Metaheuristics in Manufacturing: Predictive Modeling of Tool Wear in Machining Using Genetic Programming

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@InCollection{Zadshakoyan:2018:aamc,

• author = "Mohammad Zadshakoyan and Vahid Pourmostaghimi",
• title = "Metaheuristics in Manufacturing: Predictive Modeling of Tool Wear in Machining Using Genetic Programming",
• booktitle = "Advancements in Applied Metaheuristic Computing",
• publisher = "IGI Global",
• year = "2018",
• editor = "Nilanjan Dey",
• chapter = "5",
• pages = "118--142",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "9781522541516",
• URL = "https://www.igi-global.com/chapter/metaheuristics-in-manufacturing/192002",
• DOI = "doi:10.4018/978-1-5225-4151-6.ch005",
• abstract = "The state of a cutting tool is an important factor in any metal cutting process as additional costs in terms of scrapped components, machine tool breakage and unscheduled downtime result from worn tool usage. Therefore, tool wear prediction plays an important role in industry automation for higher productivity and acceptable product quality. Therefore, in order to increase the productivity of turning process, various researches have been made recently for tool wear estimation and classification in turning process. Chip form is one of the most important factors commonly considered in evaluating the performance of machining process. On account of the effect of the progressive tool wear on the shape and geometrical features of produced chip, it is possible to predict some measurable machining outputs such as crater wear. According to experimentally performed researches, cutting speed and cutting time are two extremely effective parameters which contribute to the development of the crater wear on the tool rake face. As a result, these parameters will change the chip radius and geometry. This chapter presents the development of the genetic equation for the tool wear using occurred changes in chip radius in turning process. The development of the equation combines different methods and technologies like evolutionary methods, manufacturing technology, measuring and control technology with the adequate hardware and software support. The results obtained from genetic equation and experiments showed that obtained genetic equations are correlated well with the experimental data. Furthermore, it can be used for tool wear estimation during cutting process and because of its parametric form, genetic equation enables us to analyse the effect of input parameters on the crater wear parameters.",
• notes = "University of Tabriz, Iran https://www.igi-global.com/book/advancements-applied-metaheuristic-computing/182886",

}

Genetic Programming entries for Mohammad Zadshakoyan Vahid Pourmostaghimi

Citations