A finite element based data analytics approach for modeling turning process of Inconel 718 alloys

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@Article{Vijayaraghavan:2016:JCP,

• author = "V. Vijayaraghavan and A. Garg and Liang Gao and R. Vijayaraghavan and Guoxing Lu",
• title = "A finite element based data analytics approach for modeling turning process of Inconel 718 alloys",
• journal = "Journal of Cleaner Production",
• year = "2016",
• volume = "137",
• pages = "1619--1627",
• month = "20 " # nov,
• keywords = "genetic algorithms, genetic programming, Finite element analysis, Machining, Turning, Inconel 718",
• ISSN = "0959-6526",
• DOI = "doi:10.1016/j.jclepro.2016.04.010",
• URL = "http://www.sciencedirect.com/science/article/pii/S0959652616302694",
• abstract = "Turning is a primary metal cutting process deployed extensively for producing components to required shape and dimensions. A commonly used material is Inconel 718, which exhibits an inferior economic feasibility in terms of turning due to its poor machinability characteristics. A combined finite element based data analytics model is introduced in this work. Finite element modelling was used to predict the cutting force while Genetic Programming was used to obtain the mathematical relation between the process variables and the cutting force. The weighted parameter analysis was conducted on the mathematical model which revealed that depth of cut and cutting angle exerts significant influence on the cutting force. As turning process is generally specified by a given depth of cut which dictates the material removal rate, optimization of tool cutting angle can result in enhanced power savings. It is anticipated that the findings obtained from this study can result in greater power savings in turning process of hard-to-machine materials which can lead to a sustainable manufacturing process.",

}

Genetic Programming entries for Venkatesh Vijayaraghavan Akhil Garg Liang Gao R Vijayaraghavan Guoxing Lu

Citations