LAUSR

This paper presents the development and application of fuzzy logic in the milling of thin-walled parts for the purpose of analyzing surface roughness. Surface roughness is an important performance indicator of finished components. Depending on conditions such as feed ratio and wall thickness, different machining strategies can be applied. The objective was to analyze and determine the influence of the machining conditions on surface roughness. The model for analyzing and determining surface roughness of the aluminum alloy AL 7075 was trained (design rules) and compared by using the experimental data. The average deviation of the compared data for surface roughness was 12.3%. The effect of the feed ratio, wall thickness and machining strategy as well as their interactions in machining are thoroughly analyzed and presented in this study.