A Model-Based Layer-to-Layer Control Algorithm for Ink-Jet 3D Printing

Ink-jet 3D printing is a promising technology for additive manufacturing, with the potential for impacting a wide variety of industries. In traditional ink-jet 3D printing, the part is built up by depositing droplets layer upon layer in an open-loop manner. Droplet and edge dimensions are typically predicted experimentally and are assumed to remain constant through the printing process. However, there is no guarantee of consistent droplet shape and dimensions or the smoothness of the finished parts due to uncertainties in the manufacturing process. To address this issue, we propose a model-based feedback control law for ink-jet 3D printing that uses a height sensor for measuring profile height after each layer for determining the appropriate layer patterns for subsequent layers. Towards this goal, a simple model describing the relationship between profile height change and droplet deposition in the layer building process is first proposed and experimentally identified. Based on this model, a closed-loop layer-to-layer control algorithm is then developed for the ink-jet printing process. Specifically, the proposed algorithm uses a model prediction control algorithm to minimize the difference between the predicted height and the desired height and the predicted surface unevenness after a fixed number of layers. Experimental results show that the algorithm is able to achieve more consistent shapes between layers, reduced edge shrinking of the part, and smoother surface of the top layer.

Functional Electrical Stimulation for Equilibrium-Point Control of Human Ankle Movement: Frequency Domain System Identification of Human Ankle Dynamics

This paper presents a novel method for creating an electrical stimulation pattern to control the equilibrium-point (EP) of human ankle movement. Focusing on the synergetic activation of agonist–antagonist (AA) muscles, the proposed method employs the ES-AA ratio (the ratio of the electrical stimulation levels for AA muscles) and the ES-AA sum (the sum of the electrical stimulation levels for AA muscles), which are based on the AA ratio (the ratio of the electromyography (EMG) voltage levels for AA muscles) and the AA sum (the sum of the EMG voltage levels for AA muscles) used in human movement analysis [1, 2]. The ES-AA ratio is related to the EP of the joint whereas the ES-AA sum is associated with mechanical stiffness of the joint. Using the AA concepts, we estimated the transfer function between the input ES-AA ratio (for the tibialis anterior (TA ) and gastrocnemius (GC)) and the force output of the endpoint in the ankle joint in an isometric environment by investigating the frequency characteristics, and finally found that the ankle-joint system was a second-order system with dead time in terms of the ES-AA ratio and foot force.

Selling Power System Flexibility: Ancillary Service and Real-Time Energy Market Challenges for Storage

Power system operators obtain the flexibility required to reliably balance aggregate generation and load through ancillary service and five-minute energy markets. Market prices are based on the marginal opportunity costs of the generators. This market design works well for generators but inherently fails for storage and demand response, denying these new technologies a fair opportunity to compete and denying the power system access to potentially lower cost reliability resources. Market design or regulatory changes may be required for storage and demand response to be viable ancillary service providers.

Methodology to Evaluate the Fuel Economy of a Multimode Combustion Engine With Three-Way Catalytic Converter

Highly diluted, low temperature homogeneous charge compression ignition (HCCI) combustion leads to ultra-low levels of engine-out NOx emissions. A standard drive cycle, however, would require switches between HCCI and spark-ignited (SI) combustion modes. In this paper a methodology is introduced, investigating the fuel economy of such a multimode combustion concept in combination with a three-way catalytic converter (TWC). The TWC needs to exhibit unoccupied oxygen storage sites in order to show acceptable performance. But the lean exhaust gas during HCCI operation fills the oxygen storage and leads to a drop in NOx conversion efficiency. Eventually the levels of NOx become unacceptable and a mode switch to a fuel rich combustion mode is necessary in order to deplete the oxygen storage. The resulting lean-rich cycling leads to a penalty in fuel economy. In order to evaluate the impact of those penalties on fuel economy, a finite state model for combustion mode switches is combined with a longitudinal vehicle model and a phenomenological TWC model, focused on oxygen storage. The aftertreatment model is calibrated using combustion mode switch experiments from lean HCCI to rich spark-assisted HCCI and back. Fuel and emissions maps acquired in steady state experiments are used. Two depletion strategies are compared in terms of their influence on drive cycle fuel economy and NOx emissions.

Numerical Analysis of the Tapping Atomic Force Microscopy on a Viscoelastic Sample

Mechanical characterization of thin samples is now routine due to the prominence of the Atomic Force Microscope. Advances in amplitude modulation techniques have allowed for accurate measurement of a sample’s elastic properties by interpreting the changes in the vibration of a cantilevered beam in intermittent contact. However, the nonlinearities associated with contact complicate attempts to find an accurate time-history for the beam. Furthermore, the inclusion of viscous effects, common to soft samples, puts an explicit solution even farther from reach. A numerical method is proposed that analyzes the time-history and frequency response of a microcantilever beam with a viscoelastic end-condition. The mathematics can be simplified by incorporating the viscoelastic end-condition into the equation of motion directly by modeling it as a distributed load. A forcing function can then be derived from the Standard Linear Solid model of viscoelasticity and implemented in the non-conservative work term of Hamilton’s principle. The Galerkin method can separate the resulting nonlinear equation of motion into time and space components. Performing a numerical analysis of the time factor equation provide the beam’s response over time. The results demonstrate the distinctive effects of viscoelasticity and periodic contact on the beam’s motion and provide the framework for the determination of viscous properties using dynamic techniques.

Observer Design for State Estimation of UV Curing Processes

This article discusses the challenges of non-intrusive state measurement for the purposes of online monitoring and control of Ultraviolet (UV) curing processes. It then proposes a two-step observer design scheme involving the estimation of distributed temperature from boundary sensing cascaded with nonlinear cure state observers. For the temperature observer, backstepping techniques are applied to derive the observer partial differential equations along with the gain kernels. For subsequent cure state estimation, a nonlinear observer is derived along with analysis of its convergence characteristics. While illustrative simulation results are included for a composite laminate curing application, it is apparent that the approach can also be adopted for other UV processing applications in advanced manufacturing.

Optimization of Fuel Economy of Hybrid Electric Vehicles Using Set Based Dynamic Programming

Dynamic Programming (DP) technique is an effective algorithm to find the global optimum. However when applying DP for finite state problems, if the state variables are discretized, it increases the cumulative errors and leads to suboptimal results. In this paper we develop and present a new DP algorithm that overcomes the above problem by eliminating the need to discretize the state space by the use of sets. We show that the proposed DP leads to a globally optimal solution for a discrete time system by minimizing a cost function at each time step. To show the efficacy of the proposed DP, we apply it to optimize the fuel economy of the series and parallel Hybrid Electric Vehicle (HEV) architectures and the case study of Chevrolet Volt 2012 and the Honda Civic 2012 for the series and parallel HEV’s respectively are considered. Simulations are performed over predefined drive cycles and the results of the proposed DP are compared to previous DP algorithm (DPdis). The proposed DP showed an average improvement of 2.45% and 21.29% over the DPdis algorithm for the series and the parallel HEV case respectively over the drive cycles considered. We also propose a real time control strategy (RTCS) for online implementation based on the concept of Preview Control. The RTCS proposed is applied for the series and parallel HEV’s over the drive cycles and the results obtained are discussed.

Repetitive Process Control of Laser Metal Deposition

The Laser Metal Deposition (LMD) process is an additive manufacturing process in which a laser and a powdered material source are used to build functional metal parts in a layer by layer fashion. While the process is usually modeled by purely temporal dynamic models, the process is more aptly described as a repetitive process with two sets of dynamic processes: one that evolves in position within the layer and one that evolves in part layer. Therefore, to properly control the LMD process, it is advantageous to use a model of the LMD process that captures the dominant two dimensional phenomena and to address the two-dimensionality in process control. Using an identified spatial-domain Hammerstein model of the LMD process, the open loop process stability is examined. Then, a stabilizing controller is designed using error feedback in the layer domain.

Application of Linear Observers to Estimate States of a Roll Simulator

In this research, a variety of Kalman Filters are implemented in an effort to estimate sled speed of a Roll Simulator. An Extended Kalman Filter (EKF) is incorporated to capture the nonlinear dynamics of the sled-platform assembly to estimate sled speed for the entire motion, as a linear Kalman Filter was found to be inadequate. When applied to experimental data, the EKF over-estimates sled speed, which is due to a disturbance force and/or uncertainty in system parameters. In combination with the disturbance observer, the Kalman Filter adequately estimates sled speed for experimental data. For lower speed/payload applications, a Kalman Filter using an accelerometer and measured drum speed is able to accurately track sled speed when a gain scheduling scheme is employed.