Applied Genetic Programming for Predicting Specific Cutting Energy for Cutting Natural Stones

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@Article{journals/aai/AticiE17,

• author = "Umit Atici and Adem Ersoy",
• title = "Applied Genetic Programming for Predicting Specific Cutting Energy for Cutting Natural Stones",
• journal = "Applied Artificial Intelligence",
• year = "2017",
• number = "5-6",
• volume = "31",
• pages = "439--452",
• keywords = "genetic algorithms, genetic programming, gene expression programming",
• bibdate = "2017-12-28",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/aai/aai31.html#AticiE17",
• DOI = "doi:10.1080/08839514.2017.1378140",
• abstract = "n the processing of marbles and other natural stones, the major cost involved in sawing with circular diamond sawblades is the energy cost. This paper reports a new and efficient approach to the formulation of SEcut using gene expression programming (GEP) based on not only rock characteristics but also design and operating parameters. Twenty-three rock types classified into four groups were cut using three types of circular diamond saws at different feed rates, depths of cut, and peripheral speeds. The input parameters used to develop the GEP-based SEcut prediction model were as follows: physico-mechanical rock characteristics (uniaxial compressive strength, Shore scleroscope hardness, Schmidt rebound hardness, and Bohme surface abrasion), operating parameters (feed rate, depth of cut, and peripheral speed), and a design variable (diamond concentration in the sawblade). The performance of the model was comprehensively evaluated on the basis of statistical criteria such as R2 (0.95).",

}

Genetic Programming entries for Umit Atici Adem Ersoy

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