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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...

Factory technologies have evolved to incorporate a great deal of manufacturing flexibility. Programmable automation in the form of CNC and PLCs along with hardware innovations (quick-change tooling, for example) and various operator assist technologies enable a high level of shop-floor flexibility. Possibly, the most inflexible part of a factory is the manufacturing information system. Customized for manufacturers by system integrators, these systems are often large monolithic systems assembled around an ERP/MRP framework or a precariously integrated set of decision-support software tools with a patchwork of communications enabling information flow between them. On the other hand, cloud-based information service platforms such as those encountered in social networks and service brokers have seen rapid and multiple cycles of evolution resulting in a meteoric rise in their ability to handle increasingly large data scales and rates, while still maintaining their elasticity and flexibility. This rapid evolution of cloud-based information services has ignited a new era in the manufacturing industry as evidenced by emerging manufacturing cyberphysical system technologies such as the Industrial Internet of Things (IIoT), and Cloud Manufacturing (CM). These technologies are part of the broader context of what is thought to be the unfolding fourth industrial revolution (Industry 4.0 or Digital Manufacturing). This revolution places at its core, connectivity, information, and machine-based intelligence to create a new paradigm for manufacturing that is highly flexible, scalable, responsive, and intelligent. This paper describes how we leveraged the newest advances in CPS, IIoT, CM, and distributed systems to create a flexible manufacturing information system infrastructure that separates information collection and distribution for decision-making functions. The first part of the paper introduces the architecture for a novel full-stack manufacturing infrastructure that is envisioned to facilitate and track the interaction between a manufacturing job, physical resources, and the software services (or apps) around them. We call this platform the Operating System for Cyber-physical Manufacturing (OSCM). In the second part of the paper, we introduce an event-based architecture for OSCM so that resource or transaction related events/data can be flexibly distributed to different decision-making/manufacturing software tools through an event/message exchange/bus. Further, such an architecture allows modularization and incremental development of different manufacturing software tools and services as new needs are identified.

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.

Precision stages, specifically flexure stages, are critical elements in micro- and nano-fabrication and metrology because of their ability to produce motion with nanoscale resolution as they are not limited by the effects of friction at sliding surfaces. In addition, they have the advantages of lower wear, longer life, no maintenance, and more repeatable and robust behaviors. However, these advantages come at the expense of the travel range. Displacements of flexure stages are typically very small and attempts to extend the range of motion by using less stiff flexures also increase the stage’s susceptibility to parasitic motions. Thus, in situations where accuracy, resolution, range, and stiffness are simultaneously needed, one is forced to deal with nonlinear (stiffening) behaviors that are inherent in any flexure stages. This paper addresses the problem of modeling the dynamics of a typical flexure stage used for producing oscillating linear motions for a micro-machining system. The nonlinear behavior of the flexure stage due to stretching of the flexures is modeled from first principles. Practical considerations associated with flexure stages such as preloading of the flexures, the distribution of its mass at different locations, and coupling to an actuator that itself has dynamics are also included in the model. Experimental verification of the model is used to assess its accuracy. Simulations are used to predict behavior in regimes not accessible by experiments.

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.

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.

Cylindrical surface and its tolerance verification play important role in machining process. Although there exists many approaches that can fit the maximum, minimum and minimum zone cylinders, the cylinder fitting problems can even be further simplified. The methodology developed in this paper seeks to reduce the number of parameters used in cylinder fitting model using the projection model. The model evaluates 3-D tolerance specifications by introducing the optimal direction of projection such that the 2-D point projected onto this direction has optimal tolerance specifications (maximum, minimum and minimum zone circles). Besides, a global optimum solver, Particle Swarm Optimization (PSO) is applied to avoid the problem of finding local optimum. The proposed simplified method shows consistent results when compared with the results from literature.

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.

Concentrating solar power (CSP) technology is an attractive approach to harvesting solar energy. Unlike photovoltaic (PV) technology, thermal storage is used in lieu of batteries for electricity ge...

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.

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, which were not conducted in a thermally controlled environment and the positioning repeatability of the machine.

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 the United States, over 50% of the unrecovered energy from industrial processes is in the form of low-grade heat (< 220°C). Materials and maintenance costs of common heat exchangers are typically too high to justify their usage. Polymers, though more affordable, are usually unsuitable for HX applications due to their low thermal conductivity (∼0.2 W/mK). Here, we show that metal-polymer hybrids may be attractive from both performance and cost perspectives. The use of polymers further increases the resistance to corrosion by sulfuric and carbonic acids often present in flue gases. An ongoing work explores different configurations of layered polyimide-copper macroscale hybrids for heat exchanger applications using numerical simulations. This paper explores a manufacturing pathway for producing such layered hybrid tubes that involves directly rolling and bonding tapes made of polymer and copper foil into tubes. A critical problem in the fabrication process is the bonding of metal and polymers. We explore approaches involving adhesives (epoxy, acrylic and silicone) for metal/polymer interfaces and direct welding (ultrasonic) for metal/metal interfaces that can be integrated into the manufacturing process. We report characterizations of the thermomechanical properties of these joining processes. This work paves the way for realizing cost-effective manufacturing of heat exchangers for low grade waste heat recovery.

A parameter identification procedure for identifying the parameters of a volumetric error model of a large and complex machine tool usually requires a large number of observations of volumetric error components in its workspace. This paper demonstrates the possibility of applying optimal observation/experimental design theories to volumetric error model parameter identification of a large 5-axis machine with one redundant axis. Several designs such as A-, D- and K-optimal designs seek to maximize the amount of information carried in the observations made in an experiment. In this paper, we adapt these design approaches in the construction of machine-tool error observers by determining locations in the workspace at which components of volumetric errors must be measured so that the underlying error model parameters can be identified. Many of optimal designs tend to localize observations at either the center or the boundary of the workspace. This can leave large volumes of the workspace inadequately represented, making the identified model parameters particularly susceptible to model inadequacy issues. Therefore, we develop constrained optimization algorithms that force the distribution of observation points in the machine’s workspace. Optimal designs provide the possibility of efficiency (reduced number of observations and hence reduced measurement time) in the identification procedure. This opens up the possibility of tracking thermal variations of the volumetric error model with periodic quick measurements. We report on the design, implementation and performance of a constrained K-optimal in tracking the thermal variations of the volumetric error over a 5.5 hour period of operations with measurements being made each hour.

Joining soft polymers and metals is receiving increasing attention in both industry and academia to enable the manufacturing of innovative products. One motivation arises from the production of next-generation heat exchanges, the structure of which is primarily composed of polymers and metals. Waste heat coming from low temperature exhaust gas stream is significant in industries in the U.S. However, traditional heat exchangers that are available to recover heat in the presence of small temperature difference are large and costly, restricting the wide application of such heat exchangers. To address this challenge, a hybrid materials design is proposed to achieve a balance between thermal conductivity and mechanical strength. High quality requirement induced by the changing operating conditions necessitates a strong bonding between polymers and copper. In this research, the possibility of using ultrasonic welding, which is conventionally employed to join dissimilar or similar metal layers, is explored. Preliminary results from welding experiments and tensile shear tests reveal that two bonding modes exist in the welding of PET and copper. Furthermore, analysis of power signals collected during welding shows that one can potentially monitor and optimize welding processes using monitoring signals. It is concluded from this study that ultrasonic welding has excellent potential in joining soft polymers and metals. Future work is also discussed on the process improvement and mechanism investigation.

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

Die casting is a type of metal casting in which a liquid metal is solidified in a reusable die. In such a complex process, measuring and controlling the process parameters are difficult. Conventional deterministic simulations are insufficient to completely estimate the effect of stochastic variation in the process parameters on product quality. In this research, a framework to simulate the effect of stochastic variation together with verification, validation, and uncertainty quantification (UQ) is proposed. This framework includes high-speed numerical simulations of solidification, microstructure, and mechanical properties prediction models along with experimental inputs for calibration and validation. Both experimental data and stochastic variation in process parameters with numerical modeling are employed, thus enhancing the utility of traditional numerical simulations used in die casting to have a better prediction of product quality. Although the framework is being developed and applied to die casting, it can be generalized to any manufacturing process or other engineering problems as well.