Optimization of the Cryogenic Treatment Process for En 52 Valve Steel Using the Grey-Taguchi Method

摘要

This study investigated the optimization of the deep cryogenic treatment for En 52 valve steel using the Taguchi method in combination with the Grey relational analysis. The factors considered for the optimization are the cooling rate, soaking temperature, soaking period, and tempering temperature, each at three different levels. The mechanical properties such as the tensile strength, hardness, and wear resistance were selected as the quality targets. Nine experimental runs based on L ₉ (34) orthogonal array of the Taguchi method were performed. An optimal parameter combination of the deep cryogenic treatment was obtained via the Grey relational analysis. The analysis of variance is applied to identify the most influential factor and it is found that the soaking period is the most influential factor for the deep cryogenic treatment of En 52 valve steel. A confirmation experiment is performed to verify the optimal result. The results of the confirmation tests prove that the tensile strength, hardness, and wear resistance of the deep cryotreated En 52 valve steel samples improved simultaneously through the optimal combination of the deep cryogenic treatment parameters obtained from the proposed method. The improvement in the tensile strength, hardness, and wear resistance of the deep cryotreated samples at the optimized treatment condition on the samples without deep cryogenic treatment is 7.84, 11.16, and 46.51%, respectively. Through the deep cryogenic treatment the wear resistance of the En 52 valve steel has improved more compared to the other responses.View full textDownload full textKeywordsANOVA, Cryogenic treatment, Grey relational analysis, Grey-Taguchi, Optimization, Valve steelRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/10426910903536766