Skip to main content

Advertisement

SpringerLink
Log in

Prediction and optimization of process parameters of green composites in AWJM process using response surface methodology

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The objective of this paper is to develop a response surface methodology (RSM)-based optimization design for process parameter optimization of abrasive water jet machining (AWJM) process on machining of green composites. The experiments are performed based on the Box-Behnken design, and most optimal parameters are selected using multi-response optimization through desirability. The machining parameters are pressure within the pumping system (PwPS), stand-off distance (SoD), and nozzle speed (NS). The corresponding response parameters that have been identified are surface roughness (Ra) and process time (PT). Additionally, the significance of the developed optimization design has been identified using analysis of variance (ANOVA). Finally, the validity and adequacy of the developed model are done through confirmation tests. The numerical result shows that the optimum process parameters obtained are PwPS (150 MPa), SoD (3.5 mm), and NS (125 mm/min), and also the percentage error in prediction of response parameters is reasonable and comparable with the experimental results. The proposed design can be used as a systematic framework for parameter optimization in environmentally conscious manufacturing processes.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chandramohan D (2014) Studies on natural fiber particle reinforced composite material for conservation of natural resources. Adv in Applied Sci Rese 5(2):305–315

    Google Scholar 

  2. La-Mantia FP, Morreale M (2011) Green composites: a brief review. Compos Part-A 42:579–588

    Article  Google Scholar 

  3. Georgios K, Arlindo S, Mihail F (2013) Green composites: a review of adequate materials for automotive applications. Compos Part-B 44:120–127

    Article  Google Scholar 

  4. Chauhan A, Chauhan P, Kaith B (2012) Natural fiber reinforced composite: a concise review article. Chem Eng Process Technol 3(2):1–3

    Google Scholar 

  5. Joseph K, Thomas S, Pavithran C (1996) Effect of chemical treatment on the tensile properties of short sisal fibre-reinforced polyethylene composites. Polymer 37:5139–5149

    Article  Google Scholar 

  6. Joshi SV, Drzal LT, Mohanty AK, Arora S (2004) Are natural fiber composites environmentally superior to glass fiber reinforced composites. Compos Part-A 35:371–376

    Article  Google Scholar 

  7. Selke SE, Wichman I (2004) Wood fiber/polyolefin composites. Compos Part-A 35:321–326

    Article  Google Scholar 

  8. Gachter R, Muller H (1990) Plastics additives, 3rd ed. Hanser Publishers.

  9. Satyanarayana KG, Sukumaran K, Kulkarni AG, Pillai SGK, Rohatgi PK (1986) Fabrication and properties of natural fiber-reinforced polyester composites. Composites 17(4):329–333

    Article  Google Scholar 

  10. Markarian J (2002) Additive developments aid growth in wood-plastic composites. Plast Addit and Compounding 4:18–21

    Article  Google Scholar 

  11. Pritchard G (2004) Two technologies merge: wood-plastic composites. Plast Addit and Compounding 6:18–21

    Article  Google Scholar 

  12. Oksman K, Selin JF (2004) Plastics and composites from polylactic acid. In: Wallenberger FT, Weston NE (eds) Natural fibers, plastics and composites, vol 1. Kluwer Academic Press, Norwell

    Google Scholar 

  13. Gomes A, Matsuo T, Goda K, Ohgi J (2007) Development and effect of alkali treatment on tensile properties of curaua fiber green composites. Compos Part-A-Appl Sci and Manuf 38(8):1811–1820

    Article  Google Scholar 

  14. Luo S, Netravali AN (1999) Mechanical and thermal properties of environment-friendly “green” composites made from pineapple leaf fibers and poly(hydroxybutyrate-co-valerate) resin. Polym Compos 20(3):367–378

    Article  Google Scholar 

  15. Takagi H, Ichihara Y (2004) Effect of fiber length on mechanical properties of green composites using a starch-based resin and short bamboo fibers. Japan Soci Mech Eng Int J Series-A 47(4):551–555

    Google Scholar 

  16. Prasad B, Sain M (2003) Mechanical properties of thermally treated hemp fibers in inert atmosphere for potential composite reinforcement. Mater Rese Innov 7(4):231–238

    Article  Google Scholar 

  17. Nourbakhsh A, Ashori A, Ziaei-Tabari H, Rezaei F (2010) Mechanical and thermochemical properties of wood-flour polypropylene blends. Polym Bull 65(7):691–700

    Article  Google Scholar 

  18. Nourbakhsha A, Baghlani FF, Ashori A (2011) Nano-SiO2 filled rice husk/polypropylene composites: physico-mechanical properties. Ind Crop Prod 33:183–187

    Article  Google Scholar 

  19. Pothana LA, Oommenb Z, Thomas S (2003) Dynamic mechanical analysis of banana fiber reinforced polyester composites. Compos Sci Technol 63(2):283–293

    Article  Google Scholar 

  20. Schneider JP, Karmaker AC (1996) Mechanical performance of short jute fiber reinforced polypropylene. J Mater Sci Letters 15:201–202

    Article  Google Scholar 

  21. Bhosale SB, Pawade RS, Brahmankar PK (2014) Effect of process parameters on MRR, TWR and surface topography in ultra-sonic machining of alumina–zirconia ceramic composite. Ceramics Int 40:12831–12836

    Article  Google Scholar 

  22. Shahrajabian H, Farahnakian M (2013) Modeling and multi-constrained optimization in drilling process of carbon fiber reinforced epoxy composite. Int J Precision Eng and Manuf 14(10):1829–1837

    Article  Google Scholar 

  23. Liu D, Tang YJ, Cong WL (2012) A review of mechanical drilling for composite laminates. Compos Struct 94:1265–1279

    Article  Google Scholar 

  24. Palanikumara K, Davim JP (2009) Assessment of some factors influencing tool wear on the machining of glass fibre-reinforced plastics by coated cemented carbide tools. J Mater Process Technol 209:511–519

    Article  Google Scholar 

  25. Chandramohan D, Marimuthu K (2010) Thrust force and torque in drilling the natural fiber reinforced polymer composite materials and evaluation of delamination factor for bone graft substitutes—a work of fiction approach. Int J Eng Sci and Techno 2(10):6437–6451

    Google Scholar 

  26. Komanduri R, Zhang B, Vissa CM (1991) Machining of fibre reinforced composites. ASME Process Manuf Comp Mater 49(27):1–36

    Google Scholar 

  27. Weller EJ (1984) Non-traditional machining processes, SME. Dearborn: 15–71.

  28. Benedict GF (1987) Non-traditional manufacturing processes, Marcel Decker Inc., New York 2(3):67–86.

  29. Boothroyed G, Knight WA (1989) Fundamentals of machining and machine tools. Marcel Decker Inc., New York, pp 468–469

    Google Scholar 

  30. Zampaloni M, Pourboghrat F, Yankovich SA (2007) Kenaf natural fiber—a discussion on manufacturing problems and solutions. Compos Part-A 38(6):1569–1580

    Article  Google Scholar 

  31. Ramkumar J, Malhotra SK, Krishnamurthy R (2004) Effect of workpiece vibration on drilling of GFRP laminates. J Mater Process Technol 152:329–332

    Article  Google Scholar 

  32. Konig W, Rummenholler S (1993) Technological and industrial safety aspects in milling FRP. ASME Mach Adv Comp 45(66):1–14

    Google Scholar 

  33. Sivapirakasam SP, Mathew J, Surianarayanan M (2011) Multi-attribute decision making for green electrical discharge machining. Expert Syst Appl 38(7):8370–8374

    Article  Google Scholar 

  34. Bradford JD, Richardson DB (1980) Production engineering technology, 3rd edn. Macmillan, London, pp 74–93

    Book  Google Scholar 

  35. Hashish M (1991) Advances in composite machining with abrasive-waterjets. Process Manuf Comp Mater 49(27):93–111

    Google Scholar 

  36. Momber AW, Kovacevic R (1992) Principles of abrasive water jet machining, 1st edn. Springer, London, pp 214–255

    MATH  Google Scholar 

  37. Xu S, Wang J (2005) A study of abrasive waterjet cutting of alumina ceramics with controlled nozzle oscillation. Int J Adv Manuf Technol 27:693–702

    Article  Google Scholar 

  38. Wang J, Wong WCK (1999) A study of waterjet cutting of metallic coated sheet steels. Int J Mach Tools Manuf 39:855–870

    Article  Google Scholar 

  39. Tsai FC, Yan BH, Kuan CY, Huang FY (2008) A Taguchi and experimental investigation into the optimal processing conditions for the abrasive jet polishing of SKD61 mold steel. Int J Mach Tool Manuf 48:932–945

    Article  Google Scholar 

  40. 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 doi. doi:10.1007/s00170-011-3815-3

    Google Scholar 

  41. Srikantha DV, Sreenivasa Rao M (2014) Metal removal and Kerf analysis in abrasive jet drilling of glass sheets. Procedia Mater Sci 6:1303–1311

    Article  Google Scholar 

  42. Azmir MA, Ahsan AK (2008) Investigation on glass/epoxy composite surfaces machined by abrasive water jet machining. J Mater Process Technol 198:122–128

    Article  Google Scholar 

  43. Caydas U, Hascalik A (2008) A study on surface roughness in abrasive water jet machining process using artificial neural networks and regression analysis method. J Mater Process Technol 202:574–582

    Article  Google Scholar 

  44. Srinivasu DS, Ramesh Babu N (2008) A neuro-genetic approach for selection of process parameters in abrasive waterjet cutting considering variation in diameter of focusing nozzle. Appl Soft Compu 8:809–819

    Article  Google Scholar 

  45. Zain AM, Haron H, Sharif S (2011) Estimation of the minimum machining performance in the abrasive waterjet machining using integrated ANN-SA. Expert Syst Appl 38:8316–8326

    Article  Google Scholar 

  46. Zain AM, Haron H, Sharif S (2011) Optimization of process parameters in the abrasive waterjet machining using integrated SA–GA. Appl Soft Comput 11:5350–5359

    Article  Google Scholar 

  47. Jegaraj JJR, Babu NR (2007) A soft computing approach for controlling the quality of cut with abrasive waterjet cutting system experiencing orifice and focusing tube wear. J Mater Process Technol 185:217–227

    Article  Google Scholar 

  48. Iqbal A, Dar NU, Hussain G (2011) Optimization of abrasive water jet cutting of ductile materials. J Wuhan Univ Technol Mater Sci Ed 26(1):88–92

    Article  Google Scholar 

  49. Sharma V, Chattopadhyaya S, Hloch S (2011) Multi response optimization of process parameters based on Taguchi-fuzzy model for coal cutting by water jet technology. Int J Adv Manuf Technol 56:1019–1025

    Article  Google Scholar 

  50. 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 71:2107–2114

    Article  Google Scholar 

  51. Vundavilli PR, Parappagoudar MB, Kodali SP, Benguluri S (2012) Fuzzy logic-based expert system for prediction of depth of cut in abrasive water jet machining process. Knowl Based Syst 27:456–464

    Article  Google Scholar 

  52. Sevil EH, Oysal Y (2015) Estimation of cutting speed in abrasive water jet using an adaptive wavelet neural network. J Intell Manuf 26:403–413

    Article  Google Scholar 

  53. Yusup N, Sarkheyli A, Zain A, Hashim SZM, Ithnin N (2014) Estimation of optimal machining control parameters using artificial bee colony. J Intell Manuf 25:1463–1472

    Article  Google Scholar 

  54. 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 40:3299–3311

    Article  Google Scholar 

  55. Jagadish RA (2014) Optimization of process parameters of green electrical discharge machining using principle component analysis (PCA). Int J Adv Manuf Technol doi. doi:10.1007/s00170-014-6372-8

    Google Scholar 

  56. Jagadish RA (2014) Multi-objective optimization of green EDM: an integrated theory. J Inst Eng India Series-C 96(1):41–47

    Article  Google Scholar 

  57. Jagadish RA (2015) A fuzzy multi-criteria decision making model for green electrical discharge machining. Adv Int Sys Compu doi. doi:10.1007/978-81-322-2217-0_4

    Google Scholar 

  58. Box GEP, Behnken DW (1960) Some new three level designs for the study of quantitative variables. Technometrics 2:455–475

    Article  MathSciNet  MATH  Google Scholar 

  59. Oktem H, Erzurumlu T, Kurtaran H (2005) Application of response surface methodology in the optimization of cutting conditions for surface roughness. J Mater Process Technol 170:11–16

    Article  Google Scholar 

  60. Ramulu M, Arola D (1994) The influence of abrasive waterjet cutting conditions on the surface quality of graphite/epoxy laminates. Int J Mach Tools Manuf 34(3):295–313

    Article  Google Scholar 

  61. Minitab14 (2003) Minitab user manual release 14. State College, PA, USA

  62. Prabhu S, Uma M, Vinayagam BK (2015) Surface roughness prediction using Taguchi-fuzzy logic-neural for network analysis for CNT nanofluids based grinding process. Neural Comput Appl 26:41–55

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology, Silchar, 788010, Assam, India

    Jagadish & Sumit Bhowmik

  2. Department of Training and placement, Jalpaiguri Government Engineering College, Jalpaiguri, 735102, West Bengal, India

    Amitava Ray

Authors
  1. Jagadish
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sumit Bhowmik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amitava Ray
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jagadish.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jagadish, Bhowmik, S. & Ray, A. Prediction and optimization of process parameters of green composites in AWJM process using response surface methodology. Int J Adv Manuf Technol 87, 1359–1370 (2016). https://doi.org/10.1007/s00170-015-8281-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-015-8281-x

Keywords

Search

Navigation