ARTIFICIAL INTELLIGENCE FUZZY LOGIC MODELING OF SURFACE ROUGHNESS IN PLASMA JET CUTTING PROCESS OF SHIPBUILDING ALUMINIUM ALLOY 5083

Abstract

In this paper the influence of different process parameters on surface roughness responses in plasma jet cutting process was investigated. Experimentations were conducted on shipbuilding aluminium 5083 sheet thickness 8 mm. Experimental work was performed according to Taguchi L27 orthogonal array by varying four parameters such as gas pressure, cutting speed, arc current and cutting height. Due to complexity of manufacturing process and aim to cover wide experimental space few constraints regarding cutting area were defined. Surface roughness parameters Ra and Rz were analysed as cut quality responses. In order to define mathematical model that will be able to describe effects of process parameters on surface roughness artificial intelligence (AI) fuzzy logic (FL) technique was applied. After functional relations between input parameters and surface roughness responses were defined prediction accuracy of developed fuzzy logic model was checked by comparison between experimental and predicted data. Mean absolute percentage error (MAPE) as well as coefficient of determination (R2) were used as validation measures. Finally, optimal process conditions that lead to minimal surface roughness were defined by creating response surface plots.