Comparative study on lubricating and cooling conditions in the drilling process of electrolytic copper

Rubio, Juan Carlos Campos; Rezende, Bruna Aparecida; Vieira, Luciano Machado Gomes; Romero, Hector Muñoz; Brenes, Luis Arturo Rapso

doi:10.1007/s00170-018-3139-7

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…Minimization of burr height and thickness using the response surface method (RSM)-based model was developed from experimental data by using different drilling parameters like cutting speed, feed rate, drill dia., point angle and clearance angle. Finally, burr minimization was performed by genetic algorithm (GA) of the RSM model by Kundu et al 11 and Gaitonde et al 12 Experimental study was also conducted to minimize burr height by changing cooling conditions, coating conditions and by changing drill geometry and pilot holes by Rubio et al [13][14] and Rezende et al 15 Lekkala et al 16 carried out Taguchi-based full factorial design (FFD) experiment to minimize the burr height with backup support in aluminum alloy. It was observed that small burr was formed at low-to-moderate cutting speed and low feed with water as cutting fluid.…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm-based drilling burr minimization using adaptive neuro-fuzzy inference system and support vector regression

Mondal

Mandal

Dey

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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Burrs are undesirable materials beyond the work piece surface during drilling or other machining processes, thus this should be as less as possible during manufacturing. The experimental study has been conducted according to the full factorial design method. A total of 27 experiments were conducted by drilling on an Aluminum 6061T6 plate by choosing three factors and three levels of process parameters like drill diameter, point angle and spindle speed. In this research article, two predictive models, namely, adaptive neuro-fuzzy inference system and support vector regression, are developed using experimental data to estimate burr height and burr thickness. Then, these predictive models have been used to find out optimum process parameters for minimum burr height and burr thickness using genetic algorithm. It has been found that both the models are able to predict burr size and thickness with good accuracy, while the adaptive neuro-fuzzy inference system performs better than support vector regression.

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