Manufacturing Technology Today

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Abrasive Water Jet Machining of Al5083/ Zro2/ B4c Hybrid Aluminium Metal Matrix Composite and Optimization of Its Process Parameters


Affiliations
1 Sri Venkateswara University College of Engineering, Tirupati, India
     

   Subscribe/Renew Journal


In this work, Abrasive water jet machining experiments are conducted on Al 5083/ B4C/ ZrO2 metal matrix composites. Experiments are conducted according to Taguchi’s experimental design (OA27) for different combinations of nozzle diameter, stand-off distance, jet pressure, abrasive flow rate, and traverse speed. The experimental data of material removal rate and surface roughness are recorded for these runs and are analysed using Taguchi - Genetic algorithm method for identification of optimal process parameters. Further, ANOVA is conducted to determine the contribution of each of these parameters on machining responses. This work is more useful for maximizing MRR thereby the machining process will be done much faster and at the same time, minimizing the Surface Roughness so as to obtain a smoother finish. The confirmation test is done at optimal parameters combinations and results are satisfactory.

Keywords

Machining, Material Removal Rate, Surface Roughness, Taguchi- Genetic Algorithm, Optimization of Process Parameters.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Azmir, M. A., & Ahsan, A. K. (2008). Investigation on glass/epoxy composite surfaces machined by abrasive water jet machining. Journal of Materials Processing Technology, 198(1-3), 122-128.

  • Balasubramaniam, R., Krishan, J., & Ramakrishnan, N. (2000). Empirical study on the generation of edge radius in abrasive jet external deburring (AJED). Journal of Materials Processing Technology, 99(1-3), 49-53.

  • Byrne, D. M. (1986). The Taguchi approach to parameter design. ASQ’s Annu Qual Congr Proc, 40, 168.

  • Chithiraiponselvan, M., & Mohanasundararaju, N. (2012). Analysis of surface roughness in abrasive waterjet cutting of cast iron. International Journal of Science, Environment and Technology, 1(3), 174-182.

  • Iqbal, A., Dar, N. U., & Hussain, G. (2011). Optimization of abrasive water jet cutting of ductile materials. Journal of Wuhan University of Technology-Mater. Sci. Ed., 26(1), 88-92.

  • Jegaraj, J. J. R., & Babu, N. R. (2005). A strategy for efficient and quality cutting of materials with abrasive waterjets considering the variation in orifice and focusing nozzle diameter. International Journal of Machine Tools and Manufacture, 45(12-13), 1443-1450.

  • Khan, M. A., Soni, H., Mashinini, P. M., & Uthayakumar, M. (2021). Abrasive water jet cutting process form machining metals and composites for engineering applications: A review. Engineering Research Express, 3(2), 022004.

  • Kumar, N., & Shukla, M. (2012). Finite element analysis of multi-particle impact on erosion in abrasive water jet machining of titanium alloy. Journal of Computational and Applied Mathematics, 236(18), 4600-4610.

  • Kumar, P., & Kant, R. (2019). Experimental study of abrasive water jet machining of Kevlar epoxy composite. Journal of Manufacturing Engineering, 14(1), 026-032.

  • Lohar, S. R., & Kubade, P. R. (2016). Current research and development in abrasive water jet machining (AWJM): A Review. Int. J. Sci. Res, 5(1), 996-999.

  • Madhu, S., & Balasubramanian, M. (2015). A review on abrasive jet machining process parameters. Applied Mechanics and Materials, 766, 629-634.

  • Nagdeve, L., Chaturvedi, V., & Vimal, J. (2012) Implementation of Taguchi approach for optimization of abrasive water jet machining process parameters. International Journal of Instrumentation, Control and Automation (IJICA), 1(3,4), 9-13.

  • Niranjan, C. A., Srinivas, S., & Ramachandra, M. (2018). An experimental study on depth of cut of AZ91 Magnesium Alloy in abrasive water jet cutting. Materials Today: Proceedings, 5(1), 2884-2890.

  • Niranjan, C. A., Srinivas, S., & Ramachandra, M. (2018). Effect of process parameters on depth of penetration and topography of AZ91 magnesium alloy in abrasive water jet cutting. Journal of magnesium and alloys, 6(4), 366-374.

  • Pudi, S., Avinash, I. G., & Ashok, I. K. (2020). Optimization of process parameters of AWJM ON AA6061-7.5% SiC MMC for lower surface roughness and higher MRR. International Journal of Engineering Applied Sciences and Technology, 5(3), 413-421.

  • Ross, P. J. (1988). Taguchi techniques for quality engineering: loss function, orthogonal experiments, parameter and tolerance design.

  • Selvam, R., Karunamoorthy, L., & Arunkumar, N. (2017). Investigation on performance of abrasive water jet in machining hybrid composites. Materials and Manufacturing Processes, 32(6), 700-706.

  • Selvan, M. C. P., & Raju, N. M. S. (2012). Analysis of surface roughness in abrasive waterjet cutting of cast iron. International Journal of Science, Environment and Technology, 1(3), 174-182.

  • Soni, D., & Patel, P. R. (2016). Experimental investigation and parametric study of abrasive water jet cutting process. Journal of Emerging Technologies and Innovative Research, 3(6), 155-162.


Abstract Views: 116

PDF Views: 0




  • Abrasive Water Jet Machining of Al5083/ Zro2/ B4c Hybrid Aluminium Metal Matrix Composite and Optimization of Its Process Parameters

Abstract Views: 116  |  PDF Views: 0

Authors

K. Vijayalakshmi
Sri Venkateswara University College of Engineering, Tirupati, India
G. Bhanodaya Reddy
Sri Venkateswara University College of Engineering, Tirupati, India

Abstract


In this work, Abrasive water jet machining experiments are conducted on Al 5083/ B4C/ ZrO2 metal matrix composites. Experiments are conducted according to Taguchi’s experimental design (OA27) for different combinations of nozzle diameter, stand-off distance, jet pressure, abrasive flow rate, and traverse speed. The experimental data of material removal rate and surface roughness are recorded for these runs and are analysed using Taguchi - Genetic algorithm method for identification of optimal process parameters. Further, ANOVA is conducted to determine the contribution of each of these parameters on machining responses. This work is more useful for maximizing MRR thereby the machining process will be done much faster and at the same time, minimizing the Surface Roughness so as to obtain a smoother finish. The confirmation test is done at optimal parameters combinations and results are satisfactory.

Keywords


Machining, Material Removal Rate, Surface Roughness, Taguchi- Genetic Algorithm, Optimization of Process Parameters.

References