C3I Rapid Prototype Investigation.

1986 ◽  
Author(s):  
Philip C. Daley
Keyword(s):  
Rapid Prototype

Developments in the Field of Rapid Prototype Production

2008 ◽  
Vol 589 ◽  
pp. 421-425 ◽  
Author(s):  
Norbert Krisztián Kovács ◽  
József Gábor Kovács
Keyword(s):  
Mechanical Strength ◽  
Porous Structure ◽  
Epoxy Resin ◽  
Rapid Prototype ◽  
Post Treatment ◽  
3D Printer ◽  
Infiltration Depth ◽  
3D Printed ◽  
The Common ◽  
Polymer Engineering

Characteristics of 3D printed specimens are porous structure and low mechanical strength. Due to porous structure post treatment is possible, and in most cases infiltration with an epoxy resin, wax or cyanoacrylate material takes place. As a result of post treatment, the mechanical strength can be increased by 100%, although this is strongly influenced by the infiltration depth that depends on the porous structure and the resin viscosity. In the framework of the common research of the Department of Polymer Engineering, BME and Varinex Zrt. the applicability of a 3D printer is examined in the field of direct tool making. As the first step, the resin uptake ability of specimens prepared with a Z810 3D printer is examined.

scholarly journals Rapid Prototyping of Interactive Storytelling and Mobile Augmented Reality Applications for Learning and Entertainment – The case of “k-Knights”

2016 ◽  
Vol 6 (2) ◽  
pp. 30 ◽  
Author(s):  
Dimitris Markouzis ◽  
Georgios Fessakis
Keyword(s):  
Augmented Reality ◽  
Serious Games ◽  
Rapid Prototype ◽  
Technology Enhanced Learning ◽  
Mobile Augmented Reality ◽  
Design Development ◽  
Interactive Storytelling ◽  
Prototype Development ◽  
Definition Of ◽  
Pedagogical Research

Mobile Augmented Reality (MAR) Technology in combination with Interactive Storytelling (IS) enables the design of new kinds of technology enhanced learning and entertainment applications. The existing pedagogical research as well as the available Interactive Storytelling MAR (ISMAR) Serious Games are rather limited. This is mainly because of the difficulties of MAR applications development and the complexity of IS authoring. The paper works on the direction to improve this situation exploring the combination of a) rapid prototype development methodology based on MAR authoring tools and b) the definition of IS genres which could serve as templates and guide the ISMAR design. In the paper, key concepts are presented, existing successful examples of MAR Serious Games are analyzed in order to extract their narration genre features, available tools for MAR rapid authoring are introduced, and afterwards the design, development and first evaluation of a prototype ISMAR Serious Game is presented. The paper contributes to the bridging of learning design, IS, and AR technology research communities and facilitates feature interdisciplinary research.

An optimized architecture for a rapid-prototype-emulator

1993 ◽  
Vol 37 (1-5) ◽  
pp. 211-214 ◽  
Author(s):  
G. vom Bögel ◽  
K. Scherer ◽  
M. Bollerott
Keyword(s):  
Rapid Prototype

Precision Model of Predicting FDM Rapid Prototype Based on BP Neural Network

2008 ◽  
Vol 392-394 ◽  
pp. 891-897
Author(s):  
G.Q. Shang ◽  
C.H. Sun ◽  
X.F. Chen ◽  
J.H. Du
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fused Deposition Modeling ◽  
Rapid Prototype ◽  
Fused Deposition ◽  
Novel Approach ◽  
Deposition Modeling ◽  
Hidden Layer ◽  
Parts Manufacturing ◽  
Bp Neural Network Model

Fused deposition modeling (FDM) has been widely applied in complex parts manufacturing and rapid tooling and so on. The precision of prototype was affected by many factors during FDM, so it is difficult to depict the process using a precise mathematical model. A novel approach for establishing a BP neural network model to predict FDM prototype precision was proposed in this paper. Firstly, based on analyzing effect of each factor on prototyping precision, some key parameters were confirmed to be feature parameters of BP neural networks. Then, the dimensional numbers of input layer and middle hidden layer were confirmed according to practical conditions, and therefore the model structure was fixed. Finally, the structure was trained by a great lot of experimental data, a model of BP neural network to predict precision of FDM prototype was constituted. The results show that the error can be controlled within 10%, which possesses excellent capability of predicting precision.

Sign in / Sign up

Export Citation Format

Share Document