On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

Yallese

Proceedings of the Institution of Mechanical Engineers, Part C:

et al. 2019

Self Cite

The purpose of this experimental work is to study the impact of the machining parameters ( Vc, ap, and f) on the surface roughness criteria ( Ra, Rz, and Rt) as well as on the cutting force components ( Fx, Fy, and Fz), during dry turning of martensitic stainless steel (AISI 420) treated at 59 hardness Rockwell cone. The machining tests were carried out using the coated mixed ceramic cutting-insert (CC6050) according to the Taguchi design (L25). Analysis of the variance (ANOVA) as well as Pareto graphs made it possible to quantify the contributions of ( Vc, ap, and f) on the output parameters. The response surface methodology and the artificial neural networks approach were used for output modeling. Finally, the optimization of the machining parameters was performed using desirability function (DF) minimizing the surface roughness and the cutting forces simultaneously. The results indicated that the roughness is strongly affected by the feed rate ( f) with contributions of (80.71%, 80.26%, and 81.80%) for ( Ra, Rz, and Rt) respectively, and that the depth of cut ( ap) is the factor having the major influence on the cutting forces ( Fx = 53.76%, Fy = 50.79%, and Fz = 65.31%). Furthermore, artificial neural network and response surface methodology models correlate very well with experimental data. However, artificial neural network models show better accuracy. The optimum machining setting for multi-objective optimization is Vc = 80 m/min, f = 0.08 mm/rev and ap = 0.141 mm.

“…On the other hand, in the family of artificial intelligence methods, which they enrich by making decisions based more on perception than on formal logical reasoning. 39–43…”

Section: Resultsmentioning

confidence: 99%

Prediction of machining performance using RSM and ANN models in hard turning of martensitic stainless steel AISI 420

Yallese

Proceedings of the Institution of Mechanical Engineers, Part C:

et al. 2019

Self Cite

“…Expressed in the secondary equation to use RSM, the data obtained by the test is quasi-proposed to obtain a secondary model. Each of the response coefficient analysis through ANOVA, which can be discriminate through model fitting compare to the experimental data, and its significance with the factor [37].…”

Section: Response Surface Methodologymentioning

confidence: 99%

Modeling and optimization of cutting forces and effect of turning parameters on SiCp/Al 45% vs SiCp/Al 50% metal matrix composites: a comparative study

Laghari

2021

SN Appl. Sci.

In this study, the proposed experimental and second-order model for the cutting forces were developed through several parameters, including cutting speed, feed rate, depth of cut, and two varying content of SiCp. Cutting force model was developed and optimized through RSM and compared for two different percentages of components SiCp/Al 45% and SiCp/Al 50%. ANOVA is used for Quantitative evaluation, the main effects plot along with the evaluation using different graphs and plots including residual analysis, contour plots, and desirability functions for cutting forces optimization. It provides the finding for choosing proper parameters for the machining process. The plots show that during increment with depth of cut in proportion with feed rate are able to cause increments in cutting forces. Higher cutting speed shows a positive response in both the weight percentage of SiCp by reducing the cutting force because of higher cutting speed increases. A very fractional increasing trend of cutting force was observed with increasing SiCp weight percentages. Both of the methods such as experiment and model-predicted results of SiCp/Al MMC materials were thoroughly evaluated for analyzing cutting forces of SiCp/Al 45%, and SiCp/Al 50%, as well as calculated the error percentages also found in an acceptable range with minimal error percentages. Article Highlights This study focuses on the effect of cutting parameters as well as different percentage of SiC particles on the cutting forces, while comparing the results of both SiC particles such as SiCp/Al 45%, and SiCp/Al 50% the result shows that there isn’t fractional amount of impact on the cutting force with nominal increasing percentages of SiC particles. Cutting speed in machining process of SiCp/Al shows positive response in reducing the cutting forces, however, increasing amount of depth of cut followed by increasing feed rate creates fluctuations in cutting force and thus increases the cutting force in the cutting process. The developed RSM mathematical model which is based on the box Behnken design show excellent competence for predicting and suggesting the machining parameters for both SiCp/Al 45%, and SiCp/Al 50% and the RSM mathematical model is feasible for optimization of the machining process with good agreement to experimental values.

Advances in Materials Science and Engineering

“…e adequacy measures are very close to 1, which is appropriate and indicates the adequate model. "Adeq Precision" measures the signal-tonoise ratio, and the value greater than 4 is considered to be desirable [20].…”

Section: Effect Of Parameters On Ferrite Contentmentioning

confidence: 99%

Optimization of FCAW Parameters for Ferrite Content in 2205 DSS Welds Based on the Taguchi Design Method

Zhang

Gao

et al. 2018

This study presents the Taguchi design method with L9 orthogonal array which was carried out to optimize the flux-cored arc welding (FCAW) process parameters such as welding current, welding voltage, welding speed, and torch angle with reference to vertical for the ferrite content of duplex stainless steel (DSS, UNS S32205) welds. The analysis of variance (ANOVA) was applied, and a mathematical model was developed to predict the effect of process parameters on the responses. The results indicate that welding current, welding voltage, welding speed, torch angle with reference to vertical, and the interaction of welding voltage and welding speed are the significant model terms connected with the ferrite content. The ferrite content increases with the increase of welding speed and torch angle with reference to vertical, but decreases with the increase of welding current and welding voltage. Through the developed mathematical model, the target of 50% ferrite content in weld metal can be obtained when all the welding parameters are set at the optimum values. Finally, in order to validate experimental results, confirmation tests were implemented at optimum working conditions. Under these conditions, there was good accordance between the predicted and the experimental results for the ferrite content.