The Rhino Software is Applied in the Computer Simulation Design of Non-Metallic Bulletproof Helmet

2012 ◽  
Vol 256-259 ◽  
pp. 2871-2876
Author(s):  
Jiang Chang Jin ◽  
Cheng Wang
Keyword(s):  
Computer Simulation ◽  
Simulation Design ◽  
The Way ◽  
Design Drawing

In the paper, the non-metallic bulletproof helmet is analyzed in research. The way can not accurately describe and transmit the shape messages of free-curve and free-camber , in design drawing by hand, design period long and a large amount of work, a large number of data are lose and man-made muff. Advance to use the way combine Rhino software and AutoCAD 2007 software in The Computer Simulation Auxiliary Design of non-metallic bulletproof helmet, raise design level.

Computer Simulation of the Deformation of Dough-Like Materials in a Parallel Plate Gripper

1996 ◽  
Vol 210 (2) ◽  
pp. 119-126 ◽  
Author(s):  
B S Evans ◽  
P N Brett
Keyword(s):  
Computer Simulation ◽  
Parallel Plate ◽  
Experimental Results ◽  
Computationally Efficient ◽  
Flexible Automation ◽  
Planning And Control ◽  
Careful Control ◽  
And Control ◽  
Automated Handling ◽  
The Way

Flexible automation for the handling of rigid materials has been available for many years, but similar systems are not as yet available for the handling of non-rigid materials such as doughs. The properties of this type of material require careful control of gripping and manipulation forces to prevent the product from deforming out of tolerance during handling. This paper describes a computationally efficient model that has been developed for use in the planning and control of gripping and manipulation strategies for dough-like materials. In addition to describing the model and the way in which it would be used in an automated handling environment, experimental results demonstrating the accuracy of the model in predicting the behaviour of three doughs are presented.

scholarly journals Implementation Of Learning Cycle 5E Model Assisted With Computer Simulation To Increase Physical Learning Outcome In Student Class VII SMPN 1 Torue

2017 ◽  
Vol 5 (2) ◽  
pp. 148
Author(s):  
Ni Putu Wiyandari ◽  
Amiruddin Hatibe ◽  
Sahrul Saehana
Keyword(s):  
Computer Simulation ◽  
Learning Outcomes ◽  
Action Observation ◽  
Study Data ◽  
Learning Cycle ◽  
Simulation Design ◽  
Student Activity ◽  
Research Material ◽  
Activity Test ◽  
Improve Student Learning

This study aims to improve students' physics learning outcomes of class VII SMP Negeri 1 Torue by using learning cycle 5E model assisted with computer simulation. Design of this action research methods follows the model of Kemmis and Mc. Taggart and it was done in two cycles. Each cycle consists of four stages of planning, implementation of action, observation and reflection. The subjects of this study are students of class VIIE SMP Negeri 1 Torue which amounted to 24 students. They consist of 13 male and 11 female students. The research material for cycle I is “form of substance” and cycle II materil is “expansion”. In this study, data were collected through observation and student activity, test of action result and interviews. On the evaluation of cycle I, we obtained classical completeness about 65.21% and classical absorption was about 68.26%. Student activity is in “enough category” with percentage about 78,25%. Teacher activity is in “good category” with 85% percentage. In the second cycle, we obtained learning classical completeness about 86.96% and classical absorption about 81.96%. Student activity and teacher activity are in “very good categories” with percentages of 90% and 96.67%, respectively. It was concluded that the implementation of 5E learning cycle learning model assisted computer simulation can improve student learning result of VIIE grade SMP Negeri 1 Torue.

scholarly journals The Stressed State Simulation of the Fixed Stepping Stoppers for the Auger Machine’s Cyclic Feeding Mechanism

2020 ◽  
Vol 174 ◽  
pp. 03002
Author(s):  
Alexey Khoreshok ◽  
Leonid Mametyev ◽  
Oleg Lyubimov ◽  
Lyudmila Samorodova ◽  
Oksana Alitkina
Keyword(s):  
Computer Simulation ◽  
Stressed State ◽  
Technical Solution ◽  
Simulation Design ◽  
State Technical ◽  
Siberian Region ◽  
Feeding Mechanism ◽  
Design Changes ◽  
Cyclic Feeding ◽  
Near Future

The article explains the importance of auger technology in mining and underground building. The contribution of the Siberian region to the development of methods for sinking of the horizontal and slightly deviated holes is noted. The modern problems facing the developers of auger complexes are formulated. The prerequisites, methods and results of studies conducted at the Kuzbass State Technical University (KuzSTU) are given. The result of it is the possibility of flexible refinement of the complex’s configuration for specific boring conditions with the verification of each applied technical solution by computer simulation. Design changes are outlined, the consequences of which can be taken into account in the near future for the auger complex’s development.

scholarly journals Neural Networks for Tested MG Artillery Fuses

2019 ◽  
Vol 49 (4) ◽  
pp. 157-186
Author(s):  
Dariusz Ampuła
Keyword(s):  
Neural Networks ◽  
Computer Simulation ◽  
Artificial Neural Networks ◽  
Test Results ◽  
Evaluation Data ◽  
Technical Parameters ◽  
Artificial Neural ◽  
Learning Test ◽  
The Way

Abstract The article presents the information about the usage of artificial neural networks. The automation process of neural networks of the analysed evaluation data results is highlighted. The kinds of MG type artillery fuses are described and the kinds of cartridges’ calibres, in which they are used, are also specified. The way of preparation of databases of test results to computer simulation is described. Building of neural networks determining the main technical parameters and sizes of learning, test and validation sets is characterized. The summary for chosen active neural networks for individual kinds of the analysed MG type artillery fuses is presented. Graphs of learning, values of sensibility indicators and fragments of prediction sheets for the chosen neural networks were shown.

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