Abstract: Problems related to manufacturing system operation and controls are complex and time consuming because of the non-linearities involved in their formulation and solution. Fast solutions to these problems can be obtained only through parallel processing. Neural nets provide massive parallel processing facilities and may also be used efficiently to model systems with non-linearities. The capabilities of neural nets can therefore be well utilized in modelling and processing problems related to manufacturing systems. In order to reduce the burden on computers, algorithms involving optimization and complex equations can be converted to heuristics. These heuristics can then be represented in terms of rules and an expert system can be built, with the added advantage of obtaining solutions in a time intensive fashion. This study studies the application of neural nets to problem solving in manufacturing system operation and control and demonstrates how present methods for solving such problems can be converted to the neural net approach.
Aman Sachdeva, B.D. Gupta and Sneh Anand, 2011. Minimizing Musculoskeletal Disorders Within Grinding Machine Workers Using Neural Networks. International Journal of Soft Computing, 6: 54-61.