REDUCING SOUND INTENSITY BY OPTIMIZING CUTTING PARAMETERS ON CNC MILLING MACHINES
Year 2022,
Volume: 6 Issue: 1, 62 - 73, 30.04.2022
Sirer Albayrak
,
Serdar Mercan
,
Hikmet Karaçam
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.
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Year 2022,
Volume: 6 Issue: 1, 62 - 73, 30.04.2022
Sirer Albayrak
,
Serdar Mercan
,
Hikmet Karaçam
References
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- 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.
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- 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.
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- 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.