Comparing Predictability of Genetic Programming and ANFIS on Drilling Performance Modeling for GFRP Composites

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Abhishek:2014:PMS,

• author = "Kumar Abhishek and Biranchi Narayan Panda and Saurav Datta and Siba Sankar Mahapatra",
• title = "Comparing Predictability of Genetic Programming and {ANFIS} on Drilling Performance Modeling for {GFRP} Composites",
• journal = "Procedia Materials Science",
• volume = "6",
• pages = "544--550",
• year = "2014",
• note = "3rd International Conference on Materials Processing and Characterisation (ICMPC 2014)",
• ISSN = "2211-8128",
• DOI = "doi:10.1016/j.mspro.2014.07.069",
• URL = "http://www.sciencedirect.com/science/article/pii/S2211812814004349",
• abstract = "Drilling of glass fibre reinforced polymer (GFRP) composite material is substantially complicated from the metallic materials due to its high structural stiffness (of the composite) and low thermal conductivity of plastics. During drilling of GFRP composites, problems generally arise like fibre pull out, delamination, stress concentration, swelling, burr, splintering and micro cracking etc. which reduces overall machining performance. Now-a-days hybrid approaches have been received remarkable attention in order to model machining process behaviour and to optimise machining performance towards subsequent improvement of both quality and productivity, simultaneously. In the present research, spindle speed, feed rate, plate thickness and drill bit diameter have been considered as input parameters; and the machining yield characteristics have been considered in terms of thrust and surface roughness (output responses) of the drilled composite product. The study illustrates the applicability of genetic programming with the help of GPTIPS as well as Adaptive Neuro Fuzzy Inference System (ANFIS) towards generating prediction models for better understanding of the process behavior and for improving process performances in drilling of GFRP composites.",
• keywords = "genetic algorithms, genetic programming, Glass fibre reinforced polymer (GFRP), Adaptive Neuro Fuzzy Inference System (ANFIS), GPTIPS.",
• notes = "PhD thesis (2015) http://ethesis.nitrkl.ac.in/6916/ Experimental Investigations on Machining of CFRP Composites: Study of Parametric Influence and Machining Performance Optimization. PhD thesis. does not seem to be on GP",

}

Genetic Programming entries for Kumar Abhishek Biranchi Narayan Panda Saurav Datta Siba Sankar Mahapatra

Citations