Skip to main content

Advertisement

Log in

Experimental investigation on machining of hardstone quartz with modified AJM using hot silicon carbide abrasives

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

This study aims to analyze the accomplishment of cutting performance in hot abrasive jet machining (HAJM) of hardstone quartz concerning surface roughness, taper angle, and material removal rate. Fifteen sets of experimental trials were conducted by considering three cutting parameters (air pressure, abrasive temperature, standoff-distance) based on Box–Behnken’s design of experiments. Additionally, response surface methodology, analysis of variance, and statistical technique (here, desirability function approach) followed by computational approach (here, genetic algorithm) are, respectively, employed for experimental investigation, predictive modeling, and multi-response optimization. Thereafter, the effectiveness of proposed two multi-objective optimization techniques is evaluated by confirmation test and subsequently, the best optimal solution is used for cost analysis to rationalize the usefulness of hot abrasives in AJM process with an intension to raise the awareness in the manufacturing industry. Based on results, application of hot abrasives in AJM process has shown an attention in enhancing the cutting performance for material removal.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Trivedi P, Dhanawade A, Kumar S (2015) An experimental investigation on cutting performance of abrasive water jet machining of austenite steel (AISI 316L). Adv Mater Process Technol 1(3–4):263–274

    Google Scholar 

  2. Sasikumar K, Arulshri K, Ponappa K, Uthayakumar M (2016) A study on kerf characteristics of hybrid aluminium 7075 metal matrix composites machined using abrasive water jet machining technology. Proc Inst Mech Eng Part B J Eng Manuf 232(4):690–704

    Google Scholar 

  3. Kumaran ST, Ko TJ, Uthayakumar M, Islam MM (2017) Prediction of surface roughness in abrasive water jet machining of CFRP composites using regression analysis. J Alloy Compd 724:1037–1045

    Google Scholar 

  4. Naresh Babu M, Muthukrishnan N (2014) Investigation on surface roughness in abrasive water-jet machining by the response surface method. Mater Manuf Process 29(11–12):1422–1428

    Google Scholar 

  5. Zohourkari I, Zohoor M, Annoni M (2014) Investigation of the effects of machining parameters on material removal rate in abrasive waterjet turning. Adv Mech Eng 6:624203

    Google Scholar 

  6. Jagadish, Bhowmik S, Ray A (2016) Prediction and optimization of process parameters of green composites in AWJM process using response surface methodology. Int J Adv Manuf Technol 87(5–8):1359–1370

    Google Scholar 

  7. Ming Ming IW, Azmi AI, Chuan LC, Mansor AF (2017) Experimental study and empirical analyses of abrasive waterjet machining for hybrid carbon/glass fiber-reinforced composites for improved surface quality. Int J Adv Manuf Technol 95(9–12):3809–3822

    Google Scholar 

  8. Dumbhare PA, Dubey S, Deshpande YV, Andhare AB, Barve PS (2018) Modelling and multi-objective optimization of surface roughness and kerf taper angle in abrasive water jet machining of steel. J Braz Soc Mech Sci Eng 40(5):259

    Google Scholar 

  9. Bañon F, Sambruno A, Batista M, Simonet B, Salguero J (2020) Study of the surface quality of carbon fiber–reinforced thermoplastic matrix composite (CFRTP) machined by abrasive water jet (AWJM). Int J Adv Manuf Technol 107:3299–3313

    Google Scholar 

  10. Aydin G, Karakurt I, Hamzacebi C (2014) Artificial neural network and regression models for performance prediction of abrasive waterjet in rock cutting. Int J Adv Manuf Technol 75(9–12):1321–1330

    Google Scholar 

  11. Abdelnasser ES, Elkaseer A, Nassef A (2016) Abrasive jet machining of glass: Experimental investigation with artificial neural network modelling and genetic algorithm optimisation. Cogent Eng 3(1):1276513. https://doi.org/10.1080/23311916.2016.1276513

    Article  Google Scholar 

  12. Nassef A, Elkaseer A, Abdelnasser ES, Negm M, Qudeiri JA (2018) Abrasive jet drilling of glass sheets: effect and optimisation of process parameters on kerf taper. Adv Mech Eng 10(1):168781401774843

    Google Scholar 

  13. Reddy NS, Tirumala D, Gajjela R, Das R (2018) ANN and RSM approach for modelling and multi objective optimization of abrasive water jet machining process. Decis Sci Lett. https://doi.org/10.5267/j.dsl.2017.11.003

    Article  Google Scholar 

  14. Ke J-H, Tsai F-C, Hung J-C, Yang T-Y, Yan BH (2011) Scrap wafer regeneration by precise abrasive jet machining with novel composite abrasive for design of experiments. Proc Inst Mech Eng Part B J Eng Manuf 225(6):881–890

    Google Scholar 

  15. Kechagias J, Petropoulos G, Vaxevanidis N (2011) Application of Taguchi design for quality characterization of abrasive water jet machining of TRIP sheet steels. Int J Adv Manuf Technol 62(5–8):635–643

    Google Scholar 

  16. Srikanth DV, Rao MS (2015) Application of Taguchi & response surface methodology in optimization for machining of ceramics with abrasive jet machining. Mater Today Proc 2(4–5):3308–3317

    Google Scholar 

  17. Nagendra Prasad K, John Basha D, Varaprasad KC (2017) Experimental investigation and analysis of process parameters in abrasive jet machining of Ti-6al-4V alloy using Taguchi method. Mater Today Proc 4(10):10894–10903

    Google Scholar 

  18. Muthuramalingam T, Vasanth S, Vinothkumar P, Geethapriyan T, Rabik MM (2018) Multi criteria decision making of abrasive flow oriented process parameters in abrasive water jet machining using Taguchi–DEAR methodology. Silicon 10(5):2015–2021

    Google Scholar 

  19. Routara BC, Nanda BK, Sahoo AK, Thatoi DN, Nayak BB (2011) Optimisation of multiple performance characteristics in abrasive jet machining using grey relational analysis. Int J Manuf Technol Manag 24(1/2/3/4):4

    Google Scholar 

  20. Santhanakumar M, Adalarasan R, Rajmohan M (2015) Experimental modelling and analysis in abrasive waterjet cutting of ceramic tiles using grey-based response surface methodology. Arab J Sci Eng 40(11):3299–3311

    Google Scholar 

  21. Nair A, Kumanan S (2018) Optimization of size and form characteristics using multi-objective grey analysis in abrasive water jet drilling of Inconel 617. J Braz Soc Mech Sci Eng 40(3):121

    Google Scholar 

  22. Dhanawade A, Kumar S (2018) Study on carbon epoxy composite surfaces machined by abrasive water jet machining. J Compos Mater. https://doi.org/10.1177/0021998318807278

    Article  Google Scholar 

  23. Thakur RK, Singh KK (2020) Experimental investigation and optimization of abrasive water jet machining parameter on multi-walled carbon nanotube doped epoxy/carbon laminate. Measurement 164:108093

    Google Scholar 

  24. Altin Karataş M, Motorcu AR, Gökkaya H (2020) Optimization of machining parameters for kerf angle and roundness error in abrasive water jet drilling of CFRP composites with different fiber orientation angles. J Braz Soc Mech Sci Eng 42(4):173

    Google Scholar 

  25. Tomy A, Hiremath SS (2020) Machining and characterization of multidirectional hybrid silica glass fiber reinforced composite laminates using abrasive jet machining. Silicon. https://doi.org/10.1007/s12633-020-00504-3

    Article  Google Scholar 

  26. Yue Z, Huang C, Zhu H, Wang J, Yao P, Liu Z (2014) Optimization of machining parameters in the abrasive waterjet turning of alumina ceramic based on the response surface methodology. Int J Adv Manuf Technol 71(9–12):2107–2114

    Google Scholar 

  27. Ibraheem HMA, Iqbal A, Hashemipour M (2014) Numerical optimization of hole making in GFRP composite using abrasive water jet machining process. J Chin Inst Eng 38(1):66–76

    Google Scholar 

  28. Babu MN, Muthukrishnan N (2017) Exploration on Kerf-angle and surface roughness in abrasive waterjet machining using response surface method. J Inst Eng (India) Ser C 99:645–656. https://doi.org/10.1007/s40032-017-0366-x

    Article  Google Scholar 

  29. Jagadeesh B, Dinesh Babu P, Nalla Mohamed M, Marimuthu P (2017) Experimental investigation and optimization of abrasive water jet cutting parameters for the improvement of cut quality in carbon fiber reinforced plastic laminates. J Ind Text 48(1):178–200

    Google Scholar 

  30. Kumar A, Singh H, Kumar V (2017) Study the parametric effect of abrasive water jet machining on surface roughness of Inconel 718 using RSM-BBD techniques. Mater Manuf Processes 33(13):1483–1490

    Google Scholar 

  31. MM IW, Azmi A, Lee C, Mansor A (2016) Kerf taper and delamination damage minimization of FRP hybrid composites under abrasive water-jet machining. Int J Adv Manuf Technol 94(5–8):1727–1744

    Google Scholar 

  32. Ravi Kumar K, Sreebalaji VS, Pridhar T (2018) Characterization and optimization of Abrasive Water Jet Machining parameters of aluminium/tungsten carbide composites. Measurement 117:57–66

    Google Scholar 

  33. Bijeta Nayak B, Abhishek K, Sankar Mahapatra S, Das D (2018) Application of WPCA based Taguchi method for multi-response optimization of abrasive jet machining process. Mater Today Proc 5(2):5138–5144

    Google Scholar 

  34. Nanda BK, Dhupal D, Buda D, Das SR (2018) Abrasive jet drilling of alumina ceramic with pressurized-fluidized bed setup. Mater Today Proc 5(2):12570–12578

    Google Scholar 

  35. Yuvaraj N, Pradeep Kumar M (2014) Multiresponse optimization of abrasive water jet cutting process parameters using TOPSIS approach. Mater Manuf Process 30(7):882–889

    Google Scholar 

  36. Radovanović M (2016) Multi-objective optimization of process performances when cutting carbon steel with abrasive water jet. Tribol Ind 38(4):454–462

    Google Scholar 

  37. Rao VDP, Mrudula M, Geethika VN (2019) Multi-objective optimization of parameters in abrasive water jet machining of carbon-glass fibre-reinforced hybrid composites. J Inst Eng (India) Ser D 100:55–66. https://doi.org/10.1007/s40033-019-00181-6

    Article  Google Scholar 

  38. Nanda BK, Mishra A, Dhupal D (2016) Fluidized bed abrasive jet machining (FB-AJM) of K-99 alumina ceramic using SiC abrasives. Int J Adv Manuf Technol 90(9–12):3655–3672

    Google Scholar 

  39. Aich U, Banerjee S, Bandyopadhyay A, Das PK (2014) Multi-objective optimisation of abrasive water jet machining responses by simulated annealing and particle swarm. Int J Mechatron Manuf Syst 7(1):38

    Google Scholar 

  40. Wakuda M, Yamauchi Y, Kanzaki S (2002) Effect of workpiece properties on machinability in abrasive jet machining of ceramic materials. Precis Eng 26(2):193–198

    Google Scholar 

  41. Seo YW, Ramulu M, Kim D (2003) Machinability of titanium alloy (Ti’6Al’4 V) by abrasive waterjets. Proc Inst Mech Eng Part B J Eng Manuf 217(12):1709–1721

    Google Scholar 

  42. Uthayakumar M, Khan MA, Kumaran ST, Slota A, Zajac J (2015) Machinability of nickel-based superalloy by abrasive water jet machining. Mater Manuf Process 31(13):1733–1739

    Google Scholar 

  43. Unde PD, Gayakwad MD, Patil NG, Pawade RS, Thakur DG, Brahmankar PK (2015) Experimental investigations into abrasive waterjet machining of carbon fiber reinforced plastic. J Compos 2015:1–9

    Google Scholar 

  44. Klichova D, Klich J (2016) Study of the effect of material machinability on quality of surface created by abrasive water jet. Procedia Eng 149:177–182

    Google Scholar 

  45. Prasad SR, Ravindranath K, Devakumar MLS (2018) Experimental investigation and parametric optimization in abrasive jet machining on nickel 233 alloy using WASPAS and MOORA. Cogent Eng 5(1):1–12

    Google Scholar 

  46. Tomy A, Hiremath SS (2019) Machinability and characterisation of machined hole on quartz using developed µ-AJM set-up. Adv Mater Process Technol. https://doi.org/10.1080/2374068x.2018.1564868

    Article  Google Scholar 

  47. Ramesha K, Santhosh N, Kiran K, Manjunath N, Naresh H (2019) Effect of the process parameters on machining of GFRP composites for different conditions of abrasive water suspension jet machining. Arab J Sci Eng 44:7933–7943

    Google Scholar 

  48. Wright DB, Herrington JA (2011) Problematic standard errors and confidence intervals for skewness and kurtosis. Behav Res Methods 43(1):8–17

    Google Scholar 

  49. Jagannatha N, Somashekhar SH, Sadashivappa K, Arun KV (2012) Machining of soda lime glass using abrasive hot air jet: an experimental study. Mach Sci Technol 16(3):459–472

    Google Scholar 

  50. Costa NR, Lourenço J, Pereira ZL (2011) Desirability function approach: a review and performance evaluation in adverse conditions. Chemometr Intell Lab Syst 107(2):234–244

    Google Scholar 

  51. Patel D, Tandon P (2015) Experimental investigations of thermally enhanced abrasive water jet machining of hard-to-machine metals. CIRP J Manuf Sci Technol 10:92–101

    Google Scholar 

Download references

Acknowledgements

This research is supported by Research Promotion Scheme for Research Centres under National Doctoral Fellowship of AICTE, India via. Reference No. 8-32/RIFD/RPS-NDF/Policy-1/2018-19.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sudhansu Ranjan Das.

Additional information

Technical Editor: Adriano Fagali de Souza.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pradhan, S., Das, S.R., Nanda, B.K. et al. Experimental investigation on machining of hardstone quartz with modified AJM using hot silicon carbide abrasives. J Braz. Soc. Mech. Sci. Eng. 42, 559 (2020). https://doi.org/10.1007/s40430-020-02644-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-020-02644-4

Keywords

Navigation