Application of grey relational analysis based on Taguchi method for optimizing machining parameters in hard turning of high chrome cast iron
作者机构：Department of Manufacturing Engineering Karabiik University Karabiik 078050 Turkey Department of Mechanical Engineering. Karabiik University Karabuk 078050 Turkey
出 版 物：《Advances in Manufacturing》 (先进制造进展（英文版）)
年 卷 期：2018年第6卷第4期
页 面：419-429页
核心收录：
主 题：High chrome cast iron Hard turning Grey relational analysis Multi-response optimization Taguchi method
摘 要：High chrome white cast iron is particularly preferred in the production of machine parts requiring high wear resistance. Although the amount of chrome in these materials provides high wear and corrosion resistances, it makes their machinability difficult. This study presents an application of the grey relational analysis based on the Taguchi method in order to optimize chrome ratio, cutting speed, feed rate, and cutting depth for the resultant cutting force (Fr) and surface roughness (Ra) when hard turning high chrome cast iron with a cubic boron nitride (CBN) insert. The effect levels of machining parameters on Fr and Ra were examined by an analysis of variance (ANOVA). A grey relational grade (GRG) was calculated to simultaneously minimize Fr and Ra. The ANOVA results based on GRG indicated that the feed rate, followed by the cutting depth, was the main parameter and contributed to respo ses. Optimal levels of parameters were found when the chrome ratio, cutting speed, feed rate, and cutting depth were 12%, 100m/min, 0.05mm/r, and 0.1mm, respectively, based on the multiresponse optimization results obtained by considering the maximum signal to noise (SIN) ratio of GRG. Confirmation results were verified by calculating the confidence level within the interval width.
维普期刊数据库