Hybrid Intelligence modeling of cut edge quality for Mn-Mo in laser machining by adaptive neuro-fuzzy inference system (ANFIS)

Abstract

Past few decades have seen a resurgent trend towards establishment of intelligent manufacturing systems, which are capable of using advanced knowledge-bases and intelligence techniques in aiding critical operational procedures in manufacturing. Increasing demands on productivity and quality with the increase in global competitiveness have necessitated development of sound predictive models and optimization strategies. This paper presents the modeling technique and prediction of cut edge quality for 2.5 mm Manganese Molybdenum pressure vessel plate by Hybrid Intelligence, namely, adaptive neuro-fuzzy inference system (ANFIS). The non-traditional laser machining, was used in the modeling investigation as this machining process requires controlling of more than seven critical parameters and to date, no researchers has used ANFIS to model this exact phenomenon. The modeling technique has been successfully developed to predict the cut edge quality with excellent degree of accuracy. Therefore the researcher strongly believes that ANFIS could be the best hybrid AI tool with the capability of data training and rule setting which has to be further explored with critical consideration in producing precise part of any material in the field of precision manufacturing.