{"title":"基于计算机辅助制造系统实验设计的“按区域粗加工”的刀具路径策略决策","authors":"G. Vosniakos, Asimina Gkortza, N. Kontolatis","doi":"10.1504/IJMR.2016.076985","DOIUrl":null,"url":null,"abstract":"This work streamlines assignment of roughing strategies for complex parts from those that are available on a computer-aided manufacturing (CAM) system. A Pelton hydro-turbine bucket is used as an example, divided into independent regions. For each region the applicable strategies, toolpath types, as well as their individual parameters are examined at a small enough number of discrete levels to ensure practical feasibility. The responses, i.e. machining time and rest material, are combined into a single weighting function. The absolute minimum number of possible scenarios is guaranteed by Taguchi design and the optimum factor levels are determined. Analysis of variance reveals the significance of each factor but also possible omissions of factors and/or interactions that may occur. The approach saves time, is open to rectification by simply adding the missing experiments and is equally applicable to any mechanical component to be machined. [Received 30 September 2015; Revised 17 February 2016; Accepted 23 February 2016]","PeriodicalId":154059,"journal":{"name":"Int. J. Manuf. Res.","volume":"121 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Toolpath strategy decisions in 'rough machining-by-region' using design of experiments on computer-aided manufacturing systems\",\"authors\":\"G. Vosniakos, Asimina Gkortza, N. Kontolatis\",\"doi\":\"10.1504/IJMR.2016.076985\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work streamlines assignment of roughing strategies for complex parts from those that are available on a computer-aided manufacturing (CAM) system. A Pelton hydro-turbine bucket is used as an example, divided into independent regions. For each region the applicable strategies, toolpath types, as well as their individual parameters are examined at a small enough number of discrete levels to ensure practical feasibility. The responses, i.e. machining time and rest material, are combined into a single weighting function. The absolute minimum number of possible scenarios is guaranteed by Taguchi design and the optimum factor levels are determined. Analysis of variance reveals the significance of each factor but also possible omissions of factors and/or interactions that may occur. The approach saves time, is open to rectification by simply adding the missing experiments and is equally applicable to any mechanical component to be machined. [Received 30 September 2015; Revised 17 February 2016; Accepted 23 February 2016]\",\"PeriodicalId\":154059,\"journal\":{\"name\":\"Int. J. Manuf. Res.\",\"volume\":\"121 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Manuf. Res.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/IJMR.2016.076985\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Manuf. Res.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJMR.2016.076985","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Toolpath strategy decisions in 'rough machining-by-region' using design of experiments on computer-aided manufacturing systems
This work streamlines assignment of roughing strategies for complex parts from those that are available on a computer-aided manufacturing (CAM) system. A Pelton hydro-turbine bucket is used as an example, divided into independent regions. For each region the applicable strategies, toolpath types, as well as their individual parameters are examined at a small enough number of discrete levels to ensure practical feasibility. The responses, i.e. machining time and rest material, are combined into a single weighting function. The absolute minimum number of possible scenarios is guaranteed by Taguchi design and the optimum factor levels are determined. Analysis of variance reveals the significance of each factor but also possible omissions of factors and/or interactions that may occur. The approach saves time, is open to rectification by simply adding the missing experiments and is equally applicable to any mechanical component to be machined. [Received 30 September 2015; Revised 17 February 2016; Accepted 23 February 2016]