Skip to main content
Log in

Experimental Investigations and Parametric Optimization of Process Parameters on Shrinkage Characteristics of Selective Inhibition Sintered High Density Polyethylene Parts

  • Published:
Experimental Techniques Aims and scope Submit manuscript

Abstract

Fabrication of functional prototypes from computer-aided design data through joining polymer powders particles is accomplished using selective inhibition sintering (SIS) process. The dimensional accuracy of sintered specimens in SIS process is significantly affected for the materials such as polymer and inhibitor, complex geometry and process parameters. Increasing dimensional accuracy in SIS process improves the quality and functional ability of end-use components. The present work investigates the shrinkage characteristics of SIS parts with reference to various process parameters such as thickness of layer, heater energy and its feedrate, and inhibitor nozzle feedrate. The test specimens are fabricated using the developed SIS system. Experimental study and mathematical modelling is accomplished based on statistical box-behnken response surface methodology. The result of analysis of variance (ANOVA) revealed that the layer thickness followed by printer feedrate and heater feedrate are the dominating variables on shrinkage. The optimal operating conditions of selected process variables to reduce shrinkage is presumed using desirability approach. The results revealed that the settings of low layer thickness and high heater energy with medium heater feedrate and medium printer feedrate is beneficial for improving dimensional stability of sintered specimen. Furthermore, the effect of these parameters on shrinkage are evaluated by conducting sensitivity analysis.

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

Similar content being viewed by others

References

  1. ASTM F2792-12a (2012) Standard terminology for additive manufacturing technologies (withdrawn 2015). ASTM International, West Conshohocken

    Google Scholar 

  2. Pham DT, Demov SS (2001) Rapid manufacturing: the technologies and applications of rapid prototyping and rapid tooling. Springer-Verlag, London

    Book  Google Scholar 

  3. Chua CK, Leong KF (1998) Rapid prototyping: principles and applications in manufacturing. John Wiley and Sons Inc., Singapore

    Google Scholar 

  4. Chua CK, Leong KF, Lim CS (2003) Rapid prototyping: principles and applications. World Scientific, Singapore

    Book  Google Scholar 

  5. Averyanova M, Cicala E, Bertrand PH, Grevey G (2012) Experimental design approach to optimize selective laser melting of martensitic 17-4 PH powder: part I – single laser tracks and first layer. Rapid Prototyp J 18(1):28–37

    Article  Google Scholar 

  6. Bikas H, Stavropoulos P, Chryssolouris G (2016) Additive manufacturing methods and modelling approaches: a critical review. Int J Adv Manuf Technol 83:389–405

    Article  Google Scholar 

  7. Khoshnevis B, Asiabanpour B, Mojdeh M, Palmer K (2003) SIS – a new SFF method based on powder sintering. Rapid Prototyp J 9(1):30–36

    Article  Google Scholar 

  8. Khoshnevis B, Yoozbashizadeh M, Chen Y (2012) Metallic part fabrication using selective inhibition sintering (SIS). Rapid Prototyp J 18(2):144–153

    Article  Google Scholar 

  9. Rosochowski A, Matuszak A (2000) Rapid tooling- the state of art. J Mater Process Technol 106(1–3):191–198

    Article  Google Scholar 

  10. Mahapatra SS, Sood AK (2012) Bayesian regularization-based Levenberg–Marquardt neural model combined with BFOA for improving surface finish of FDM processed part. Int J Adv Manuf Technol 60:1223–1235

    Article  Google Scholar 

  11. Sachdeva A, Singh S, Sharma VS (2013) Investigating surface roughness of parts produced by SLS process. Int J Adv Manuf Technol 64:1505–1516

    Article  Google Scholar 

  12. Sood AK, Ohdar RK, Mahapatra SS (2012) Experimental investigation and empirical modelling of FDM process for compressive strength improvement. J Adv Res 3:81–90

    Article  CAS  Google Scholar 

  13. Rayegani F, Onwubolu GC (2014) Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE). Int J Adv Manuf Technol 73:509–519

    Article  Google Scholar 

  14. Rajamani D, Esakki B (2017) Examining mechanical strength characteristics of selective inhibition sintered HDPE specimens using RSM and desirability approach. IOP Conf Ser: Mater Sci Eng 234:012002

    Article  Google Scholar 

  15. Esakki B, Rajamani D, Arunkumar P (2017) Modeling and prediction of optimal process parameters in wear behaviour of selective inhibition sintered high density polyethylene parts. Prog Addit Manuf. https://doi.org/10.1007/s40964-017-0033-z

    Article  Google Scholar 

  16. Karapatis NP, Van Griethuysen JPS, Glardon R (1998) Direct rapid tooling: a review of current research. Rapid Prototyp J 4(2):77–89

    Article  Google Scholar 

  17. Singh S, Sharma VS, Sachdeva A (2012) Optimization and analysis of shrinkage in selective laser sintered polyamide parts. Mater Manuf Process 27:707–714

    Article  CAS  Google Scholar 

  18. Sood AK, Ohdar RK, Mahapatra SS (2009) Improving dimensional accuracy of fused deposition modelling processed part using grey Taguchi method. Mater Des 30:4243–4252

    Article  CAS  Google Scholar 

  19. Negi S, Sharma RK (2016) Study on shrinkage behaviour of laser sintered PA 3200GF specimens using RSM and ANN. Rapid Prototyp J 22(4):645–659

    Article  Google Scholar 

  20. Senthilkumaran K, Pandey PM, Rao PVM (2009) Influence of building strategies on the accuracy of parts in selective laser sintering. Mater Des 30:2946–2954

    Article  CAS  Google Scholar 

  21. Wang RJ, Wang L, Zhao L, Liu Z (2007) Influence of process parameters on part shrinkage in SLS. Int J Adv Manuf Technol 33:498–504

    Article  Google Scholar 

  22. Raghunath N, Pandey PM (2007) Improving accuracy through shrinkage modeling by using taguchi method in selective laser sintering. Int J Mach Tool Manu 47(6):985–995

    Article  Google Scholar 

  23. Hopkinson N, Sercombe TB (2008) Process repeatability and sources of error in indirect SLS of aluminium. Rapid Prototyp J 14(2):108–113

    Article  Google Scholar 

  24. Ning Y, Wong YS, Fuh JYH, Loh HT (2006) An approach to minimize build errors in direct metal laser sintering. IEEE T Autom Sci Eng 3(1):73–80

    Article  Google Scholar 

  25. Shi Y, Li Z, Huang S, Zeng F (2004) Effects of the properties of the polymer materials on the quality of selective laser sintering. Proc Inst Mech Eng Pt L J Mater Des Appl 218(3):247–252

    CAS  Google Scholar 

  26. Asiabanpour B, Khoshnevis B, Palmer K (2006) Advancements in the selective inhibition sintering process development. Virtual Phys Prototyp 1(1):43–52

    Article  Google Scholar 

  27. Joseph J, Muthukumaran S (2017) Optimization of activated TIG welding parameters for improving weld joint strength of AISI 4135 PM steel by genetic algorithm and simulated annealing. Int J Adv Manuf Technol 93(1–4):23–34

    Article  Google Scholar 

  28. Myers RH, Montgomery DC (1995) Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons, Inc., New York

    Google Scholar 

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

    Article  Google Scholar 

  30. Rajmohan T, Palanikumar K (2013) Modeling and analysis of performances in drilling hybrid metal matrix composites using D-optimal design. Int J Adv Manuf Technol 64:1249–1261

    Article  Google Scholar 

  31. Ogorkiewicz RM (1970) Engineering properties of thermoplastics. Wiley & Sons Ltd.

  32. Childs THC, Tontowi AE (2001) Selective laser sintering of a crystalline and a glass-filled crystalline polymer: experiments and simulations. P I Mech Eng B J Eng Manuf 215:1481–1495

    Article  Google Scholar 

  33. Derringer G, Suich R (1980) Simultaneous optimization of several response variables. J Qual Technol 12:214–219

    Article  Google Scholar 

  34. Tamilarasan A, Rajamani D (2017) Multi-response optimization of Nd: YAG laser cutting parameters of Ti-6Al-4V superalloy sheet. J Mech Sci Technol 31(2):813–821

    Article  Google Scholar 

  35. Kim IS, Son KJ, Yang YS, Yaragada PKDV (2003) Sensitivity analysis for process parameters in GMA welding processes using a factorial design method. Int J Mach Tool Manu 43(8):763–769

    Article  Google Scholar 

  36. Lakshminarayanan AK, Balasubramanian V (2009) Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints. T Nonferr Metal Soc 19(1):9–18

    Article  CAS  Google Scholar 

  37. Joardar H, Das NS, Sutradhar G, Singh S (2014) Application of response surface methodology for determining cutting force model in turning of LM6/SiCP metal matrix composite. Measurement 47:452–464

    Article  Google Scholar 

Download references

Acknowledgements

The funding support from Armament Research Board (ARMREB), Defence Research and Development Organization (DRDO), Government of India is thankfully acknowledged. (ARMREB/MAA/2015/167).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to D. Rajamani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rajamani, D., Balasubramanian, E., Arunkumar, P. et al. Experimental Investigations and Parametric Optimization of Process Parameters on Shrinkage Characteristics of Selective Inhibition Sintered High Density Polyethylene Parts. Exp Tech 42, 631–644 (2018). https://doi.org/10.1007/s40799-018-0286-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40799-018-0286-6

Keywords

Navigation