Development of a Neural Network Based Real Time Control for Laser Welding

1989 ◽  
Author(s):  
Nicholas E. Longinow ◽  
Edmund R. Bangs
Keyword(s):  
Neural Network ◽  
Laser Welding ◽  
Real Time ◽  
Real Time Control ◽  
Time Control

Neural-Network Based Approach for Real-Time Control of BMEP and MFB50 in a Euro 6 Diesel Engine

2017 ◽  
Author(s):  
Roberto Finesso ◽  
Ezio Spessa ◽  
Yixin Yang ◽  
Giuseppe Conte ◽  
Gennaro Merlino
Keyword(s):  
Neural Network ◽  
Diesel Engine ◽  
Real Time ◽  
Real Time Control ◽  
Time Control

Research on Edge Extraction System of the Laser Welding Image Based on ARM

2014 ◽  
Vol 701-702 ◽  
pp. 334-340
Author(s):  
Jin Ping Tang ◽  
Yu Jian Qiang ◽  
Liang Hua ◽  
Rong Pan
Keyword(s):  
Laser Welding ◽  
Real Time ◽  
Scale Transformation ◽  
Extraction System ◽  
Welding Parameters ◽  
Edge Extraction ◽  
Real Time Control ◽  
Display Module ◽  
Time Control ◽  
Welding Image

The article researched on edge extraction system of the laser welding image based on ARM micro controller. The system can achieve accurate extraction on the edge of the laser welding pool, and reduce the system cost effectively while satisfying the real-time constraints. With STM32f103 controller for the system as the core, the outside are image storage and display module. The image processing methods of gray-scale transformation, image smoothing, threshold segmentation and edge extraction are realized in ARM, and the processed images are displayed in LCD. The experimental results show that the system researched in this paper can extract color molten pool image edge accurately without distortion and better reflect the relationship between welding parameters and weld pool geometry size, and provide the basis for building real-time control system of the laser welding quality.

Real-time control in pulsed laser welding

1996 ◽  
Author(s):  
Regis de Charette ◽  
Frederic Coste ◽  
Lilian Sabatier ◽  
Jean-Pierre Chevalier
Keyword(s):  
Laser Welding ◽  
Real Time ◽  
Pulsed Laser ◽  
Real Time Control ◽  
Pulsed Laser Welding ◽  
Time Control

Multi-dimensional Taylor network modelling and optimal control of SISO nonlinear systems for tracking by output feedback

2019 ◽  
Vol 37 (3) ◽  
pp. 699-717 ◽  
Author(s):  
Qi-Ming Sun ◽  
Hong-Sen Yan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Systems ◽  
Real Time ◽  
Output Feedback ◽  
Tracking Error ◽  
Real Time Control ◽  
Network Modelling ◽  
Single Input Single Output ◽  
Time Control

Abstract In this paper, a multi-dimensional Taylor network (MTN) output feedback tracking control of nonlinear single-input single-output (SISO) systems in discrete-time form is studied. To date, neural networks are generally used to identify unknown nonlinear systems. However, the neuron of neural networks includes the exponential function, which contributes to the complexity of calculation, making the neural network control unable to meet the real-time requirements. In order to identify the controlled object whose model is unknown, the MTN, which requires only addition and multiplication, is utilized for successful real-time control of the SISO nonlinear system based on only its output feedback. Lyapunov analysis proves that output signals in the closed-loop system remain bounded and the tracking error converges to an arbitrarily small neighbourhood around the origin. In contrast to the back propagation (BP) neural network self-adaption reconstitution controller, the edge of the scheme is that the MTN optimal controller promises desirable response speed, robustness and real-time control.

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