Monitoring and optimization of machining process when turning of AISI316L based on response surface methodology, artificial neural network and desirability function.

doi:10.21203/rs.3.rs-2463873/v1

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Research Article

Monitoring and optimization of machining process when turning of AISI316L based on response surface methodology, artificial neural network and desirability function.

https://doi.org/10.21203/rs.3.rs-2463873/v1

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The object of this research is to investigate the effect of cutting parameters such as cutting speed (Vc), feed rate (f) and depth of cut (ap) on machining parameters including cutting temperature (T_C) and tool flank wear (V_B) during dry turning of AISI 316L using coated carbide tool. The experiments were conducted according to Taguchi L27 orthogonal array, RSM and ANN have been used. Results revealed that (ap) found to be the dominant factor for T_C. V_B mainly influenced by Vc, f and ap, respectively. The prediction results obtained by ANN and RSM models showed a good agreement with experimental data. However, ANN models proved their capability to provide more accurate results compared to RSM models. According to the optimization analysis, Desirability function showed good accuracy in optimization.

ANN

ANOVA

Desirability Function

Dry Turning

Cutting Temperature

Tool Wear

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Editorial decision: Major Revisions Needed
14 Feb, 2023
Reviewers agreed at journal
13 Jan, 2023
Reviewers invited by journal
13 Jan, 2023
Editor assigned by journal
13 Jan, 2023
First submitted to journal
10 Jan, 2023

You are reading this latest preprint version

Monitoring and optimization of machining process when turning of AISI316L based on response surface methodology, artificial neural network and desirability function.

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