ANALYSIS OF SURFACE ROUGHNESS, SOUND LEVEL, VIBRATION AND CURRENT WHEN MACHINING AISI 1040 STEEL

Year 2019, Volume: 37 Issue: 2, 423 - 437, 01.06.2019

Abidin Şahinoğlu Abdulkadir Güllü İbrahim Çiftçi

Abstract

AISI 1040 steel is widely used for production of various parts. This material has been studied by many researchers. In this work, turning tests were carried out on AISI 1040 steel workpieces at five different depth of cuts, four different feed rates and 4 different cutting speeds without coolant. The influence of the cutting parameters on turned part surface roughness, vibration, sound level and machine tool motor current were examined. A full factorial experimental design method was used. The Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) were used to determine the effects of input parameters on the resultant surface roughness, vibration, sound level, current. The experimental results showed that increasing feed rate increased the surface roughness, vibration, sound level and current values. The most effective cutting parameter on all the output parameters was found to be the feed rate. Furthermore as feed rate and depth of cut increased, the current value and sound level also increased.

Keywords

AISI 1040, surface roughness, sound level, vibration, current, ANOVA.

References

• [1] Murugappan, S., Arul, S. 2017. Effect of Cryogenic Pre cooling on Chip Reduction Co-efficient during Turning of EN8 Steel Rod. Materials today proceedig, 4,8848-8855.
• [2] Sharma, A.K., Singh, R.K., Dixit, A.R., Tiwari, A.K. 2016. Characterization and experimental investigation of Al2O3 nanoparticle based cutting fluid in turning of AISI 1040 steel under minimum quantity lubrication (MQL). Materials Today: Proceeding, 3,1899-1906.
• [3] Nizamuddin, M., Agrawal, S., Patil, N. 2018. The Effect of Karanja based Soluble Cutting Fluid on Chips Formation in Orthogonal Cutting Process of AISI 1045 Steel. Procedia Manufacturing, 20,12-17.
• [4] Bedekar, V., Pauskar, P., Shivpuri, R., Howe, J. 2014. Microstructure and texture evolutions in AISI 1050 steel by flow forming. Procedia Engineering, 81,2355-2360.
• [5] Khalil, A.N.M., Ali, M.A.M., Azmi, A.I. 2015. Effect of Al2O3 nanolubricant with SDBS on tool wear during turning process of AISI 1050 with minimal quantity lubricant. Procedia Manufacturing, 2,130-134.
• [6] Klocke, F., Lortz, W., Trauth, D. 2018. Analysis of the dynamic chip formation process in turning. International Journal of Mechanical Sciences, 135,313-324.
• [7] Moganapriya, C., Rajasekar, R., Ponappa, K., Venkatesh, R., Jerome, S. 2018. Influence of Coating Material and Cutting Parameters on Surface Roughness and Material Removal Rate in Turning Process Using Taguchi Method. Materials Today: Proceedings, 5,8532-8538.
• [8] Hessainia, Z., Belbah, A., Yallese, M.A., Mabrouki, T., Rigal, J.F. 2013. On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations, Measurement. 46,1671-1681.
• [9] Yallese, M.A., Chaoui, K., Zeghib, N., Boulanouar, L., Rigal, J.F. 2009. Hard machining of hardened bearing steel using cubic boron nitride tool. Journal of materials processing technology, 209,1092-1104.
• [10] Padmini, R., Krishna, P.V., Mohana, Rao, G. 2016. Effectiveness of vegetable oil based nanofluids as potential cutting fluids in turning AISI 1040 steel. Tribology International, 94,490-501.
• [11] Krishna, P.V., Srikant, R.R., Rao, D.N. 2010. Experimental investigation on the performance of nanoboric acid suspensions in SAE-40 and coconut oil during turning of AISI 1040 steel. International Journal of Machine Tools and Manufacture, 50,911-916.
• [12] Aouici, H., Yallese, M.A., Chaoui, K., Mabrouki, T., Rigal, J. 2012. Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization. Measurement, 45,344-353.
• [13] Benga, G.C., Abrao, A.M. 2003. Turning of hardened 100Cr6 bearing steel with ceramic and PCBN cutting tools. Journal of Materials Processing Technology, 143,237-241.
• [14] Quintana, G., Ciurana, J. 2011. Chatter in machining processes: A review. International Journal of Machine Tools and Manufacture, 51,363-376.
• [15] Siddhpura, M., Paurabally, R. 2012. A review of chatter vibration research in turning. International Journal of Machine Tools and Manufacture, 61,27-47.
• [16] Karabulut, Ş., Şahinoğlu, A. 2018. Effect of the cutting parameters on surface roughness, power consumption and machine noise in machining of R260 steel. Politeknik Dergisi, 21(1),237-244.
• [17] Şahinoğlu, A., Karabulut, Ş., Güllü, A. 2017. Study on Spindle Vibration and Surface Finish in Turning of Al 7075. Solid State Phenomena, 261,321-327.
• [18] Plaza, E., Lopez, P. 2018. Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations. Mechanical Systems and Signal Processing, 98,902-919.
• [19] Kishore, R., Choudhury, S., Orra, K. 2018. On-line control of machine tool vibration in turning operation using electro-magneto rheological damper. Journal of Manufacturing Processes, 31,187-198.
• [20] Şahinoğlu, A., Gülllü, A., Dönertaş, M.A. 2017. GGG50 Malzemenin Torna Tezgâhında Farklı Kesme Parametrelerinde İşlenmesinde Titreşim, Ses Şiddetinin ve Yüzey Pürüzlülüğünün İncelenmesi. Sinop Üniversitesi Fen Bilimleri Dergisi, 2(1),67-79.
• [21] Zhong, Q., Tang, R., Peng, T. 2017. Decision rules for energy consumption minimization during material removal process in turning. Journal of Cleaner Production, 140,1819-1827.
• [22] Bagaber, S.A., Yusoff, A.R. 2017. Multi-objective optimization of cutting parameters to minimize power consumption in dry turning of stainless steel 316. Journal of Cleaner Production, 157,30-46.
• [23] Salgado, D.R. , Alonso, F.J. 2007. An approach based on current and sound signals for in-process tool wear monitoring. International Journal of Machine Tools & Manufacture, 47,2140-2152.
• [24] Zhou, L., Li, J., Li, F., Meng, Q., Li, J., Xu, X. 2016. Energy consumption model and energy efficiency of machine tools: a comprehensive literature review. Journal of Cleaner Production, 112,3721-3734.
• [25] Bilga, P.S., Singh, S., Kumar, R. 2016. Optimization of energy consumption response parameters for turning operation using Taguchi method. Journal of Cleaner Production, 137,1406-1417.
• [26] Pathak, A., Warghane, R., Deokar, S. 2018. Optimization of cutting parameters in Dry Turning of AISI A2 Tool Steel using Carbide Tool by Taguchi Based Fuzzy Logics. Materials Today Proceedings, 5,5082-5090.
• [27] Bhushan, R.K. 2013. Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. Journal of Cleaner Production, 39,242-254.
• [28] Chinchanikar, S., Choudhury, S.K. 2013. Effect of work material hardness and cutting parameters on performance of coated carbide tool when turning hardened steel: An optimization approach. Measurement, 46,1572-1584.
• [29] Saini, S. , Ahuja, I.S., Sharma, V.S. 2012. Influence of cutting parameters on tool wear and surface roughness in hard turning of AISI H11 tool steel using ceramic tools. International Journal of Precision Engineering and Manufacturing, 13,1295-1302.
• [30] Şeker, U., Kurt, A., Ciftci, I. 2002. Design and construction of a dynamometer for measurement of cutting forces during machining with linear motion. Materials & Design, 23,355-360.