as one of the reliable and cost-effective machining methods for advanced ceramics and commercial machinery is available for the process. One limitation of the commercial RUM machines is that only circular holes can be efficiently machined. An approach to extend the RUM process to face milling of ceramics was proposed and the development of the experimental apparatus as well as the preliminary experimental results were published earlier in this journal. As a follow-up, this paper will present the results of an experimental investigation of the newly-developed Rotary Ultrasonic Face Milling (RUFM) process. In this investigation, a five-variable two-level fractional factorial design is used to conduct the experiments. The purpose of these experiments is to reveal the main effects as well as the interaction effects of the process parameters on the process outputs such as Material Removal Rate (MRR), cutting force, material removal mode and surface roughness.

Parallel link spatial mechanisms in general, and Stewart Platforms in particular, are increasingly being studied for possible use in multi-axis machine-tools. An important consideration in the design of such machines is their stiffness. For a given design, stiffness varies with the direction in which it is computed, the posture (or configuration) of the mechanism and the direction of the actuation or disturbing force. This paper addresses the problem of finding the minimum and maximum stiffnesses and the directions in which they occur for a manipulator in a given posture. In addition, the computation of stiffness in an arbitrary direction is also discussed. Since engineers are often interested in the response of the mechanism in the direction of perturbation (called single-dimensional or engineering stiffness in this paper), this paper proves the computed bounds (maximum and minimum) are tight for such a definition. Stiffness computed using the algebraic formulae derived are compared to those obtained from a Finite Element Analysis model to demonstrate correctness of formulation. Finally, minimum, maximum, arbitrary direction and single-dimensional stiffness maps, are produced for a Stewart Platform and their use in machine tool design is discussed.

In rotary ultrasonic machining of ceramic materials there exist two modes of material removal: brittle fracture mode and ductile mode. Two models were developed based on the assumption that the brittle fracture is the dominating mode of material removal, and were published previously. This paper presents the follow-up work on modeling of the ductile-mode material removal in rotary ultrasonic machining. After a brief review of the ductile phenomena in ceramic machining, an approach to modeling the ductile-mode removal in rotary ultrasonic machining is proposed. Then, magnesia stabilized zirconia is used to demonstrate the model's capability of predicting the material removal rate from the process parameters and the material property of the workpiece. Finally, the results of the pilot experiments to verify the model are discussed.

Configuration flexibility and deadlock-free operation are two essential properties of control systems for highly automated flexible manufacturing systems. Configuration flexibility, the ability to quickly modify manufacturing system components and their logical relationships, requires automatic generation of control executables from high level system specifications. These control executables must guarantee deadlock-free operation. The resource order policy is a configurable controller that provides the deadlock-free guarantee for buffer space allocation. It uses a total ordering of system machines and routing information to generate a set of configuration specific linear constraints. These constraints encode the system state along with a buffer capacity function and define a deadlock-free region of operation. Constraint generation and execution are of polynomial complexity.

The development of efficient deadlock avoidance policies (DAPs) for sequential resource allocation systems (RASs) is a problem of increasing interest in the scientific community, largely because of its relevance to the design of large-scale flexibly automated manufacturing systems. Much of the work on this problem existing in the literature is focused on the so-called single-unit RAS model, which is the simplest model in the considered class of RASs. Furthermore, due to a well-established result stating that, even for single-unit RASs, the computation of the maximally permissive DAP is intractable (NP-hard), many researchers (including our group) have focused on obtaining good suboptimal policies which are computationally tractable (scalable) and provably correct. In the first part of the paper, it is shown, however, that for a large subset (in fact, a majority) of single-unit RASs, the optimal DAP can be obtained in real-time with a computational cost which is a polynomial function of the system size (i.e., the number of resource types and the distinct route stages of the processes running through the system). The implications of this result for the entire class of single-unit RASs are also explored. With a result on the design of optimal DAPs for single-unit RASs, the second part of the paper concentrates on the development of scalable and provably correct DAPs for the more general case of conjunctive RASs.

Enhancement of the fluorescent output of surface-based fluorescence assays by performing them upon nanostructured photonic crystal (PC) surfaces has been demonstrated to increase signal intensities by >8000×. Using the multiplicative effects of optical resonant coupling to the PC in increasing the electric field intensity experienced by fluorescent labels (“enhanced excitation”) and the spatially biased funneling of fluorophore emissions through coupling to PC resonances (“enhanced extraction”), PC enhanced fluorescence (PCEF) can be adapted to reduce the limits of detection of disease biomarker assays, and to reduce the size and cost of high sensitivity detection instrumentation. In this work, we demonstrate the first silicon-based PCEF detection platform for multiplexed biomarker assay. The sensor in this platform is a silicon-based PC structure, comprised of a SiO2 grating that is overcoated with a thin film of high refractive index TiO2 and is produced in a semiconductor foundry for low cost, uniform, and reproducible manufacturing. The compact detection instrument that completes this platform was designed to efficiently couples fluorescence excitation from a semiconductor laser to the resonant optical modes of the PC, resulting in elevated electric field strength that is highly concentrated within the region

Among the various material removal processes applicable to ceramic materials, rotary ultrasonic machining has the potential for high material removal rate while maintaining low machining pressure and resulting in less surface damage. The limitation of rotary ultrasonic machining is that only circular holes or cavities can be machined due to the rotary motion of the tool. Attempts have been made by other researchers to extend rotary ultrasonic machining process to machining flat surfaces or milling slots. However, these extensions either changed the material removal mechanisms or had some severe drawbacks. One of the reasons for this might be an insufficient understanding of the material removal mechanisms involved. In this paper, a new approach to extend rotary ultrasonic machining to face milling of ceramics is proposed, which keeps all the material removal mechanisms of rotary ultrasonic machining. The development of the experimental apparatus and the design of the cutting tool are described. Preliminary experimental results are presented and discussed.

Direct writing of color patterns is investigated in (AgI)x(AgPO3)1−x ionically conductive glasses through the use of an electron beam. A range of glasses from x = 0–0.5 are explored to represent varying levels of ionic conductivity and the gamut of colors possible depending on substrate composition. For AgIAgPO3, writing capabilities are found to include a linear, tunable optical density of patterns in transmission, as well as diffraction limited color resolution in reflection. Writing at multiple currents, beam energies, and raster techniques are performed to explore the sensitivity to variations of process parameters. With advanced multiple beam voltage patterning techniques, the authors demonstrate the capability to both generate new colors as well as erase patterns previously created with this process.

Quasistatic error sources, which include thermal, mechanical loading and geometric error sources, are responsible for a very large proportion (typically, 70%) of the volumetric errors of a numerically controlled machine-tool. This paper, the first in a set of three, discusses the development of a very general model for the effects of geometric errors of the components of the kinematic chain (structural members and axes) of a machine on the volumetric errors in the work space. The effects of the other two sources are modeled as changes in the geometric error. The generality of the model arises from the fact that the errors along an axis of the machine can be characterized by any polynomial functions and the model is recursive in the order of these polynomials. This model can be used as the basis of a compensation scheme as well as in budgeting of errors on a machine-tool.

This paper is the second in a series of three papers addressing the problem of modeling and compensation of quasistatic machine-tool errors. In the first paper of this set, a general nth order model for the geometric errors of a machine was developed. The use of such a model as the basis of an on-line compensation scheme for a machine-tool requires that its parameters be known and changes in their magnitudes (due to mechanical and thermal changes) tracked during its operation. This paper presents a parameter estimation approach which requires observation of components of the volumetric error of the machine at certain points in the machine's work space. What is the minimal number of observations required? and What are the best locations for these observation points in a machine's work space? are questions answered in this paper. The estimation procedure is discussed for a first order model; however, it generalizes to any order error model. Experimental evidence of the effectiveness of the first order model error model in adequately representing the quasistatic errors of a machine-tool is presented. The effectiveness of the parameter estimation approach is also demonstrated. Finally, the use of the model and parameter estimation procedure as the basis of a compensation scheme that is practicable in a production environment is discussed.