{"title":"提高挤压铸造复合铝基(lm24 - sicp -椰壳灰)复合材料的耐磨性","authors":"M. Arulraj, P. Palani, M. Sowrirajan","doi":"10.1504/IJCMSSE.2019.10022981","DOIUrl":null,"url":null,"abstract":"This experimental study focuses on processing of hybrid aluminium matrix (LM24-SiCp-coconut shell ash) composite for making castings through squeeze casting process. The primary objective was to analyse the influence of the process parameters namely reinforcement percentage, pouring temperature, squeeze pressure and mould temperature on wear resistance. Samples were cast for each experimental condition based on L9 (34) orthogonal array. Pin-on-disc apparatus was used to measure the wear rate. From analysis of variance (ANOVA), it was observed that reinforcement percentage and squeeze pressure were the process parameters making a noticeable improvement in wear resistance. A mathematical model representing the process was developed using nonlinear regression analysis. The optimum casting conditions were obtained through Taguchi method and genetic algorithm tool and the conditions were validated through the confirmation experiments. The results show that parametric conditions obtained through the optimisation tools exhibit about 20% improvement in wear resistance compared to the base alloy.","PeriodicalId":39426,"journal":{"name":"International Journal of Computational Materials Science and Surface Engineering","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Enhancing wear resistance of squeeze cast hybrid aluminium matrix (LM24-SiCp-coconut shell ash) composite\",\"authors\":\"M. Arulraj, P. Palani, M. Sowrirajan\",\"doi\":\"10.1504/IJCMSSE.2019.10022981\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This experimental study focuses on processing of hybrid aluminium matrix (LM24-SiCp-coconut shell ash) composite for making castings through squeeze casting process. The primary objective was to analyse the influence of the process parameters namely reinforcement percentage, pouring temperature, squeeze pressure and mould temperature on wear resistance. Samples were cast for each experimental condition based on L9 (34) orthogonal array. Pin-on-disc apparatus was used to measure the wear rate. From analysis of variance (ANOVA), it was observed that reinforcement percentage and squeeze pressure were the process parameters making a noticeable improvement in wear resistance. A mathematical model representing the process was developed using nonlinear regression analysis. The optimum casting conditions were obtained through Taguchi method and genetic algorithm tool and the conditions were validated through the confirmation experiments. The results show that parametric conditions obtained through the optimisation tools exhibit about 20% improvement in wear resistance compared to the base alloy.\",\"PeriodicalId\":39426,\"journal\":{\"name\":\"International Journal of Computational Materials Science and Surface Engineering\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Computational Materials Science and Surface Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/IJCMSSE.2019.10022981\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computational Materials Science and Surface Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJCMSSE.2019.10022981","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
This experimental study focuses on processing of hybrid aluminium matrix (LM24-SiCp-coconut shell ash) composite for making castings through squeeze casting process. The primary objective was to analyse the influence of the process parameters namely reinforcement percentage, pouring temperature, squeeze pressure and mould temperature on wear resistance. Samples were cast for each experimental condition based on L9 (34) orthogonal array. Pin-on-disc apparatus was used to measure the wear rate. From analysis of variance (ANOVA), it was observed that reinforcement percentage and squeeze pressure were the process parameters making a noticeable improvement in wear resistance. A mathematical model representing the process was developed using nonlinear regression analysis. The optimum casting conditions were obtained through Taguchi method and genetic algorithm tool and the conditions were validated through the confirmation experiments. The results show that parametric conditions obtained through the optimisation tools exhibit about 20% improvement in wear resistance compared to the base alloy.
期刊介绍:
IJCMSSE is a refereed international journal that aims to provide a blend of theoretical and applied study of computational materials science and surface engineering. The scope of IJCMSSE original scientific papers that describe computer methods of modelling, simulation, and prediction for designing materials and structures at all length scales. The Editors-in-Chief of IJCMSSE encourage the submission of fundamental and interdisciplinary contributions on materials science and engineering, surface engineering and computational methods of modelling, simulation, and prediction. Papers published in IJCMSSE involve the solution of current problems, in which it is necessary to apply computational materials science and surface engineering methods for solving relevant engineering problems.