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Investigation on the Correlation between Surface Roughness and Acoustic Emission Characteristics in Turning Process


Affiliations
1 Surface Engineering and Tribology Laboratory, CSIR-Central Mechanical Engineering Research Institute, Durgapur, India
2 Material Processing and Microsystem Laboratory, CSIR-Central Mechanical Engineering Research Institute, Durgapur, India
     

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One of the most significant feature for monitoring in machining processes is tool wear. It has a direct influence on the quality of machined surfaces. In-order to maintain the product quality and to reduce material wastage, online tool wear monitoring has become a regular practice. With the progress in tool wear, surface roughness changes accordingly and this change can be used to assess the tool condition. However, it is difficult to measure the surface roughness online. It is well known that interaction of tool and workpiece results in high frequency stress waves known as Acoustic Emissions (AE), which can be used as an indirect online method to monitor the surface roughness and in-turn tool wear. The analysis of AE signals which received significant attention in structural and machine health monitoring opens wide opportunities to monitor the machining process. Hence, in the present work an attempt has been made to explore the correlation between the acoustic emission characteristics and workpiece surface roughness during the high speed turning operation using AISI 4340 alloy steel workpiece with the help of Zirconia Toughened Alumina (ZTA) tool on a lathe machine. Experiments has been designed as per Central Composite Design (CCD) of Response Surface Methodology (RSM) with varying 3 levels of 3 parameters such as cutting speed, feed rate and depth of cut. For each experiment, AE signals are acquired and surface roughness is measured using Surtronic 25 portable surface roughness meter. Analysis of variance (ANOVA) is used to study the effect of control parameters on output responses and a model is prepared using regression analysis. It is observed from the ANOVA analysis that feed rate and cutting speed have profound influence on surface roughness and RMS respectively. The optimum condition is found at Cutting speed of 300 m/min with feed rate of 0.12 mm/rev and depth of cut of 1.5 mm with 97.15% desirability for minimum surface roughness and nominal RMS value. From the parametric study, it is observed that AE characteristic (RMS) shows good correlation with surface roughness which can be used for further analysis in online monitoring of tool wear.

Keywords

AE Characteristics, RMS, Surface Roughness, Tool Wear.
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  • Gómez, MP and Hey, AM: Assessment of cutting tool condition by acoustic emission, vol. 1, 2012, 321–328.

  • Amorim, HJ and Neto, A O K: Study of the Relationship between Tool Wear and Surface Finish in Turning with Carbide Tool, 'Adv. Mater. Res.', vol. 902, 2014, 95-100.

  • Seeman, M; Ganesan, G; Karthikeyan, R and Velayudham, A: Study on tool wear and surface roughness in machining of particulate aluminum metal matrix composite-response surface methodology approach, 'Int. J. Adv. Manuf. Technol'., vol. 48, no. 5-8, 2010, 613-624.

  • Teti, R; Jemielniak, K; O'Donnell, G; and Dornfeld, D: Advanced monitoring of machining operations, 'CIRP Ann.', vol. 59, no. 2, 2010, 717-739.

  • Kurada, S and Bradley, C: A review of machine vision sensors for tool condition monitoring, 'Comput. Ind'., vol. 34, no. 1, Oct. 1997, 55-72.

  • Cakan, A: Real-time monitoring of flank wear behavior of ceramic cutting tool in turning hardened steels, 'Int. J. Adv. Manuf. Technol.', vol. 52, no. 9-12, 2011, 897-903.

  • Cook, NH: Tool wear sensors, 'Wear', vol. 62, no. 1, 1980, 49-57.

  • Lunde, G and Anderson, PB: A study of the wear processes on cemented carbide cutting tool by a radioactive tracer technique, 'Int. J. Mach. Tool Des. Res.', vol. 10, no. 1, Mar. 1970, 79-93.

  • Siddhpura, A and Paurobally, R: A review of flank wear prediction methods for tool condition monitoring in a turning process, 'Int. J. Adv. Manuf. Technol.', vol. 65, no. 1-4, 2013, 371-393.

  • Dimla, DE and Lister, PM: On-line metal cutting tool condition monitoring, 'Int. J. Mach. Tools Manuf.', vol. 40, no. 5, 739-768, Apr. 2000.

  • Ambhore, N; Kamble, D; Chinchanikar, S and Wayal, V: Tool condition monitoring system: A review, 'Mater. Today Proc.', vol. 2, no. 4-5, 2015, 3419-3428.

  • Li, X: A brief review: Acoustic emission method for tool wear monitoring during turning,” Int. J. Mach. Tools Manuf., vol. 42, no. 2, 2002, 157-165.

  • Nair, A and Cai, CS: Acoustic emission monitoring of bridges: Review and case studies, 'Eng. Struct.', vol. 32, no. 6, Jun, 2010, 1704-1714.

  • Nor, NM: Structural health monitoring through acoustic emission, in Eco-Efficient Repair and Rehabilitation of Concrete Infrastructures, Elsevier, 2018, 123-146.

  • Ferrari, G and Gómez, MP: Correlation Between Acoustic Emission , Thrust and Tool Wear in Drilling, 'Procedia Mater. Sci.', vol. 8, 2015, 693-701.

  • Dirk Söffker and Sandra Rothe*: New Approaches for Supervision of Systems with Sliding Wear: Fundamental Problems and Experimental Results Using Different Approaches, 'Appl. Sci.', vol. 7, no. 8, 2017, 843.

  • Hase, A; Wada, M; Koga, T; Mishina, H: The relationship between acoustic emission signals and cutting phenomena in turning process, 'Int. J. Adv. Manuf. Technol.', vol. 70, no. 5–8, 2014, 947-955.

  • Kumar Singh, B; Roy, H; Mondal, B; and Roy, S S: Development and machinability evaluation of MgO doped Y-ZTA ceramic inserts for high-speed machining of steel Y-ZTA ceramic inserts for high-speed machining of steel,” Machining Science and Technology, Taylor & Francis, 1-15, 2018.

  • Singh, BK; Mondal, B; Mandal, N: Machinability evaluation and desirability function optimization of turning parameters for Cr2O3 doped zirconia toughened alumina (Cr-ZTA) cutting insert in high speed machining of steel, 'Ceram. Int.', vol. 42, no. 2, 2016, 3338-3350.


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  • Investigation on the Correlation between Surface Roughness and Acoustic Emission Characteristics in Turning Process

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Authors

Sucharita Saha
Surface Engineering and Tribology Laboratory, CSIR-Central Mechanical Engineering Research Institute, Durgapur, India
Bipin Kumar Singh
Material Processing and Microsystem Laboratory, CSIR-Central Mechanical Engineering Research Institute, Durgapur, India
M. Phani Kumar
Surface Engineering and Tribology Laboratory, CSIR-Central Mechanical Engineering Research Institute, Durgapur, India
Naresh Chandra Murmu
Surface Engineering and Tribology Laboratory, CSIR-Central Mechanical Engineering Research Institute, Durgapur, India

Abstract


One of the most significant feature for monitoring in machining processes is tool wear. It has a direct influence on the quality of machined surfaces. In-order to maintain the product quality and to reduce material wastage, online tool wear monitoring has become a regular practice. With the progress in tool wear, surface roughness changes accordingly and this change can be used to assess the tool condition. However, it is difficult to measure the surface roughness online. It is well known that interaction of tool and workpiece results in high frequency stress waves known as Acoustic Emissions (AE), which can be used as an indirect online method to monitor the surface roughness and in-turn tool wear. The analysis of AE signals which received significant attention in structural and machine health monitoring opens wide opportunities to monitor the machining process. Hence, in the present work an attempt has been made to explore the correlation between the acoustic emission characteristics and workpiece surface roughness during the high speed turning operation using AISI 4340 alloy steel workpiece with the help of Zirconia Toughened Alumina (ZTA) tool on a lathe machine. Experiments has been designed as per Central Composite Design (CCD) of Response Surface Methodology (RSM) with varying 3 levels of 3 parameters such as cutting speed, feed rate and depth of cut. For each experiment, AE signals are acquired and surface roughness is measured using Surtronic 25 portable surface roughness meter. Analysis of variance (ANOVA) is used to study the effect of control parameters on output responses and a model is prepared using regression analysis. It is observed from the ANOVA analysis that feed rate and cutting speed have profound influence on surface roughness and RMS respectively. The optimum condition is found at Cutting speed of 300 m/min with feed rate of 0.12 mm/rev and depth of cut of 1.5 mm with 97.15% desirability for minimum surface roughness and nominal RMS value. From the parametric study, it is observed that AE characteristic (RMS) shows good correlation with surface roughness which can be used for further analysis in online monitoring of tool wear.

Keywords


AE Characteristics, RMS, Surface Roughness, Tool Wear.

References