On The Limitations Of Neural Network Techniques For Analysing Cause
And Effect Relationships In Manufacturing Processes – A Case Study
Author(s)
R. S. Ransing, M. R. Ransing & R. W. Lewis
Abstract
On the limitations of neural network
techniques for analysing cause and effect
relationships in manufacturing processes
- a case study
R. S. Ransing, M. R. Ransing & R. W. Lewis
Civil and Computational Engineering Research Centre,
School of Engineering, University of Wales Swansea, SA2 8PP, UK
Abstract
The cause and effect relationship is complex for many manufacturing processes.
The ability to learn causal relationships from diagnostic examples is extremely
useful. This learning ability can help not only to quantify the influence of causes
on defects for existing components but also to set up new process, material and
design parameters to manufacture new components. A neural network approach
that can adapt and learn from past examples, was explored for analysing and
quantifying cause and effect relationships. Neural networks use the data to
extract any pre-existing relationships between the input and output variables.
However, in many real situations very few good quality training examples ar
Keywords
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