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Optimization and reliability analysis to improve surface quality and mechanical characteristics of heat-treated fused filament fabricated parts

  • Sunpreet Singh
  • Manjeet Singh
  • Chander Prakash
  • Munish Kumar Gupta
  • Mozammel MiaEmail author
  • Rupinder Singh
ORIGINAL ARTICLE
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Abstract

Fused filament fabrication (FFF), an economic additive manufacturing (AM) method, is largely used for the fabrication of customized components (of medical, engineering, architectural, toy, artistic, etc. industries). However, the poor mechanical and surface properties are critical barriers limiting the growth of FFF. Therefore, a novel heat treatment approach has been utilized to improve the overall performance of printed parts. The parts were made with acrylonitrile-butadiene-styrene (ABS) with three infill densities (20, 60, and 100%) and annealing was carried out by changing the levels of temperature (105, 115, and 125 °C) and time duration (20, 25, and 30 min). The experimental design was conducted by Taguchi orthogonal array while the optimization was conducted using Taguchi S/N approach. The investigated responses were surface roughness, hardness, dimensional accuracy, tensile strength, flexural strength, and impact strength. Moreover, the reliability of the mechanical properties, with higher error (α > 5%), was verified by using the Weibull statistic to determine the survival rate of annealed FFF parts for functional applications. The adopted annealing approach was found to improve the physical and mechanical properties. The SEM analysis of fractured specimens revealed the type of failure (ductile or brittle). In recapitulation, the annealing process improved the quality characteristics of FFF parts.

Keywords

Annealing Optimization Fused filament fabrication Heat treatment Weibull statistic 

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Notes

Funding information

Prof. Rupinder Singh received funding from the Science and Engineering Research Board (DST), New Delhi (India), under the Extramural Research Award (EMR/2014/001209).

References

  1. 1.
    Gao W, Zhang Y, Ramanujan D, Ramani K, Chen Y, Williams CB, Zavattieri PD (2015) The status, challenges, and future of additive manufacturing in engineering. Comput Aided Des 69:65–89CrossRefGoogle Scholar
  2. 2.
    Gibson I, Rosen D, Stucker B (2014) Additive manufacturing technologies: 3D printing, rapid prototyping, and direct digital manufacturing. Springer, BerlinGoogle Scholar
  3. 3.
    Thompson MK, Moroni G, Vaneker T, Fadel G, Campbell RI, Gibson I, Bernard A, Schulz J, Graf P, Ahuja B, Martina F (2016) Design for additive manufacturing: trends, opportunities, considerations, and constraints. CIRP Ann Manuf Technol 65:737–760CrossRefGoogle Scholar
  4. 4.
    Stansbury JW, Idacavage MJ (2016) 3D printing with polymers: challenges among expanding options and opportunities. Dent Mater 32:54–64CrossRefGoogle Scholar
  5. 5.
    Maulvi FA, Shah MJ, Solanki BS, Patel AS, Soni TG, Shah DO (2007) Application of 3D printing technology in the development of novel drug delivery systems. Int J Drug Dev and Res 9:44–49Google Scholar
  6. 6.
    Frazier WE (2014) Metal additive manufacturing: a review. J Mater Eng Perform 23:1917–1928CrossRefGoogle Scholar
  7. 7.
    Studart AR (2016) Additive manufacturing of biologically-inspired materials. Chem Soc Rev 45:359–376CrossRefGoogle Scholar
  8. 8.
    Singh S, Ramakrishna S, Singh R (2017) Material issues in additive manufacturing: a review. J Manuf Process 25:185–200CrossRefGoogle Scholar
  9. 9.
    Turner B, Strong RA, Gold S (2014) A review of melt extrusion additive manufacturing processes: I. Process design and modeling. Rapid Prototyp J 20:192–204CrossRefGoogle Scholar
  10. 10.
    Calleja A, Tabernero I, Ealo JA, Campa FJ, Lamikiz A, de Lacalle LN (2014) Feed rate calculation algorithm for the homogeneous material deposition of blisk blades by 5-axis laser cladding. Int J Adv Manuf Technol 74:1219–1228CrossRefGoogle Scholar
  11. 11.
    Calleja A, Tabernero I, Ealo JA, Campa FJ, Lamikiz A, de Lacalle LN (2014) Improvement of strategies and parameters for multi-axis laser cladding operations. Opt Lasers Eng 56:113–120CrossRefGoogle Scholar
  12. 12.
    Garg A, Bhattacharya A, Batish A (2016) On surface finish and dimensional accuracy of FDM parts after cold vapor treatment. Mater Manuf Process 31:522–529CrossRefGoogle Scholar
  13. 13.
    Singh S, Singh N, Gupta M, Prakash C, Singh R (2018) Mechanical feasibility of ABS/HIPS-based multi-material structures primed by low-cost polymer printer. Rapid Prototyp J.  https://doi.org/10.1108/RPJ-01-2018-0028
  14. 14.
    Anitha R, Arunachalam S, Radhakrishnan P (2001) Critical parameters influencing the quality of prototypes in fused deposition modelling. J Mater Process Technol 118:385–388CrossRefGoogle Scholar
  15. 15.
    Armillotta A (2006) Assessment of surface quality on textured FDM prototypes. Rapid Prototyp J 12:35–41CrossRefGoogle Scholar
  16. 16.
    Panda SK, Padhee S, Sood AK, Mahapatra SS (2009) Optimization of fused deposition modelling (FDM) process parameters using bacterial foraging technique. Intell Inf Manag 30:89Google Scholar
  17. 17.
    Mohamed OA, Masood SH, Bhowmik JL (2017) Investigation on the flexural creep stiffness behavior of PC–ABS material processed by fused deposition modeling using response surface definitive screening design. JOM 69:498–505CrossRefGoogle Scholar
  18. 18.
    Torres J, Cotelo J, Karl J, Gordon AP (2015) Mechanical property optimization of FDM PLA in shear with multiple objectives. JOM 67:1183–1193CrossRefGoogle Scholar
  19. 19.
    Basavaraj CK, Vishwas M (2016) Studies on effect of fused deposition modelling process parameters on ultimate tensile strength and dimensional accuracy of nylon. In IOP Conference Series: Materials Science and Engineering 149:012035Google Scholar
  20. 20.
    Griffiths CA, Howarth J, De Almeida-Rowbotham G, Rees A, Kerton R (2009) A design of experiments approach for the optimisation of energy and waste during the production of parts manufactured by 3D printing. J Clean Prod 139:74–85CrossRefGoogle Scholar
  21. 21.
    Bikas H, Stavropoulos P, Chryssolouris G (2016) Additive manufacturing methods and modelling approaches: a critical review. Int J Adv Manuf Technol 83:389–405CrossRefGoogle Scholar
  22. 22.
    Sweeney CB, Green MJ, Saed M (2014) Microwave-induced localized heating of CNT filled polymer composites for enhanced inter-bead diffusive bonding of fused filament fabricated parts. Patent No. WO 2015130401-A3Google Scholar
  23. 23.
    Shaffer S, Yang K, Vargas J, Di Prima MA, Voit W (2014) On reducing anisotropy in 3D printed polymers via ionizing radiation. Polymer 55:5969–5979CrossRefGoogle Scholar
  24. 24.
    Kim BK, Yoon LK, Xie XM (1997) Effects of annealing in ABS ternary blends. J Appl Polym Sci 66:1531–1542CrossRefGoogle Scholar
  25. 25.
    Gao H, Kaweesa DV, Moore J, Meisel NA Investigating the impact of acetone vapor smoothing on the strength and elongation of printed ABS parts. JOM 2009, 69:580–585Google Scholar
  26. 26.
    Singh R, Singh S, Singh IP (2016) Effect of hot vapor smoothing process on surface hardness of fused deposition modeling parts. 3D PRINTING 3:128–133CrossRefGoogle Scholar
  27. 27.
    Singh R, Singh S, Fraternali F (2016) Development of in-house composite wire based feed stock filaments of fused deposition modelling for wear-resistant materials and structures. Compos Part B 98:244–249CrossRefGoogle Scholar
  28. 28.
    Galantucci LM, Lavecchia F, Percoco G (2009) Experimental study aiming to enhance the surface finish of fused deposition modeled parts. CIRP Ann Manuf Technol 58:189–192CrossRefGoogle Scholar
  29. 29.
    Galantucci LM, Lavecchia F, Percoco G (2010) Quantitative analysis of a chemical treatment to reduce roughness of parts fabricated using fused deposition modeling. CIRP Ann Manuf Technol 59:247–250CrossRefGoogle Scholar
  30. 30.
    Amirali L, Janeteas C, Barari A (2018) Surface roughness of FDM parts after post-processing with acetone vapor bath smoothing process. Int J Adv Manuf Technol 95:1505–1520CrossRefGoogle Scholar
  31. 31.
    Sergio P, Sorgente D, Percoco G (2017) Enhancing the sustainability of chemical vapour polishing of additive manufactured ABS parts using a vacuum chamber. Rapid Prototyp J 23:1043–1050CrossRefGoogle Scholar
  32. 32.
    Khoa NT, Lee BK (2018) Post-processing of FDM parts to improve surface and thermal properties. Rapid Prototyp J 24:1091–1100CrossRefGoogle Scholar
  33. 33.
    Singh S, Singh R (2016) Effect of annealing on surface roughness of additively manufactured plastic parts: a case study. National Conference on Production Engineering (COPE-2016), Oct. 07–08, Guru Nanak Dev Engineering College, LudhianaGoogle Scholar
  34. 34.
    ASTM D638-14 (2014) Standard test method for tensile properties of plastics. ASTM International, West ConshohockenGoogle Scholar
  35. 35.
    ASTM D790-17 (2017) Standard test methods for flexural properties of unreinforced and reinforced plastics and electrical insulating materials. ASTM International, West ConshohockenGoogle Scholar
  36. 36.
    ASTM D6110-18 (2018) Standard test method for determining the charpy impact resistance of notched specimens of plastics. ASTM International, West ConshohockenGoogle Scholar
  37. 37.
    Pérez M, Medina-Sánchez G, García-Collado A, Gupta M, Carou D (2018) Surface quality enhancement of fused deposition modeling (FDM) printed samples based on the selection of critical printing parameters. Materials 11(8):1382CrossRefGoogle Scholar
  38. 38.
    Kim GD, Oh YT (2008) A benchmark study on rapid prototyping processes and machines: quantitative comparisons of mechanical properties, accuracy, roughness, speed, and material cost. Proc Inst Mech Eng B J Eng Manuf 222:201–215CrossRefGoogle Scholar
  39. 39.
    Singh R, Gupta MK (2017) Experimental investigations for modelling hardness of ABS replica based investment castings. Proc Natl Acad Sci India Sect A: Phys Sci 1–11Google Scholar
  40. 40.
    Raju M, Gupta MK, Bhanot N, Sharma VS (2018) A hybrid PSO–BFO evolutionary algorithm for optimization of fused deposition modelling process parameters. J Intell Manuf 2018:1–16Google Scholar
  41. 41.
    Naresh K, Shankar K, Velmurugan R (2018) Reliability analysis of tensile strengths using Weibull distribution in glass/epoxy and carbon/epoxy composites. Compos Part B 133:129–144CrossRefGoogle Scholar
  42. 42.
    Keleş Ö, Keleş Ö, Blevins CW, Blevins CW, Bowman KJ, Bowman KJ (2017) Effect of build orientation on the mechanical reliability of 3D printed ABS. Rapid Prototyp J 23:320–328CrossRefGoogle Scholar
  43. 43.
    An ZL, Chen T, Cheng DL, Chen TH, Wang ZY (2017) Statistical analysis and prediction on tensile strength of 316L-SS joints at high temperature based on Weibull distribution. In IOP Conference Series: Materials Science and Engineering 281:012062Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Sunpreet Singh
    • 1
  • Manjeet Singh
    • 1
  • Chander Prakash
    • 1
  • Munish Kumar Gupta
    • 2
    • 3
  • Mozammel Mia
    • 4
    Email author
  • Rupinder Singh
    • 5
  1. 1.School of Mechanical EngineeringLPUPhagwaraIndia
  2. 2.Mechanical Engineering DepartmentNITHamirpurIndia
  3. 3.University Center for Research & DevelopmentChandigarh UniversityMohaliIndia
  4. 4.Mechanical and Production EngineeringAhsanullah University of Science and TechnologyDhakaBangladesh
  5. 5.Department of Production EngineeringGuru Nanak Dev Engineering CollegeLudhianaIndia

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