Tool Wear Prediction When Machining with Self-Propelled Rotary Tools

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@Article{umer:2022:Materials,

• author = "Usama Umer and Syed Hammad Mian and Muneer Khan Mohammed and Mustufa Haider Abidi and Khaja Moiduddin and Hossam Kishawy",
• title = "Tool Wear Prediction When Machining with Self-Propelled Rotary Tools",
• journal = "Materials",
• year = "2022",
• volume = "15",
• number = "12",
• pages = "Article No. 4059",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1996-1944",
• URL = "https://www.mdpi.com/1996-1944/15/12/4059",
• DOI = "doi:10.3390/ma15124059",
• abstract = "The performance of a self-propelled rotary carbide tool when cutting hardened steel is evaluated in this study. Although various models for evaluating tool wear in traditional (fixed) tools have been introduced and deployed, there have been no efforts in the existing literature to predict the progression of tool wear while employing self-propelled rotary tools. The work-tool geometric relationship and the empirical function are used to build a flank wear model for self-propelled rotary cutting tools. Cutting experiments are conducted on AISI 4340 steel, which has a hardness of 54–56 HRC, at various cutting speeds and feeds. The rate of tool wear is measured at various intervals of time. The constant in the proposed model is obtained using genetic programming. When experimental and predicted flank wear are examined, the established model is found to be competent in estimating the rate of rotary tool flank wear progression.",
• notes = "also known as \cite{ma15124059}",

}

Genetic Programming entries for Usama Umer Syed Hammad Mian Muneer Khan Mohammed Mustufa Haider Abidi Khaja Moiduddin Hossam A Kishawy

Citations