Neural intelligence and regression analysis in modeling and optimization of flank wear during turning of Monel K500
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The current research article develops artificial neural network (ANN) and regression analysis for prediction and optimizationof flank wear. For this, the flank wear results were observed from live experiments conducted under the various levels of inputvariables like speed, feed and depth of cut. The ANN model architecture: 9-4-1 was observed as suitable for this analysis. Theexperimental results were utilized to train, test and validate the network. Neural intelligence tool was used to perform ANNanalysis. The prediction capability of the regression model was estimated based on R-value (correlation coefficient) amongexperiment’s and model prediction’s values. The predictions by ANN were found to be in precise among the predictive modelsas obtained from the R: squared value and correlation value. The insert life was also evaluated with respect differentmachining time at various level of machining factors. The flank wear, which is equal to equal to 0.35 mm, was considered ascriteria to decide the life of the insert. Further, the machining factors were optimized based on desirability approach. Therefore, these predictive models and optimized factors would pave the aero industries to predict as well as optimize the flankwear. The result of the experiments has revealed the feed rate as noteworthy variable than speed and depth of cut.
Abstract
The current research article develops artificial neural network (ANN) and regression analysis for prediction and optimizationof flank wear. For this, the flank wear results were observed from live experiments conducted under the various levels of inputvariables like speed, feed and depth of cut. The ANN model architecture: 9-4-1 was observed as suitable for this analysis. Theexperimental results were utilized to train, test and validate the network. Neural intelligence tool was used to perform ANNanalysis. The prediction capability of the regression model was estimated based on R-value (correlation coefficient) amongexperiment’s and model prediction’s values. The predictions by ANN were found to be in precise among the predictive modelsas obtained from the R: squared value and correlation value. The insert life was also evaluated with respect differentmachining time at various level of machining factors. The flank wear, which is equal to equal to 0.35 mm, was considered ascriteria to decide the life of the insert. Further, the machining factors were optimized based on desirability approach. Therefore, these predictive models and optimized factors would pave the aero industries to predict as well as optimize the flankwear. The result of the experiments has revealed the feed rate as noteworthy variable than speed and depth of cut.
참고문헌 (22)
- Aykut Eser, , Adv. Mat. Sci. Eng, pp.1~14 (2021)
- M. V. Ramana, , Clean. Eng. Tech, 2, pp.100034~ (2021)
- E. García-Martínez, , Materials, 14, pp.4868~ (2021)
- T. Segreto, , Prod. Eng. Res. Deel, 14, pp.693~705 (2020)
- S. Selvakumar, , Mat. Manuf. Proc, 36, pp.792~802 (2021)
- R. U. Baig, , Adv. Mech. Eng, 13, pp.1~14 (2021)
- S. H. Chen, , Inter. J. Adv. Manuf. Tech, 116, pp.3795~3808 (2021)
- A. Kosarac, , Materials, 15, pp.700~ (2022)
- M. S. Surya, , Int. J. Inter. Des. Manuf, pp.453~462 (2021)
- M. Adineh, , SN Appl. Sci, 3, pp.100~ (2021)
- T. Segreto, , Prod. Eng. Res. Deve, 14, pp.693~705 (2020)
- Z. Jurkovic, , J. Intell. Manuf, 29, pp.1683~1693 (2018)
- J. Francis Xavier, , J. Nan. Mate, pp.1~11 (2021)
- C. Ezilarasan, , Int. J. Precision Tech, 2, pp.1~14 (2011)
- K. Arul, , Mat. Manuf. Proc, 35, pp.405~414 (2020)
- K. Arul, , J. Bal. Trib. Assoc, 26, pp.589~600 (2020)
- A. Kulandaivel, , Mach. Sci. Technol, 24(5), pp.810~836 (2020)
- Maryam Abolfazli, "Preparation and characterization of Alumina", Magnesia, Graphite refractory composites using response surface methodology, pp.Journal of Ceramic Processing Research, 2022[DOI]
- Thirumalai Ramanathan, "An analysis of ceramic tool condition monitoring for enhanced and secured manufacturing", Journal of Ceramic Processing Research, 23(2), pp.228~232 (2022), 청정에너지연구소[DOI]
- Senthilkumar KM, "Optimization of stir casting process parameter using response surface meth odology", Journal of Ceramic Processing Research, 23(2), pp.233~236 (2022), 청정에너지연구소[DOI]
- S. Karthick, "Optimization of CO2 laser welding process parameters on cupronickel alloys using multi-objective techniques", Journal of Ceramic Processing Research, 23(3), pp.278~286 (2022), 청정에너지연구소[DOI]
- Yan-Cherng Lin, "Optimization of EDM parameters for ZrO2 and Al2O3 ceramics using Taguchi method", Journal of Ceramic Processing Research, 16(2), pp.249~257 (2015), 청정에너지연구소[DOI]