{"title":"Optimization of Machining Variables of Inconel 800 Alloy in CNC Face Milling Using TiAlN and TiAlN-TiN Coated Inserts","authors":"Vinod Kumar, N. Bala","doi":"10.4028/p-70w6sn","DOIUrl":null,"url":null,"abstract":"This research work is based on the machinability of an Inconel 800 alloy using TiAlN-coating and TiAlN-TiN-coating tools. In the CNC VMC Face milling process feed, depth of cut (DoC), and cutting speed consider input variables and surface roughness, Tool wear is measured for all machining conditions. To enhance the machining conditions a Taguchi L9 Design of Experiment was created. ANOVA analysis was used to identify the important variables influencing Flank wear (tool wear) and surface roughness. The signal-to-noise ratio for the ideal cutting combination was identified by evaluating the optimum surface roughness and tool wear. for the effect of coating, a comparison was done between the findings obtained using both TiAlN-coated and TiAlN-TiN tungsten carbide-coated tools. The best optimum surface roughness and tool wear of the experiment conducted under machining with TiAlN-TiN coated carbide tool resulted in .3433 µm and 128 µm respectively.","PeriodicalId":7271,"journal":{"name":"Advanced Materials Research","volume":"101 1","pages":"139 - 150"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Materials Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4028/p-70w6sn","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This research work is based on the machinability of an Inconel 800 alloy using TiAlN-coating and TiAlN-TiN-coating tools. In the CNC VMC Face milling process feed, depth of cut (DoC), and cutting speed consider input variables and surface roughness, Tool wear is measured for all machining conditions. To enhance the machining conditions a Taguchi L9 Design of Experiment was created. ANOVA analysis was used to identify the important variables influencing Flank wear (tool wear) and surface roughness. The signal-to-noise ratio for the ideal cutting combination was identified by evaluating the optimum surface roughness and tool wear. for the effect of coating, a comparison was done between the findings obtained using both TiAlN-coated and TiAlN-TiN tungsten carbide-coated tools. The best optimum surface roughness and tool wear of the experiment conducted under machining with TiAlN-TiN coated carbide tool resulted in .3433 µm and 128 µm respectively.