Abstract
In this chapter, an attempt has been made to design suitable knowledge bases (KBs) for carrying out forward and reverse mappings of a Tungsten inert gas (TIG) welding process. In forward mapping, the outputs (also known as the responses) are expressed as the functions of the input variables (also called the factors), whereas in reverse mapping, the factors are represented as the functions of the responses. Both the forward as well as reverse mappings are required to conduct, for an effective online control of a process. Conventional statistical regression analysis is able to carry out the forward mapping efficiently but it may not be always able to solve the problem of reverse mapping. It is a novel attempt to conduct the forward and reverse mappings of a TIG welding process using fuzzy logic (FL)-based approaches and these are found to solve the said problem efficiently.
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