Neural network (NN) based modelling and Multi-objective Swarm Algorithm (MSA) optimization of CNC milling operation

CNC milling is advanced machine tool which is most demandable due to its accuracy and precision level. In this experimental research Helix angle (HA), Axial depth of cut (AD), Radial depth of cut (RD), and cutting speed (CS) are taken as input parameter and Material Removal Rate (MRR), Tool Wear Rat...

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Bibliographic Details
Published in:Materials today : proceedings 2023-03
Main Authors: Hameed, Azzam S., Mallick, Bijan, Mondal, Krishnendu, Hikmat, S.K.
Format: Article
Language:English
Subjects:
ABS
ANN
CNC Milling
CSO
MRR
PSO
TWR
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Summary:CNC milling is advanced machine tool which is most demandable due to its accuracy and precision level. In this experimental research Helix angle (HA), Axial depth of cut (AD), Radial depth of cut (RD), and cutting speed (CS) are taken as input parameter and Material Removal Rate (MRR), Tool Wear Rate (TWR), and Surface Roughness (SR) are considered as a response variables. Overall research performed into parts, in first part ANN model used to represent the nonlinear relationship between each of the response output variables with input variables. In last phase five multi-objective Swarm Intelligence (SI) optimization techniques: Cuckoo search Optimization (CSO), Particle Swarm Optimization (PSO), and Ant Bee Colony Optimization (ABC), Bat Algorithm (BA) & Simulating Annealing (SA) has been examined in order to have a better understanding of how they solve many objective challenges in order to get the best CNC milling results. A confirmatory test conducts for the validation of the best technique.
ISSN:2214-7853
2214-7853
DOI:10.1016/j.matpr.2023.03.076