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ANN-FPA Based Modelling and Optimization of Drilling Burrs Using RSM and GA

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Advances in Manufacturing Processes, Intelligent Methods and Systems in Production Engineering (GCMM 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 335))

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Abstract

In this work, drilling experiments are performed on an aluminum alloy following the L27 orthogonal array of response surface methodology (RSM) to find out suitable drilling parameters to minimize burr height and thickness. Drilling is done using backup support. Two types of predictive models namely, artificial neural networks (ANN), and a combination of ANN with flower pollination algorithm (ANN-FPA) are constructed using the experimental data to predict burr height and burr thickness. The developed ANN-FPA predictive model is found to be more accurate than the only ANN model. Mathematical model of burr height and burr thickness are developed using this experimental input data and ANN-FPA based output data and it is optimized using Genetic Algorithm (GA) and RSM desirability function optimization techniques. Burr height observed is found minimum at a cutting velocity of 10 m/min, feed of 0.08 mm/rev, and depth of hole within backup support of 0.2 mm, whereas burr thickness is the lowest in this work at a cutting velocity of 3.9 m/min, a feed rate of 0.095 mm/rev and depth of hole inside backup support of 0.56 mm. The validation tests are performed at these optimized process conditions and the prediction error is found to be 6.53% using GA and 6.68% using RSM desirability function for measurement of burr height, and 4.70% using GA a well as RSM desirability function in burr thickness measurement. Finally, developed multi-objective functions of burr height and thickness are optimized through GA. These results are useful to implement in manufacturing practice.

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Acknowledgments

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. There is no conflict of interest related to this work.

Author information

Authors and Affiliations

  1. Mechanical Engineering Department, Jalpaiguri Government Engineering College, Jalpaiguri, 735102, India

    Nripen Mondal & Madhab Chandra Mandal

  2. Electronics & Communication Engineering Department, Jalpaiguri Government Engineering College, Jalpaiguri, 735102, India

    Sudip Mandal

  3. Mechanical Engineering Department, Kalyani Government Engineering College, Kalyani, 741235, India

    Santanu Das

  4. Mechanical & Industrial Engineering Department, College of Engineering, Al Imam Mohammad Ibn Saud Islamic University, Riyadh, 11432, Saudi Arabia

    Barun Haldar

Authors
  1. Nripen Mondal
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  2. Sudip Mandal
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  4. Santanu Das
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  5. Barun Haldar
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Corresponding author

Correspondence to Santanu Das .

Editor information

Editors and Affiliations

  1. Liverpool John Moores University, Liverpool, UK

    Andre Batako

  2. Wroclaw University of Science and Technology, Wrocław, Poland

    Anna Burduk

  3. School of Civil Engineering and Built Environment, Liverpool John Moores University, Liverpool, UK

    Kanisius Karyono

  4. General Engineering Research Institute, Liverpool John Moores University, Liverpool, UK

    Xun Chen

  5. Faculty of Mechanical Engineering, Silesian University of Technology, Gliwice, Poland

    Ryszard Wyczółkowski

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Mondal, N., Mandal, S., Mandal, M.C., Das, S., Haldar, B. (2022). ANN-FPA Based Modelling and Optimization of Drilling Burrs Using RSM and GA. In: Batako, A., Burduk, A., Karyono, K., Chen, X., Wyczółkowski, R. (eds) Advances in Manufacturing Processes, Intelligent Methods and Systems in Production Engineering. GCMM 2021. Lecture Notes in Networks and Systems, vol 335. Springer, Cham. https://doi.org/10.1007/978-3-030-90532-3_15

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