An Introduction to MultiAgent Systems.

Additive manufacturing (AM) alias 3D printing translates computer-aided design (CAD) virtual 3D models into physical objects. By digital slicing of CAD, 3D scan, or tomography data, AM builds objects layer by layer without the need for molds or machining. AM enables decentralized fabrication of customized objects on demand by exploiting digital information storage and retrieval via the Internet. The ongoing transition from rapid prototyping to rapid manufacturing prompts new challenges for mechanical engineers and materials scientists alike. Because polymers are by far the most utilized class of materials for AM, this Review focuses on polymer processing and the development of polymers and advanced polymer systems specifically for AM. AM techniques covered include vat photopolymerization (stereolithography), powder bed fusion (SLS), material and binder jetting (inkjet and aerosol 3D printing), sheet lamination (LOM), extrusion (FDM, 3D dispensing, 3D fiber deposition, and 3D plotting), and 3D bioprinting....

Polymers for 3D Printing and Customized Additive Manufacturing

In this Progress Report we provide an update on recent developments in inkjet printing technology and its applications, which include organic thin-film transistors, light-emitting diodes, solar cells, conductive structures, memory devices, sensors, and biological/pharmaceutical tasks. Various classes of materials and device types are in turn examined and an opinion is offered about the nature of the progress that has been achieved.

Inkjet Printing—Process and Its Applications

An introduction to heat transfer

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Biomechanics And Motor Control Of Human Movement

Efforts to adapt and extend graphic arts printing techniques for demanding device applications in electronics, biotechnology and microelectromechanical systems have grown rapidly in recent years. Here, we describe the use of electrohydrodynamically induced fluid flows through fine microcapillary nozzles for jet printing of patterns and functional devices with submicrometre resolution. Key aspects of the physics of this approach, which has some features in common with related but comparatively low-resolution techniques for graphic arts, are revealed through direct high-speed imaging of the droplet formation processes. Printing of complex patterns of inks, ranging from insulating and conducting polymers, to solution suspensions of silicon nanoparticles and rods, to single-walled carbon nanotubes, using integrated computer-controlled printer systems illustrates some of the capabilities. High-resolution printed metal interconnects, electrodes and probing pads for representative circuit patterns and functional transistors with critical dimensions as small as 1 μm demonstrate potential applications in printed electronics.

High-resolution electrohydrodynamic jet printing

We present a pulsed dc voltage printing regime for high-speed, high-resolution and high-precision electrohydrodynamic jet (E-jet) printing. The voltage pulse peak induces a very fast E-jetting mode from the nozzle for a short duration, while a baseline dc voltage is picked to ensure that the meniscus is always deformed to nearly a conical shape but not in a jetting mode. The duration of the pulse determines the volume of the droplet and therefore the feature size on the substrate. The droplet deposition rate is controlled by the time interval between two successive pulses. Through a suitable choice of the pulse width and frequency, a jet-printing regime with a specified droplet size and droplet spacing can be created. Further, by properly coordinating the pulsing with positioning commands, high spatial resolution can also be achieved. We demonstrate high-speed printing capabilities at 1 kHz with drop-on-demand and registration capabilities with 3–5 µm droplet size for an aqueous ink and 1–2 µm for a photocurable polymer ink.

https://iopscience.iop.org/article/10.1088/0960-1317/20/9/095026/pdf

High-speed and drop-on-demand printing with a pulsed electrohydrodynamic jet

This paper presents an improved method for precision motion control by combining individual axis iterative learning control (ILC) and cross-coupled ILC (CCILC) into a single control input. CCILC is a new method in which a multi-axis cross-coupled controller (CCC) is reformatted into a single-input single-output (SISO) ILC format. Applying the techniques of ILC to CCC enables learning of the cross-coupled error which leads to a modified control signal and subsequent improvements in the contour trajectory tracking performance. In this paper, performance of the combined ILC and CCILC system is compared to standard feedback control through computer simulations and experimental testing on a Cartesian robotic system. Sufficient stability and convergence properties for the combined system are presented along with a modified approach for determining monotonic convergence of systems that are computationally challenging. The combined design is shown to enhance the precision motion control of the robotic system through performance improvements in individual axis tracking and contour tracking.

A Cross-Coupled Iterative Learning Control Design for Precision Motion Control

In this paper, we focus on improving performance and robustness in precision motion control (PMC) of multi-axis systems through the use of iterative learning control (ILC). A norm optimal ILC framework is used to design optimal learning filters based on design objectives. This paper contains two key contributions. The first half of this paper presents the norm optimal framework, including the introduction of an additional degree of design flexibility via time-varying weighting matrices. This addition enables the controller to take trajectory, position-dependent dynamics, and time-varying stochastic disturbances into consideration when designing the optimal learning controller. Explicit guidelines and analysis requirements for weighting matrix design are provided. The second half of this paper seeks to demonstrate the use of these guidelines. Using the design details provided in the paper, norm optimal learning controllers using time-invariant and time-varying weighting matrices are designed for comparison through simulation on a model of a multi-axis robotic testbed.

Kira Barton

Papers

High-resolution electrohydrodynamic jet printing

High-speed and drop-on-demand printing with a pulsed electrohydrodynamic jet

A Cross-Coupled Iterative Learning Control Design for Precision Motion Control

A Norm Optimal Approach to Time-Varying ILC With Application to a Multi-Axis Robotic Testbed

A desktop electrohydrodynamic jet printing system