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REDUCING SOUND INTENSITY BY OPTIMIZING CUTTING PARAMETERS ON CNC MILLING MACHINES

Year 2022, , 62 - 73, 30.04.2022
https://doi.org/10.46519/ij3dptdi.1041325

Abstract

In the cutting process with machine tools in the Machinery Manufacturing Industry; while the desired surface integrity is ensured by the optimization of the cutting parameters, the noise level must be kept at a minimum to protect the health of the workers. The noise level can be reduced by using this optimization without compromising the surface roughness through processing of EN AW 6013 material on a CNC milling machine. Experimental design was examined in three variables, three levels and two target functions. The effects of these parameters on the target function were studied by performing experimental plans determined by "Central Composite Design (CCT)" of Response Surface Method (RSM)". To assess the sound intensity and surface roughness, mathematical models were developed by performing regression analysis on the experimental results. These developed models have been tested with control experiments and it has been seen that the models have acceptable error rates. The obtained regression equation is highly modeled with a validity of 93.29% for sound intensity and 97.33% for surface roughness. Therefore, cutting parameters were found to be related to sound intensity and surface roughness values.

References

  • 1. Suraratchai, M., Limido, J., Mabru, C., Chieragatti, R., "Modelling the influence of machined surface roughness on the fatigue life of aluminium alloy", International Journal of Fatigue., Vol. 30, Pages 2119–2126, 2008.
  • 2. Hilbert, L. R., Bagge-Ravn, D., Kold, J., Gram, L., ''Influence of surface roughness of stainless steel on microbial adhesion and corrosion resistance'', International Biodeterioration & Biodegradation.,Vol. 52, Pages 175–185, 2003.
  • 3. Hesselbach, J., Hoffmeister, H. W., Schuller, B. C., Loeis, K., ''Development of an active clamping system for noise and vibration reduction'', CIRP Annals - Manufacturing Technology., Vol.59, Pages 395–398, 2010.
  • 4. Sahoo, A. K., Sahoo, B., ''Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) of indexable carbide inserts in hard machining: Part-II (RSM, grey relational and techno economical approach)'', Measurement., Vol. 46, Pages 2868–2884, 2013.
  • 5. Lela, B., Bajić, D., Jozić, S., ''Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling'', International Journal of Advanced Manufacturing Technology., Vol.42, Pages 1082–1088, 2009.
  • 6. Chavoshi, S. Z., Tajdari, M., ''Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool'', International Journal of Material Forming., Vol.3, Pages 233–239, 2010.
  • 7. Singh, D., Rao, P. V., ''A surface roughness prediction model for hard turning process'', International Journal of Advanced Manufacturing Technology., Vol.32, Pages 1115–1124, 2007.
  • 8. Yan, J., Li, L., ''Multi-objective optimization of milling parameters – the trade-offs between energy, production rate and cutting quality'', Journal of Cleaner Production., Vol.52, Pages 462–471, 2013.
  • 9. Şahinoğlu, A., Rafighi, M., ''Investigation of Vibration, Sound level, Machine Current and Surface Roughness Values of AISI 4140 During Machining on the Lathe'', Arabian Journal for Science and Engineering., Vol.45, Pages 765–778, 2020.
  • 10. Asiltürk, I., Akkuş, H., ''Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method'', Measurement., Vol. 44, Pages 1697–1704, 2011.
  • 11. Özel, T., Karpat, Y., ''Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks'', International Journal of Machine Tools and Manufacture.,Vol. 45,Pages 467–479, 2005.
  • 12. Basak, S., Dixit, U. S., Davim, J. P., ''Application of radial basis function neural networks in optimization of hard turning of AISI D2 cold-worked tool steel with a ceramic tool, Proceedings of the Institution of Mechanical Engineers'', Part B: Journal of Engineering Manufacture.,Vol. 221, Pages 987–998, 2007.
  • 13. Singh, D., Venkateswara Rao, P., ''Optimization of tool geometry and cutting parameters for hard turning'', Materials and Manufacturing Processes., Vol. 22, Pages 15–21, 2007.
  • 14. Kalidass, S., Palanisamy, P., ''Prediction of Surface Roughness for AISI 304 Steel with Solid Carbide Tools in End Milling Process Using Regression and ANN Models'', Arabian Journal for Science and Engineering., Vol.39, Pages 8065–8075, 2014.
  • 15. Kuram, E., Ozcelik, B., ''Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling'', Journal of Intelligent Manufacturing., Vol.27, Pages 817–830, 2016.
  • 16. Bagaber, S. A., Yusoff, A. R., ''Multi-objective optimization of cutting parameters to minimize power consumption in dry turning of stainless steel 316'', Journal of Cleaner Production., Vol.157, Pages 30–46, 2017.
  • 17. Bouzid, L., Yallese, M. A., Chaoui, K., Mabrouki, T., Boulanouar, L., ''Mathematical modeling for turning on AISI 420 stainless steel using surface response methodology, Proceedings of the Institution of Mechanical Engineers'', Part B: Journal of Engineering Manufacture., Vol.229, Pages 45–61, 2014.
  • 18. Zerti, O., Yallese, M. A., Zerti, A., Belhadi, S., Girardin, F., ''Simultaneous improvement of surface quality and productivity using grey relational analysis based taguchi design for turning couple (AISI D3 steel/ mixed ceramic tool (Al2O3 + TiC))'', International Journal of Industrial Engineering Computations., Vol.9, Pages 173–194, 2018.
  • 19. Bouzid, L., Boutabba, S., Yallese, M. A., Belhadi, S., Girardin, F., ''Simultaneous optimization of surface roughness and material removal rate for turning of X20Cr13 stainless steel'', International Journal of Advanced Manufacturing Technology., Vol.74,Pages 879–891, 2014.
  • 20. Karabulut, Ş., ''Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method'', Measurement: Journal of the International Measurement Confederation.,Vol. 66,Pages 139–149, 2015.
  • 21. Campatelli, G., Lorenzini, L., Scippa, A., ''Optimization of process parameters using a Response Surface Method for minimizing power consumption in the milling of carbon steel'', Journal of Cleaner Production., Vol.66, Pages 309–316, 2014.
  • 22. Nur Sabreena, A. H., Nor Azma, Y., Mohamad, O., ''Article Response Surface Methodology for Optimisation of Parameters for Extraction of Stevia Rebaudiana Using Water, H 2 0'', | Iioabj |., Vol.7, Pages 459–466, 2016.
  • 23. Choudhury, I. A., El-Baradie, M. A., ''Surface roughness prediction in the turning of high-strength steel by factorial design of experiments'', Journal of Materials Processing Technology., Vol.67, Pages 55–61, 1997.
  • 24. Dabnun, M. A., Hashmi, M. S. J., El-Baradie, M. A., ''Surface roughness prediction model by design of experiments for turning machinable glass–ceramic (Macor)'', Journal of Materials Processing Technology., Vol. 164–165, Pages 1289–1293, 2005.
  • 25. Kopač, J., Bahor, M., Interaction of the technological history of a workpiece material and the machining parameters on the desired quality of the surface roughness of a product, Journal of Materials Processing Technology.,Vol. 92–93,Pages 381–387, 1999.
  • 26. Puertas Arbizu, I., Luis Pérez, C. J., ''Surface roughness prediction by factorial design of experiments in turning processes'', Journal of Materials Processing Technology.,Vol. 143–144, Pages 390–396, 2003.
Year 2022, , 62 - 73, 30.04.2022
https://doi.org/10.46519/ij3dptdi.1041325

Abstract

References

  • 1. Suraratchai, M., Limido, J., Mabru, C., Chieragatti, R., "Modelling the influence of machined surface roughness on the fatigue life of aluminium alloy", International Journal of Fatigue., Vol. 30, Pages 2119–2126, 2008.
  • 2. Hilbert, L. R., Bagge-Ravn, D., Kold, J., Gram, L., ''Influence of surface roughness of stainless steel on microbial adhesion and corrosion resistance'', International Biodeterioration & Biodegradation.,Vol. 52, Pages 175–185, 2003.
  • 3. Hesselbach, J., Hoffmeister, H. W., Schuller, B. C., Loeis, K., ''Development of an active clamping system for noise and vibration reduction'', CIRP Annals - Manufacturing Technology., Vol.59, Pages 395–398, 2010.
  • 4. Sahoo, A. K., Sahoo, B., ''Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) of indexable carbide inserts in hard machining: Part-II (RSM, grey relational and techno economical approach)'', Measurement., Vol. 46, Pages 2868–2884, 2013.
  • 5. Lela, B., Bajić, D., Jozić, S., ''Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling'', International Journal of Advanced Manufacturing Technology., Vol.42, Pages 1082–1088, 2009.
  • 6. Chavoshi, S. Z., Tajdari, M., ''Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool'', International Journal of Material Forming., Vol.3, Pages 233–239, 2010.
  • 7. Singh, D., Rao, P. V., ''A surface roughness prediction model for hard turning process'', International Journal of Advanced Manufacturing Technology., Vol.32, Pages 1115–1124, 2007.
  • 8. Yan, J., Li, L., ''Multi-objective optimization of milling parameters – the trade-offs between energy, production rate and cutting quality'', Journal of Cleaner Production., Vol.52, Pages 462–471, 2013.
  • 9. Şahinoğlu, A., Rafighi, M., ''Investigation of Vibration, Sound level, Machine Current and Surface Roughness Values of AISI 4140 During Machining on the Lathe'', Arabian Journal for Science and Engineering., Vol.45, Pages 765–778, 2020.
  • 10. Asiltürk, I., Akkuş, H., ''Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method'', Measurement., Vol. 44, Pages 1697–1704, 2011.
  • 11. Özel, T., Karpat, Y., ''Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks'', International Journal of Machine Tools and Manufacture.,Vol. 45,Pages 467–479, 2005.
  • 12. Basak, S., Dixit, U. S., Davim, J. P., ''Application of radial basis function neural networks in optimization of hard turning of AISI D2 cold-worked tool steel with a ceramic tool, Proceedings of the Institution of Mechanical Engineers'', Part B: Journal of Engineering Manufacture.,Vol. 221, Pages 987–998, 2007.
  • 13. Singh, D., Venkateswara Rao, P., ''Optimization of tool geometry and cutting parameters for hard turning'', Materials and Manufacturing Processes., Vol. 22, Pages 15–21, 2007.
  • 14. Kalidass, S., Palanisamy, P., ''Prediction of Surface Roughness for AISI 304 Steel with Solid Carbide Tools in End Milling Process Using Regression and ANN Models'', Arabian Journal for Science and Engineering., Vol.39, Pages 8065–8075, 2014.
  • 15. Kuram, E., Ozcelik, B., ''Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling'', Journal of Intelligent Manufacturing., Vol.27, Pages 817–830, 2016.
  • 16. Bagaber, S. A., Yusoff, A. R., ''Multi-objective optimization of cutting parameters to minimize power consumption in dry turning of stainless steel 316'', Journal of Cleaner Production., Vol.157, Pages 30–46, 2017.
  • 17. Bouzid, L., Yallese, M. A., Chaoui, K., Mabrouki, T., Boulanouar, L., ''Mathematical modeling for turning on AISI 420 stainless steel using surface response methodology, Proceedings of the Institution of Mechanical Engineers'', Part B: Journal of Engineering Manufacture., Vol.229, Pages 45–61, 2014.
  • 18. Zerti, O., Yallese, M. A., Zerti, A., Belhadi, S., Girardin, F., ''Simultaneous improvement of surface quality and productivity using grey relational analysis based taguchi design for turning couple (AISI D3 steel/ mixed ceramic tool (Al2O3 + TiC))'', International Journal of Industrial Engineering Computations., Vol.9, Pages 173–194, 2018.
  • 19. Bouzid, L., Boutabba, S., Yallese, M. A., Belhadi, S., Girardin, F., ''Simultaneous optimization of surface roughness and material removal rate for turning of X20Cr13 stainless steel'', International Journal of Advanced Manufacturing Technology., Vol.74,Pages 879–891, 2014.
  • 20. Karabulut, Ş., ''Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method'', Measurement: Journal of the International Measurement Confederation.,Vol. 66,Pages 139–149, 2015.
  • 21. Campatelli, G., Lorenzini, L., Scippa, A., ''Optimization of process parameters using a Response Surface Method for minimizing power consumption in the milling of carbon steel'', Journal of Cleaner Production., Vol.66, Pages 309–316, 2014.
  • 22. Nur Sabreena, A. H., Nor Azma, Y., Mohamad, O., ''Article Response Surface Methodology for Optimisation of Parameters for Extraction of Stevia Rebaudiana Using Water, H 2 0'', | Iioabj |., Vol.7, Pages 459–466, 2016.
  • 23. Choudhury, I. A., El-Baradie, M. A., ''Surface roughness prediction in the turning of high-strength steel by factorial design of experiments'', Journal of Materials Processing Technology., Vol.67, Pages 55–61, 1997.
  • 24. Dabnun, M. A., Hashmi, M. S. J., El-Baradie, M. A., ''Surface roughness prediction model by design of experiments for turning machinable glass–ceramic (Macor)'', Journal of Materials Processing Technology., Vol. 164–165, Pages 1289–1293, 2005.
  • 25. Kopač, J., Bahor, M., Interaction of the technological history of a workpiece material and the machining parameters on the desired quality of the surface roughness of a product, Journal of Materials Processing Technology.,Vol. 92–93,Pages 381–387, 1999.
  • 26. Puertas Arbizu, I., Luis Pérez, C. J., ''Surface roughness prediction by factorial design of experiments in turning processes'', Journal of Materials Processing Technology.,Vol. 143–144, Pages 390–396, 2003.
There are 26 citations in total.

Details

Primary Language English
Subjects Mechanical Engineering
Journal Section Research Article
Authors

Sirer Albayrak 0000-0002-3201-1789

Serdar Mercan 0000-0002-1225-8290

Hikmet Karaçam 0000-0002-1899-2373

Publication Date April 30, 2022
Submission Date December 24, 2021
Published in Issue Year 2022

Cite

APA Albayrak, S., Mercan, S., & Karaçam, H. (2022). REDUCING SOUND INTENSITY BY OPTIMIZING CUTTING PARAMETERS ON CNC MILLING MACHINES. International Journal of 3D Printing Technologies and Digital Industry, 6(1), 62-73. https://doi.org/10.46519/ij3dptdi.1041325
AMA Albayrak S, Mercan S, Karaçam H. REDUCING SOUND INTENSITY BY OPTIMIZING CUTTING PARAMETERS ON CNC MILLING MACHINES. IJ3DPTDI. April 2022;6(1):62-73. doi:10.46519/ij3dptdi.1041325
Chicago Albayrak, Sirer, Serdar Mercan, and Hikmet Karaçam. “REDUCING SOUND INTENSITY BY OPTIMIZING CUTTING PARAMETERS ON CNC MILLING MACHINES”. International Journal of 3D Printing Technologies and Digital Industry 6, no. 1 (April 2022): 62-73. https://doi.org/10.46519/ij3dptdi.1041325.
EndNote Albayrak S, Mercan S, Karaçam H (April 1, 2022) REDUCING SOUND INTENSITY BY OPTIMIZING CUTTING PARAMETERS ON CNC MILLING MACHINES. International Journal of 3D Printing Technologies and Digital Industry 6 1 62–73.
IEEE S. Albayrak, S. Mercan, and H. Karaçam, “REDUCING SOUND INTENSITY BY OPTIMIZING CUTTING PARAMETERS ON CNC MILLING MACHINES”, IJ3DPTDI, vol. 6, no. 1, pp. 62–73, 2022, doi: 10.46519/ij3dptdi.1041325.
ISNAD Albayrak, Sirer et al. “REDUCING SOUND INTENSITY BY OPTIMIZING CUTTING PARAMETERS ON CNC MILLING MACHINES”. International Journal of 3D Printing Technologies and Digital Industry 6/1 (April 2022), 62-73. https://doi.org/10.46519/ij3dptdi.1041325.
JAMA Albayrak S, Mercan S, Karaçam H. REDUCING SOUND INTENSITY BY OPTIMIZING CUTTING PARAMETERS ON CNC MILLING MACHINES. IJ3DPTDI. 2022;6:62–73.
MLA Albayrak, Sirer et al. “REDUCING SOUND INTENSITY BY OPTIMIZING CUTTING PARAMETERS ON CNC MILLING MACHINES”. International Journal of 3D Printing Technologies and Digital Industry, vol. 6, no. 1, 2022, pp. 62-73, doi:10.46519/ij3dptdi.1041325.
Vancouver Albayrak S, Mercan S, Karaçam H. REDUCING SOUND INTENSITY BY OPTIMIZING CUTTING PARAMETERS ON CNC MILLING MACHINES. IJ3DPTDI. 2022;6(1):62-73.

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