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The real world simply doesnpsilat map well to binary distinctions, and numerical precision is often unhelpful in making qualitative statements. Computational modeling, namely fuzzy logic gives us a way to deal with such situations. Computational modeling leads to greater generality and better rapport with reality. It is driven by the need for methods of analysis and design, which can come to grips with the pervasive imprecision of the real world and exploit the tolerance for imprecision to achieve tractability, robustness and low cost solution. Machining operations confronted by a shortage of technical manpower and pricing competition, not only need to implement automated and operator-free technology, but also to meet the demands for much higher cut surface quality of complex profiles. Surface roughness quality has a large influence on the economics of the laser machining operation. Hence, this micro quality starts from the control of many parameters on the machine itself. Only highly qualify personnel with skillful experience will be able to obtain a good surface finish quality in shortest time possible. The machine head to table complex movement, with at least 14 controllable parameters and eight uncontrollable parameters often discourage researchers for traditional modeling approaches. The controlling of these parameters affects the cut quality seriously and offsets its precise requirement. This paper discusses a specific approach of surface roughness predictive modelling by Fuzzy Logic ~ a unique way of computational solution. The objective of the paper is: to design knowledge based rules, algorithm, architecture & learning ability to develop fuzzy surface roughness predictive model for laser machining; to develop fuzzy predictive model using design parameters which were critically analyzed; tomake a comparative study of the observed and modelled surface roughness output of 5 mm Mn-Mo pressure vessel plate.