Optimization of cutting conditions during cutting by using neural networks

Župerl, Uroš; Čuš, F.

doi:10.1016/s0736-5845(02)00079-0

Cited by 132 publications

(81 citation statements)

References 11 publications

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“…Figure 1. Flowchart of surface roughness prediction of ANFIS system [13] For this model, main parameters for the experiments are discharge current I e , pulse duration t i (input data set) and surface roughness R a (output data set) (Figure 2). The training dataset and testing dataset are obtained from experiments.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Predicting of Machining Quality in Electric Discharge Machining using Intelligent Optimization Techniques

Rodić

Gostimirović

Kováč

et al. 2014

IJMECH

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Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Predicting of Machining Quality in Electric Discharge Machining using Intelligent Optimization Techniques

Rodić

Gostimirović

Kováč

et al. 2014

IJMECH

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“…More complex models have also been applied for surface roughness and tool wear modeling to optimise off-line cutting parameters. Zuperl (Zuperl & Cus, 2003) also applied and compared feedforward and radial basis neural networks for learning a multi-objective function similar to the one presented in (Cus & Balic, 2003). Choosing the radial basis networks due to their fast learning ability and reliability, he applied a large-scale optimization algorithm to obtain the optimal cutting parameters.…”

Section: Wwwintechopencom Adaptive Control Optimization Of Cutting mentioning

confidence: 99%

Adaptive Control Optimization of Cutting Parameters for High Quality Machining Operations Based on Neural Networks and Search Algorithms

Abellán

Romero

Siller

et al. 2008

Advances in Robotics, Automation and Control

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“…The investigation reveals the possibility of carbide insert coated with several layers in finish turning of hardened material even at higher cutting speeds. Zuperl and Cus [21] suggested a neural network technique for multiple objective optimization of machining variables which ensures simple, fast and efficient selection of optimum cutting parameters.…”

Section: Introductionmentioning

confidence: 99%

Investigating Machinability in Hard Turning of AISI 52100 Bearing Steel Through Performance Measurement: QR, ANN and GRA Study

Panda¹,

Sahoo²,

Panigrahi³

et al. 2018

INT. J. AUTOMOT. MECH. ENG.

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The existing endeavor investigates on machinability characteristics through performance measurement of flank wear, surface quality and chip morphology during finish turning of AISI 52100 bearing steel (55 ± 1 HRC) under dry environment employing carbide insert coated along with various layers (TiN/TiCN/Al2O3). Secondly the influence of machining variables viz. cutting speed, feed rate and depth of cut on responses are assessed by ANOVA and modeled through quadratic regression and artificial neural network. Multiparametric optimization of cutting conditions has been obtained through Taguchi based grey relational analysis. Finally, tool life at ideal conditions has been evaluated through experiment. Based on the study, it is disclosed that coated carbide with multilayer insert outperformed during hard machining as wear at the flank surface and surface quality are within the benchmark cap of 0.3 mm and 1.6 microns respectively. From the chip morphology analysis, multilayer coated carbide insert generates lower temperature and maintains cutting edge sharpness and delays the growth of tool wear. ANN model using multilayered feed forward network yields accurate prediction of responses with minimum error percentage compared to QR model. The optimal parametric combination through GRA approach is found to be d1 (0.1 mm)-f1 (0.04 mm/rev)-v2 (110 m/min) and is greatly improved. Feed is the compelling aspect for multi-responses pursued by cutting speed. The tool life at optimized cutting condition is found to be approximately 19 minutes.

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Optimization of cutting conditions during cutting by using neural networks

Cited by 132 publications

References 11 publications

Predicting of Machining Quality in Electric Discharge Machining using Intelligent Optimization Techniques

Predicting of Machining Quality in Electric Discharge Machining using Intelligent Optimization Techniques

Adaptive Control Optimization of Cutting Parameters for High Quality Machining Operations Based on Neural Networks and Search Algorithms

Investigating Machinability in Hard Turning of AISI 52100 Bearing Steel Through Performance Measurement: QR, ANN and GRA Study

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