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Multi-objective Optimization in Drilling Kevlar Fiber Reinforced Polymer Using Grey Fuzzy Analysis and Backpropagation Neural Network–Genetic Algorithm (BPNN–GA) Approaches

  • Bobby O. P. SoepangkatEmail author
  • Bambang Pramujati
  • Mohammad Khoirul Effendi
  • Rachmadi Norcahyo
  • A. M. Mufarrih
Regular Paper
  • 32 Downloads

Abstract

An integrated approach has been applied to predict and optimize multi-performance characteristics, such as optimum thrust force (Fz), torque (Mz), hole surface roughness (Ra), delamination (D) and hole roundness (R), in drilling process of Kevlar fiber reinforced polymer. The experiments were performed by varying drill point geometry and drilling process parameters, i.e., drill point angle, feed rate, and spindle speed. The quality characteristics Fz, Mz, Ra, D, and R were the smaller the better. Taguchi orthogonal array (OA) L18 was used as the design of experiments. Grey fuzzy analysis was first applied to obtain a rough estimation of the optimum drill point geometry and drilling process parameters. Backpropagation neural network (BPNN) model was developed and utilized to predict the optimum Fz, Mz, Ra, D, and R. Genetic algorithm (GA) was performed to search for global optimum of drilling process parameters combinations. The analysis of the effect of drill point angle, as well as drilling process parameters, on the individual performance characteristics was conducted by examining both the percentage contribution of drill point geometry and drilling process parameters on the total variance of three responses individually, and the response graphs. The results of the confirmation experiment showed that the BPNN based GA optimization method could accurately predict and also significantly improve the multiple performance characteristics.

Keywords

BPNN–GA Drilling process KFRP Multi performance optimization Grey fuzzy analysis 

List of Symbols

\( y_{i} \)

Measured characteristic value of the response

\( X_{i}^{*} \left( k \right) \)

Normalization value of the \( k \) response

\( \hbox{min} \;X_{i} \left( k \right) \)

The smallest value of \( X_{i} \left( k \right) \) for the kth response

\( {\text{max}}\;X_{i} \left( k \right) \)

The largest value of \( X_{i} \left( k \right) \) for the kth response

\( \zeta \)

Distinguishing coefficient

\( \xi_{i} \left( k \right) \)

GRG value of the kth response

\( \Delta_{0,i} \left( k \right) \)

Deviation sequence of reference for the kth response

\( \Delta_{min} \)

Smallest value of \( \Delta_{0,i} \)

\( \Delta_{max} \)

Largest value of \( \Delta_{0,i} \)

Notes

Acknowledgements

The authors acknowledge the PNBP Grant Provided by Mechanical Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.

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Copyright information

© Korean Society for Precision Engineering 2019

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentInstitut Teknologi Sepuluh NopemberSurabayaIndonesia
  2. 2.Mechanical Engineering DepartmentUniversitas Nusantara PGRIKediriIndonesia

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