Research on the Key Technologies of Intelligent Control for Cap-Shape Bending of Sheet Metal

2011 ◽  
Vol 239-242 ◽  
pp. 2867-2872
Author(s):  
Hong Lei Sun ◽  
Chun Jian Su ◽  
Rui Xue Zhai
Keyword(s):  
Neural Network ◽  
Control System ◽  
Real Time ◽  
Sheet Metal ◽  
Intelligent Control ◽  
Feed Forward Neural Network ◽  
Forming Process ◽  
Intelligent Control System ◽  
The Neural Network ◽  
Real Time Identification

The blueprint for an intelligent control system of cap-shape bending has been advanced in this paper using neural network technology, aiming at an accurate control of bending springback, the prominent problem during the forming process for the cap-shape bending of sheet metal. The feed-forward neural network of real-time identification for material performance parameters and the friction coefficient have been established. The neural network identifies the parameters for real-time needed material performance, which utilizes the measurability of the physical quantities, and predicts the parameters for optimum technology, so a satisfied accuracy of convergence has been achieved. The intelligent control experimentation system of cap-shape bending has been established, the validity of which has been tested for four kinds of materials. The result of the tests proves the feasibility of the blueprint of the intelligent control system.

Digital twin of the chip formation process

2020 ◽  
pp. 81-86
Author(s):  
Yu.G. Kabaldin ◽  
D.A. Shatagin ◽  
M.S. Anosov ◽  
A.M. Kuz'mishina
Keyword(s):  
Neural Network ◽  
Control System ◽  
Crystal Lattice ◽  
Network Model ◽  
Intelligent Control ◽  
Neural Network Model ◽  
Formation Process ◽  
Chip Formation ◽  
Intelligent Control System ◽  
The Relationship

The formation of chips during the processing of various materials was studied. The relationship between the type of chips, the type of crystal lattice of the material and the number of sliding systems is shown. A neural network model of chip formation is developed, which allows predicting the type of chips. An intelligent control system for the process of chip formation during cutting is proposed. Keywords: chip formation, crystal lattice, neural network model, type of chips. [email protected]

scholarly journals Neuro-Fuzzy Control of a Robotic Manipulator

2014 ◽  
Vol 19 (3) ◽  
pp. 575-584 ◽  
Author(s):  
P. Gierlak ◽  
M. Muszyńska ◽  
W. Żylski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Intelligent Control ◽  
Fuzzy System ◽  
Operating Conditions ◽  
Intelligent Control System ◽  
Hybrid Controller ◽  
Compensatory Control ◽  
Neuro Fuzzy

Abstract In this paper, to solve the problem of control of a robotic manipulator’s movement with holonomical constraints, an intelligent control system was used. This system is understood as a hybrid controller, being a combination of fuzzy logic and an artificial neural network. The purpose of the neuro-fuzzy system is the approximation of the nonlinearity of the robotic manipulator’s dynamic to generate a compensatory control. The control system is designed in such a way as to permit modification of its properties under different operating conditions of the two-link manipulator

Hybrid MATLAB and LabVIEW with T-S Cloud Inference Neural Network to Realize a Flatness Intelligent Control System

2014 ◽  
Vol 85 (12) ◽  
pp. 1639-1652 ◽  
Author(s):  
Xiuling Zhang ◽  
Liang Zhao ◽  
Jiayin Zang ◽  
Hongmin Fan ◽  
Long Cheng
Keyword(s):  
Neural Network ◽  
Control System ◽  
Intelligent Control ◽  
Intelligent Control System

scholarly journals Real-Time Brain-Machine Interface Achieves High-Velocity Prosthetic Finger Movements using a Biologically-Inspired Neural Network Decoder

2021 ◽  
Author(s):  
Matthew S. Willsey ◽  
Samuel R. Nason ◽  
Scott R. Ensel ◽  
Hisham Temmar ◽  
Matthew J. Mender ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Finger Movements ◽  
Feed Forward Neural Network ◽  
Current Standard ◽  
Biologically Inspired ◽  
Current State ◽  
The Neural Network ◽  
Starting Point ◽  
Prosthetic Finger

AbstractDespite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to a control signal for the prosthetic device is one of the limitations in achieving rapid and realistic finger movements. To achieve more realistic finger movements, we developed a shallow feed-forward neural network, loosely inspired by the biological neural pathway, to decode real-time two-degree-of-freedom finger movements. Using a two-step training method, a recalibrated feedback intention–trained (ReFIT) neural network achieved a higher throughput with higher finger velocities and more natural appearing finger movements than the ReFIT Kalman filter, which represents the current standard. The neural network decoders introduced herein are the first to demonstrate real-time decoding of continuous movements at a level superior to the current state-of-the-art and could provide a starting point to using neural networks for the development of more naturalistic brain-controlled prostheses.

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