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Modeling and optimization of fused deposition modeling (FDM) process through printing PLA implants using adaptive neuro-fuzzy inference system (ANFIS) model and whale optimization algorithm

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Abstract

Fused deposition modeling (FDM) is a widely used additive manufacturing (AM) technique for developing complex features and geometries within the shortest possible time as per customer needs. Nowadays, the customization of biomedical parts is becoming possible due to the increasing accuracy and enhanced ability of FDM machines to control the process parameters. The control on surface quality of parts produced by FDM process is of prime concern for the researchers, which are induced due to the stair steps on sloping surfaces, and needs addressing. In addition, to meet customer demands rapidly, the FDM part build time must be reduced without much compromising the strength of the parts necessary for specific applications. In the same context, this paper presents the effect and control of FDM process parameters, i.e., layer thickness, raster angle, infill density and internal structure, on surface roughness, build time and compressive strength of developed biomedical implant parts. The face-centered central composite design is employed to consummate the experimental trials, and experimental data are used to establish an adaptive neuro-fuzzy inference system (ANFIS) model for predicting the surface roughness, build time and compressive strength with respect to changes in FDM process parameters. At the end, whale optimization algorithm (WOA) has been applied to minimize surface roughness, minimize build time and maximize compressive strength simultaneously. Then, the optimal solutions obtained from WOA methodology have been compared with ANFIS predicted results. The results reveal that ANFIS-WOA methodology provides optimal combination of FDM process parameters accurately.

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Abbreviations

AM:

Additive manufacturing

3D:

Three dimensional

SLS:

Selective laser sintering

LOM:

Laminated object manufacturing

SLA:

Stereolithography

FDM:

Fused deposition modeling

CAD:

Computer-aided design

ABS:

Acrylonitrile butadiene styrene

DoE:

Design of experiment

GA:

Genetic algorithm

PLA:

Polylactic acid

ANN:

Artificial neural network

ANFIS:

Adaptive neuro-fuzzy inference system

WOA:

Whale optimization algorithm

UTM:

Universal testing machine

RMSE:

Root mean square error

SR:

Surface roughness

BT:

Build time

CS:

Compressive strength

RSM:

Response surface methodology

NSGA:

Non-sorted genetic algorithm

References

  1. Masood SH (2014) Introduction to advances in additive manufacturing and tooling. In: Hashmi S (ed) Comprehensive materials processing. Science Direct Elsevier, New York, p 12

    Google Scholar 

  2. Wiedemann B, Jantzen HA (1999) Strategies and applications for rapid product and process development in Daimler-Benz AG. Comput Ind 39(1):11–25

    Article  Google Scholar 

  3. Rochus P, Plesseria JY, Van Elsen M, Kruth JP, Carrus R, Dormal T (2007) New applications of rapid prototyping and rapid manufacturing (RP/RM) technologies for space instrumentation. Acta Astronaut 61(1–6):352–359

    Article  Google Scholar 

  4. Colombo G, Filippi S, Rizzi C, Rotini F (2010) A new design paradigm for the development of custom-fit soft sockets for lower limb prostheses. Comput Ind 61(6):513–523

    Article  Google Scholar 

  5. Wohlers T (2014) 3D printing and additive manufacturing state of the industry. Annual worldwide progress report, Wohlers Associates. https://www.wohlersassociates.com/state-of-the-industry-reports.html. Assessed 10 Oct 2020

  6. 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):1382

    Article  Google Scholar 

  7. Oropallo W, Piegl LA (2016) Ten challenges in 3D printing. Eng Comput 32(1):135–148

    Article  Google Scholar 

  8. Pandey PM, Reddy NV, Dhande SG (2003) Slicing procedures in layered manufacturing: a review. Rapid Prototyp J 9(5):274–288

    Article  Google Scholar 

  9. Wu W, Geng P, Li G, Zhao D, Zhang H, Zhao J (2015) Influence of layer thickness and raster angle on the mechanical properties of 3D-printed PEEK and a comparative mechanical study between PEEK and ABS. Materials 8(9):5834–5846

    Article  Google Scholar 

  10. Rocha CR, Perez ART, Roberson DA, Shemelya CM, MacDonald E, Wicker RB (2014) Novel ABS-based binary and ternary polymer blends for material extrusion 3D printing. J Mater Res 29(17):1859–1866

    Article  Google Scholar 

  11. Kuo CC, Liu LC, Teng WF, Chang HY, Chien FM, Liao SJ et al (2016) Preparation of starch/acrylonitrile-butadiene-styrene copolymers (ABS) biomass alloys and their feasible evaluation for 3D printing applications. Compos Part B: Eng 86:36–39

    Article  Google Scholar 

  12. Mohan N, Senthil P, Vinodh S, Jayanth N (2017) A review on composite materials and process parameters optimisation for the fused deposition modelling process. Virtual Phys Prototyp 12(1):47–59

    Article  Google Scholar 

  13. Mohamed OA, Masood SH, Bhowmik JL (2015) Optimization of fused deposition modeling process parameters: a review of current research and future prospects. Adv Manuf 3(1):42–53

    Article  Google Scholar 

  14. Levy GN, Schindel R, Kruth JP (2003) Rapid manufacturing and rapid tooling with layer manufacturing (LM) technologies, state of the art and future perspectives. CIRP Ann 52(2):589–609

    Article  Google Scholar 

  15. Pilipović A, Raos P, Šercer M (2009) Experimental analysis of properties of materials for rapid prototyping. Int J Adv Manuf Technol 40(1–2):105–115

    Article  Google Scholar 

  16. Tofail SA, Koumoulos EP, Bandyopadhyay A, Bose S, O’Donoghue L, Charitidis C (2018) Additive manufacturing: scientific and technological challenges, market uptake and opportunities. Mater Today 21(1):22–37

    Article  Google Scholar 

  17. Anitha R, Arunachalam S, Radhakrishnan P (2001) Critical parameters influencing the quality of prototypes in fused deposition modelling. J Mater Process Technol 118(1–3):385–388

    Article  Google Scholar 

  18. Ahn SH, Montero M, Odell D, Roundy S, Wright PK (2002) Anisotropic material properties of fused deposition modeling ABS. Rapid Prototyp J 8(4):248–257

    Article  Google Scholar 

  19. Thrimurthulu KPPM, Pandey PM, Reddy NV (2004) Optimum part deposition orientation in fused deposition modeling. Int J Mach Tools Manuf 44(6):585–594

    Article  Google Scholar 

  20. Chockalingam K, Jawahar N, Praveen J (2016) Enhancement of anisotropic strength of fused deposited ABS parts by genetic algorithm. Mater Manuf Process 31(15):2001–2010

    Article  Google Scholar 

  21. Chin Ang K, Fai Leong K, Kai Chua C, Chandrasekaran M (2006) Investigation of the mechanical properties and porosity relationships in fused deposition modelling-fabricated porous structures. Rapid Prototyp J 12(2):100–105

    Article  Google Scholar 

  22. Horvath D, Noorani R, Mendelson M (2007) Improvement of surface roughness on ABS 400 polymer using design of experiments (DOE). In: Materials science forum, vol 561. Trans Tech Publications, pp 2389–2392

  23. Raghunath N, Pandey PM (2007) Improving accuracy through shrinkage modelling by using Taguchi method in selective laser sintering. Int J Mach Tools Manuf 47(6):985–995

    Article  Google Scholar 

  24. Zhang Y, Chou K (2008) A parametric study of part distortions in fused deposition modelling using three-dimensional finite element analysis. Proc Inst Mech Eng, Part B: J Eng Manuf 222(8):959–968

    Article  Google Scholar 

  25. Sood AK, Ohdar RK, Mahapatra SS (2009) Improving dimensional accuracy of fused deposition modelling processed part using grey Taguchi method. Mater Des 30(10):4243–4252

    Article  Google Scholar 

  26. Nancharaiah T (2011) Optimization of process parameters in FDM process using design of experiments. Int J Emerg Technol 2(1):100–102

    Google Scholar 

  27. Rianmora S, Koomsap P (2010) Recommended slicing positions for adaptive direct slicing by image processing technique. Int J Adv Manuf Technol 46(9–12):1021–1033

    Article  Google Scholar 

  28. Sood AK, Ohdar RK, Mahapatra SS (2010) Parametric appraisal of mechanical property of fused deposition modelling processed parts. Mater Des 31(1):287–295

    Article  Google Scholar 

  29. Phatak AM, Pande SS (2012) Optimum part orientation in rapid prototyping using genetic algorithm. J Manuf Syst 31(4):395–402

    Article  Google Scholar 

  30. Zhang J, Peng A (2012) Processing parameter optimization of FDM based on robust design. Trans Nanjing Univ Aeronaut Astronaut 29(1):62–67

    Google Scholar 

  31. Sahu RK, Mahapatra SS, Sood AK (2013) A study on dimensional accuracy of fused deposition modeling (FDM) processed parts using fuzzy logic. J Manuf Sci Prod 13(3):183–197

    Google Scholar 

  32. Gurrala PK, Regalla SP (2014) Part strength evolution with bonding between filaments in fused deposition modelling: This paper studies how coalescence of filaments contributes to the strength of final FDM part. Virtual Phys Prototyp 9(3):141–149

    Article  Google Scholar 

  33. Boschetto A, Bottini L (2014) Accuracy prediction in fused deposition modeling. Int J Adv Manuf Technol 73(5–8):913–928

    Article  Google Scholar 

  34. Jin Y, Wan Y, Zhang B, Liu Z (2017) Modeling of the chemical finishing process for polylactic acid parts in fused deposition modeling and investigation of its tensile properties. J Mater Process Technol 240:233–239

    Article  Google Scholar 

  35. Altan M, Eryildiz M, Gumus B, Kahraman Y (2018) Effects of process parameters on the quality of PLA products fabricated by fused deposition modeling (FDM): surface roughness and tensile strength. Mater Test 60(5):471–477

    Article  Google Scholar 

  36. Tickoo S (2011) CATIA V5R20 for engineers and designers (Indian Edition). CADCIM Technologies, Schereville, USA. Dreamtech Press, ISBN 978-93-5004-063-8

  37. Cura U (2018) Advanced 3D printing software made accessible. https://www.ultimaker.com/en/products/ultimaker-cura-software. Accessed 10 June 2020

  38. Ultimaker BV (2018) Ultimaker. https://ultimaker.com/3d-printers. Accessed 2 Jan 2020

  39. Panaitescu DM, Frone AN, Chiulan I (2016) Nanostructured biocomposites from aliphatic polyesters and bacterial cellulose. Ind Crops Prod 93:251–266

    Article  Google Scholar 

  40. Wu W, Ye W, Wu Z, Geng P, Wang Y, Zhao J (2017) Influence of layer thickness, raster angle, deformation temperature and recovery temperature on the shape-memory effect of 3D-printed polylactic acid samples. Materials 10(8):970

    Article  Google Scholar 

  41. Tyler B, Gullotti D, Mangraviti A, Utsuki T, Brem H (2016) Polylactic acid (PLA) controlled delivery carriers for biomedical applications. Adv Drug Deliv Rev 107:163–175

    Article  Google Scholar 

  42. ISO, E. (1997) 4287–Geometrical Product specifications (GPS)—surface texture: profile method–terms, definitions and surface texture parameters. International Organization for Standardization, Geneva

    Google Scholar 

  43. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  44. Kumar R, Hynes NRJ (2019) Prediction and optimization of surface roughness in thermal drilling using integrated ANFIS and GA approach. Eng Sci Technol, Int J 23(1):30–41

    Google Scholar 

  45. Farah S, Anderson DG, Langer R (2016) Physical and mechanical properties of PLA, and their functions in widespread applications—a comprehensive review. Adv Drug Deliv Rev 107:367–392

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur, Rajasthan, 303007, India

    Tarun Sai, Vimal Kumar Pathak & Ashish Kumar Srivastava

Authors
  1. Tarun Sai
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  2. Vimal Kumar Pathak
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  3. Ashish Kumar Srivastava
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Correspondence to Vimal Kumar Pathak.

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Technical Editor: Lincoln Cardoso Brandao.

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Sai, T., Pathak, V.K. & Srivastava, A. Modeling and optimization of fused deposition modeling (FDM) process through printing PLA implants using adaptive neuro-fuzzy inference system (ANFIS) model and whale optimization algorithm. J Braz. Soc. Mech. Sci. Eng. 42, 617 (2020). https://doi.org/10.1007/s40430-020-02699-3

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