Artificial neural networks applied to arc welding process modeling and control

1990 ◽  
Vol 26 (5) ◽  
pp. 824-830 ◽  
Author(s):  
K. Andersen ◽  
G.E. Cook ◽  
G. Karsai ◽  
K. Ramaswamy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Modeling ◽  
Arc Welding ◽  
Welding Process ◽  
Modeling And Control ◽  
Artificial Neural ◽  
Arc Welding Process ◽  
And Control

Weld modeling and control using artificial neural networks

1995 ◽  
Vol 31 (6) ◽  
pp. 1484-1491 ◽  
Author(s):  
G.E. Cook ◽  
R.J. Barnett ◽  
K. Andersen ◽  
A.M. Strauss
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modeling And Control ◽  
Artificial Neural ◽  
And Control

Application of a Neural Manufacturing Concept to Process Modeling, Monitoring and Control

1995 ◽  
Vol 387 ◽  
Author(s):  
Chi Yung Fu ◽  
Loren Petrich ◽  
Benjamin Law
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Modeling ◽  
Lawrence Livermore National Laboratory ◽  
National Laboratory ◽  
Monitoring And Control ◽  
Artificial Neural ◽  
The Cost ◽  
And Control ◽  
Capital Intensive

AbstractThe cost of a fabrication line, such as one in a semiconductor house, has increased dramatically over the years, and it is possibly already past the point that some new start-up company can have sufficient capital to build a new fabrication line. Such capital-intensive manufacturing needs better utilization of resources and management of equipment to maximize its productivity. In order to maximize the return from such a capital-intensive manufacturing line, we need to work on the following: 1) increasing the yield, 2) enhancing the flexibility of the fabrication line, 3) improving quality, and finally 4) minimizing the down time of the processing equipment. Because of the significant advances now made in the fields of artificial neural networks, fuzzy logic, machine learning and genetic algorithms, we advocate the use of these new tools in manufacturing. We term the applications to manufacturing of these and other such tools that mimic human intelligence neural manufacturing. This paper describes the effort at the Lawrence Livermore National Laboratory (LLNL) [1] to use artificial neural networks to address certain semiconductor process modeling, monitoring and control questions.

Modeling and Control of the Lithographic Offset Color Printing Process Using Artificial Neural Networks

1994 ◽  
Vol 116 (2) ◽  
pp. 274-276 ◽  
Author(s):  
Ming-Shong Lan ◽  
P. Lin ◽  
J. Bain
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Model ◽  
Spatial Relationship ◽  
Printing Process ◽  
Modeling And Control ◽  
Color Printing ◽  
Artificial Neural ◽  
Flow Effects ◽  
And Control

This paper investigates the use of artificial neural networks (ANNs) for modeling and control of the lithographic offset color printing process. The color controller consists of two ANNs; the controller network, which learns an inverse model of the process, takes a set of desired colors as input and generates a set of ink key settings, while the model network learns a forward model of the process through which the controller network can be adapted by using the error backpropagation method. We use three-layer networks with “local” connections between neurons of adjacent layers for the process model as well as for the controller; the architectures address the spatial relationship of multiple inking zones and consider the crosswise ink flow effects existing in the printing process.

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