Characterizing and predicting the self-folding behavior of weft-knit fabrics

This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this recordSelf-folding behavior is an exciting property of weft knit fabrics that can be created using just front and back stitches. This behavior is easy to create, but not easy to anticipate and currently cannot be predicted by existing computer aided design (CAD) software that controls the CNC knitting machines. This work identifies the edge deformation behaviors that lead to self-folding in weft knits, and methods to characterize the mechanical forces driving these behaviors with regard to chosen manufacturing parameters. With this data and analysis of the fabric deformations, the self-folding behavior was purposely controlled using calculated scaling factors. Furthermore, theoretical equations were developed to mathematically predict these scaling factors, minimizing the trial and error required to design with self-folding behavior and create textiles with novel engineered properties. By understanding the mechanisms responsible for creating these threedimensional self-folding textiles, they can then be designed in a programmable manner for use in technical applications.National Science FoundationUS Army Manufacturing Technology Program (US Army DEVCOM

Progressive refinement rendering of implicit surfaces

The visualisation of implicit surfaces can be an inefficient task when such surfaces are complex and highly detailed. Visualising a surface by first converting it to a polygon mesh may lead to an excessive polygon count. Visualising a surface by direct ray casting is often a slow procedure. In this paper we present a progressive refinement renderer for implicit surfaces that are Lipschitz continuous. The renderer first displays a low resolution estimate of what the final image is going to be and, as the computation progresses, increases the quality of this estimate at an interactive frame rate. This renderer provides a quick previewing facility that significantly reduces the design cycle of a new and complex implicit surface. The renderer is also capable of completing an image faster than a conventional implicit surface rendering algorithm based on ray casting

A hybrid kinetic Monte Carlo method for simulating silicon films grown by plasma-enhanced chemical vapor deposition

We present a powerful kinetic Monte Carlo (KMC) algorithm that allows one to simulate the growth of nanocrystalline silicon by plasma enhanced chemical vapor deposition (PECVD) for film thicknesses as large as several hundreds of monolayers. Our method combines a standard n-fold KMC algorithm with an efficient Markovian random walk scheme accounting for the surface diffusive processes of the species involved in PECVD. These processes are extremely fast compared to chemical reactions, thus in a brute application of the KMC method more than 99% of the computational time is spent in monitoring them. Our method decouples the treatment of these events from the rest of the reactions in a systematic way, thereby dramatically increasing the efficiency of the corresponding KMC algorithm. It is also making use of a very rich kinetic model which includes 5 species (H, SiH3, SiH2, SiH, and Si 2H5) that participate in 29 reactions. We have applied the new method in simulations of silicon growth under several conditions (in particular, silane fraction in the gas mixture), including those usually realized in actual PECVD technologies. This has allowed us to directly compare against available experimental data for the growth rate, the mesoscale morphology, and the chemical composition of the deposited film as a function of dilution ratio.open1

Plasma Modelling and Diagnostics for the Prediction of Structural Changes in Silicon Thin Film Deposition Plasma Modelling and Diagnostics for the Prediction of Structural Changes in Silicon Thin Film Deposition

In the present work, we apply a 2D self consistent model of SiH4/H2 discharges in order to investigate the transition from microcrystalline to amorphous silicon growth. The model is used to examine the relative importance of ions and atomic hydrogen in the film growth process. This examination also involves monitoring the changes that take place in the electrical properties of the plasma, the gas phase as well as the surface chemistry near the transition between the two growth regimes. Based on these results, the discussion is extended to the mechanism of a-Si:H to μc- Si:H growth transition and the use of plasma diagnostics that can be used to monitor this transition. Keywords: Micro Crystalline Si, deposition, PECVD, thin fil

Screen Space Ambient Occlusion Based Multiple Importance Sampling for Real-Time Rendering

International audienceWe propose a new approximation technique for accelerating the Global Illumination algorithm for real-time rendering. The proposed approach is based on the Screen-Space Ambient Occlusion (SSAO) method, which approximates the global illumination for large, fully dynamic scenes at interactive frame rates. Current algorithms that are based on the SSAO method suffer from difficulties due to the large number of samples that are required. In this paper, we propose an improvement to the SSAO technique by integrating it with a Multiple Importance Sampling technique that combines a stratified sampling method with an importance sampling method, with the objective of reducing the number of samples. Experimental evaluation demonstrates that our technique can produce high-quality images in real time and is significantly faster than traditional techniques