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Role of trochoidal machining process parameter and chip morphology studies during end milling of AISI D3 steel

  • J. SanthakumarEmail author
  • U. Mohammed Iqbal
Article
  • 16 Downloads

Abstract

This study aims at discovering the effect of the trochoidal loop spacing parameter on Surface Roughness (SR), Specific Cutting Energy (SCE) and Temperature (T) during flat end milling operations. Twenty experimental runs were conducted based on the face centered central composite design (CCD) of response surface methodology (RSM). Artificial Neural Network (ANN) prediction modelling was created using four learning algorithms such as Batch Back Propagation Algorithm (BBPA), Quick Propagation Algorithm (QPA), Incremental Back Propagation Algorithm (IBPA) and Legvenberg–Marquardt back propagation Algorithm (LMBPA). The results were compared based on the value of Root mean square (RMSE) obtained for each learning algorithm and it was identified that LMBPA model produced least RMSE value. The predictive LMBPA neural network model was found to be capable of better predictions of surface roughness, temperature and specific cutting energy within the trained range. The Genetic algorithm(GA) gives the optimum parameters for conformation test and they are cutting speed of 41 m/min, feed rate of 136 mm/min and trochoidal loop spacing of 1.3 mm and error percentage between experimental and GA predicted values is 3.60% for surface roughness, 3.15% for specific cutting energy and 3.89% for temperature was found to be minimal. Scratches and serrated boundaries at both side of the chips were observed and laces, chip adhesion and side flow marks were found on machined surface.

Keywords

End milling Trochoidal loop spacing Response surface methodology Artificial Neural Network Specific cutting energy Temperature Surface roughness Genetic algorithm 

Abbreviations

AISI

American Iron & Steel Institute

HRC

Hardness measured with the Rockwell test for hard materials

RSM

Response surface methodology

BUE

Built up edge

CCD

Central composite design

MRR

Material removal rate

VMS

Vision measuring system

SR

Surface roughness

SCE

Specific cutting energy

T

Temperature

BBPA

Batch back propagation algorithm

QPA

Quick propagation algorithm

IBPA

Incremental back propagation algorithm

LMBPA

Legvenberg–Marquardt back propagation algorithm

GA

Genetic algorithm

RMSE

Root mean square error

ANN

Artificial neural network

List of symbols

Cs

Cutting speed

fz

Feed rate

Ls

Loop spacing

Fx

Normal force

Fy

Feed force

Fz

Axial cutting forces

ap

Depth of cut

xi

Input to node j

yi

Total input to node j in hidden

wij

Weight representing the strength of the connection between the ith node and jth node

bj

Bias associated with node j

Notes

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurIndia

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