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

  • J. SanthakumarEmail author
  • U. Mohammed Iqbal


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.


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



American Iron & Steel Institute


Hardness measured with the Rockwell test for hard materials


Response surface methodology


Built up edge


Central composite design


Material removal rate


Vision measuring system


Surface roughness


Specific cutting energy




Batch back propagation algorithm


Quick propagation algorithm


Incremental back propagation algorithm


Legvenberg–Marquardt back propagation algorithm


Genetic algorithm


Root mean square error


Artificial neural network

List of symbols


Cutting speed


Feed rate


Loop spacing


Normal force


Feed force


Axial cutting forces


Depth of cut


Input to node j


Total input to node j in hidden


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


Bias associated with node j



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© 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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