Modeling multiple-response environmental and manufacturing characteristics of EDM process

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Garg:2016:JCPa,

• author = "Akhil1 Garg and Jasmine Siu Lee Lam and L. Gao",
• title = "Modeling multiple-response environmental and manufacturing characteristics of {EDM} process",
• journal = "Journal of Cleaner Production",
• year = "2016",
• volume = "137",
• pages = "1588--1601",
• month = "20 " # nov,
• ISSN = "0959-6526",
• DOI = "doi:10.1016/j.jclepro.2016.04.070",
• URL = "http://www.sciencedirect.com/science/article/pii/S0959652616303389",
• abstract = "Among the machining operations, Electrical discharge machining (EDM) process is widely used in production industries because of its ability to machine the materials of any hardness. However, the machining of advanced materials including ceramics, composites, and super-alloys requiring the precise surface finish and dimensional accuracy also increases the energy consumption and cost simultaneously. As such, both environmental and economic performances are compromised. Also, EDM process is itself considered hazardous because of the large toxic liquid and solid wastes and gases produced due to reaction products developed from highly energized dielectric media placed between tool and workpiece. Thus, an appropriate balance between manufacturing and environmental aspects is highly desirable for ensuring higher productivity and environmental sustainability of the process. In this context, the present work proposes two variants of optimization approach of genetic programming (GP) in modelling the multi-response characteristics, i.e. two environmental aspects (thermal energy consumption and dielectric consumption) and one manufacturing aspect (relative tool to wear ratio) of the EDM process. These variants are proposed by introducing two model selection criteria from statistical learning theory to be used as fitness functions in the framework of GP. The performance of the proposed GP models is evaluated against the experimental data based on five statistical error metrics and the two hypothesis tests. Further, the relationships between manufacturing, environmental aspects and the input process parameters are unveiled, which can be used by industry users to optimize the process economically and environmentally. It was found that the input peak current has the highest impact on the environmental aspects of the EDM process.",
• keywords = "genetic algorithms, genetic programming, Electrical discharge machining (EDM), Machining, Environmental, Energy consumption, Relative tool to wear ratio",

}

Genetic Programming entries for Akhil Garg Jasmine Siu Lee Lam Liang Gao

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