Kan Wang

High resolution aerosol jet printing of D- band printed transmission lines on flexible LCP substrate

In this paper, aerosol jet printing technology is assessed for D-band RF applications for the first time. It describes the fabrication process, the technology assessment and the characterization of coplanar waveguides (CPW) lines and CPW to microstrip transitions on liquid crystal polymer (LCP) in the D band using silver nanoparticle aerosol jet printing process. Feature sizes with a resolution of 10 μm, which is the finest resolution among all of the digital printing technologies, were realized successfully. The conductivity of the sintered silver structures was half to that of bulk silver after sintering at temperatures up to 200 °C. Printed transmission lines demonstrated losses of 0.35 dB/mm at 110 GHz and 0.51 dB/mm at 170 GHz that are less than that of the insertion loss of the inkjet printing lines by an order of magnitude.

Low-Loss 3-D Multilayer Transmission Lines and Interconnects Fabricated by Additive Manufacturing Technologies

This paper presents low-loss 3-D transmission lines and vertical interconnects fabricated by aerosol jet printing (AJP) which is an additive manufacturing technology. AJP stacks up multiple layers with minimum feature size as small as 20 μm in the xy-direction and 0.7 μm in the z-direction. It also solves the problem of fabricating vias to realize the vertical transition by 3-D printing. The loss of the stripline is measured to be 0.53 dB/mm at 40 GHz. The vertical transition achieves a broadband bandwidth from 0.1 to 40 GHz. The results of this paper demonstrate the feasibility of utilizing 3-D printing for low-cost multilayer system-on-package RF/millimeter-wave front-ends.

A facile method for integrating direct-write devices into three-dimensional printed parts

Integrating direct-write (DW) devices into three-dimensional (3D) printed parts is key to continuing innovation in engineering applications such as smart material systems and structural health monitoring. However, this integration is challenging because: (1) most 3D printing techniques leave rough or porous surfaces if they are untreated; (2) the thermal sintering process required for most conductive inks could degrade the polymeric materials of 3D printed parts; and (3) the extensive pause needed for the DW process during layer-by-layer fabrication may cause weaker interlayer bonding and create structural weak points. These challenges are rather common during the insertion of conductive patterns inside 3D printed structures. As an avoidance tactic, we developed a simple ‘print-stick-peel’ method to transfer the DW device from the polytetrafluoroethylene or perfluoroalkoxy alkanes film onto any layer of a 3D printed object. This transfer can be achieved using the self-adhesion of 3D printing materials or applying additional adhesive. We demonstrated this method by transferring Aerosol Jet® printed strain sensors into parts fabricated by PolyJet™ printing. This report provides an investigation and discussion on the sensitivity, reliability, and influence embedding the sensor has on mechanical properties.

CNT Enabled Co-braided Smart Fabrics: A New Route for Non-invasive, Highly Sensitive & Large-area Monitoring of Composites

The next-generation of hierarchical composites needs to have built-in functionality to continually monitor and diagnose their own health states. This paper includes a novel strategy for in-situ monitoring the processing stages of composites by co-braiding CNT-enabled fiber sensors into the reinforcing fiber fabrics. This would present a tremendous improvement over the present methods that excessively focus on detecting mechanical deformations and cracks. The CNT enabled smart fabrics, fabricated by a cost-effective and scalable method, are highly sensitive to monitor and quantify various events of composite processing including resin infusion, onset of crosslinking, gel time, degree and rate of curing. By varying curing temperature and resin formulation, the clear trends derived from the systematic study confirm the reliability and accuracy of the method, which is further verified by rheological and DSC tests. More importantly, upon wisely configuring the smart fabrics with a scalable sensor network, localized processing information of composites can be achieved in real time. In addition, the smart fabrics that are readily and non-invasively integrated into composites can provide life-long structural health monitoring of the composites, including detection of deformations and cracks.

Predictive model for carbon nanotube–reinforced nanocomposite modulus driven by micromechanical modeling and physical experiments

This article proposes an improved surrogate model for the prediction of the elastic modulus of carbon nanotube–reinforced-nanocomposites. By statistically combining micromechanical modeling results with limited amounts of experimental data, a better predictive surrogate model is constructed using a two-stage sequential modeling approach. A set of data for multi-walled carbon nanotube–bismaleimide nanocomposites is used in a case study to demonstrate the effectiveness of the proposed surrogate modeling procedure. In the case study, the theoretical composite modulus is computed with micromechanical models, and the experimental modulus is measured through tensile tests. Both theoretical and experimental composite moduli are integrated by using a statistical adjustment method to construct the surrogate model. The results demonstrate an improved predictive ability compared to the original micromechanical model.

A Review on the 3D Printing of Functional Structures for Medical Phantoms and Regenerated Tissue and Organ Applications

Abstract Medical models, or “phantoms,” have been widely used for medical training and for doctor-patient interactions. They are increasingly used for surgical planning, medical computational models, algorithm verification and validation, and medical devices development. Such new applications demand high-fidelity, patient-specific, tissue-mimicking medical phantoms that can not only closely emulate the geometric structures of human organs, but also possess the properties and functions of the organ structure. With the rapid advancement of three-dimensional (3D) printing and 3D bioprinting technologies, many researchers have explored the use of these additive manufacturing techniques to fabricate functional medical phantoms for various applications. This paper reviews the applications of these 3D printing and 3D bioprinting technologies for the fabrication of functional medical phantoms and bio-structures. This review specifically discusses the state of the art along with new developments and trends in 3D printed functional medical phantoms (i.e., tissue-mimicking medical phantoms, radiologically relevant medical phantoms, and physiological medical phantoms) and 3D bio-printed structures (i.e., hybrid scaffolding materials, convertible scaffolds, and integrated sensors) for regenerated tissues and organs.

Modulus prediction of buckypaper based on multi-fidelity analysis involving latent variables

Buckypapers are thin sheets produced from Carbon NanoTubes (CNTs) that effectively transfer the exceptional mechanical properties of CNTs to bulk materials. To accomplish a sensible tradeoff between effectiveness and efficiency in predicting the mechanical properties of CNT buckypapers, a multi-fidelity analysis appears necessary, combining costly but high-fidelity physical experiment outputs with affordable but low-fidelity Finite Element Analysis (FEA)-based simulation responses. Unlike the existing multi-fidelity analysis reported in the literature, not all of the input variables in the FEA simulation code are observable in the physical experiments; the unobservable ones are the latent variables in our multi-fidelity analysis. This article presents a formulation for multi-fidelity analysis problems involving latent variables and further develops a solution procedure based on nonlinear optimization. In a broad sense, this latent variable-involved multi-fidelity analysis falls under the category of non-isometric matching problems. The performance of the proposed method is compared with both a single-fidelity analysis and the existing multi-fidelity analysis without considering latent variables, and the superiority of the new method is demonstrated, especially when we perform extrapolation.

3-D PRINTING OF BIOLOGICAL TISSUE-MIMICKING AORTIC ROOT USING A NOVEL META-MATERIAL TECHNIQUE: POTENTIAL CLINICAL APPLICATIONS

Mimicking the dynamic mechanical properties of the human aorta in 3D printed models is challenging because of the inherent difference between mechanical behaviors of polymeric materials and human tissues (Fig. A). We sought to print the aortic root using materials which achieved the strain-

Optimisation of composite manufacturing processes with computer experiments and Kriging methods

This paper deals with the use of Kriging models in multi-objective optimisation of composite manufacturing processes. A comparative analysis of different optimisation methods was conducted for a case study devoted to resin transfer moulding (RTM) processes. The computerised experiments of composite manufacturing were based on a numerical simulation model of the RTM process. A Latin Hypercube Design (LHD) of the computer experiments was adopted. The computational results from the designed experiments were analysed and a Kriging model was built from the data. Finally, a data envelopment analysis (DEA) was conducted, based on the data generated from the Kriging model to optimise the RTM process. The overall performance of this method was evaluated.

Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation

Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions. Very often, however, researchers find the historical records of emerging surgical techniques, such as the transcatheter aortic valve replacement (TAVR), are highly scarce in quantity. In this paper, we address this challenge by proposing novel generative invertible networks (GIN) to select features and generate high-quality virtual patients that may potentially serve as an additional data source for machine learning. Combining a convolutional neural network (CNN) and generative adversarial networks (GAN), GIN discovers the pathophysiologic meaning of the feature space. Moreover, a test of predicting the surgical outcome directly using the selected features results in a high accuracy of 81.55%, which suggests little pathophysiologic information has been lost while conducting the feature selection. This demonstrates GIN can generate virtual patients not only visually authentic but also pathophysiologically interpretable.