Parameter Optimization Of Fused Deposition Modelling Under Ambient Temperature Control

Fused Deposition Modelling (FDM) is one of the most popular RP techniques available in the market. However, there is still limitation in terms of FDM performance such as surface roughness and dimensional accuracy. Creation of a part with good surface roughness and dimensional accuracy is critical as...

Full description

Saved in:

Bibliographic Details
Main Author:	Harun, Nurul Hatiqah
Format:	Thesis
Language:	English English
Published:	2019
Subjects:	T Technology (General) TS Manufactures
Online Access:	http://eprints.utem.edu.my/id/eprint/24675/1/Parameter%20Optimization%20Of%20Fused%20Deposition%20Modelling%20Under%20Ambient%20Temperature%20Control.pdf http://eprints.utem.edu.my/id/eprint/24675/2/Parameter%20Optimization%20Of%20Fused%20Deposition%20Modelling%20Under%20Ambient%20Temperature%20Control.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utem-ep.24675
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Kasim, Mohd Shahir
topic	T Technology (General) TS Manufactures
spellingShingle	T Technology (General) TS Manufactures Harun, Nurul Hatiqah Parameter Optimization Of Fused Deposition Modelling Under Ambient Temperature Control
description	Fused Deposition Modelling (FDM) is one of the most popular RP techniques available in the market. However, there is still limitation in terms of FDM performance such as surface roughness and dimensional accuracy. Creation of a part with good surface roughness and dimensional accuracy is critical as it can affect the part accuracy, post-processing cost and functionality of the parts. Based on the literature review, it was found that studies on the effect of ambient temperature in improving the surface roughness and dimensional accuracy of FDM built parts have been limited to certain extend. Environmental factors such as temperature and relative humidity have been believed to be the sources of error affecting surface finish and dimensional accuracy. Besides, temperature fluctuations during production also believed could lead to delamination and higher surface roughness. Therefore, this research study aims to investigate the effect of parameter variables such as ambient temperature, layer thickness and part angle to the samples fabricated by using FDM machine. The response surface methodology (RSM) was employed by using historical data in the experiment to determine the significant factors and their interactions on the FDM performance. Three levels manipulated factors namely ambient temperature (30°C, 45°C, 60°C), layer thickness (0.178 mm, 0.267 mm, 0.356 mm) and part angle (22.5°, 45°, 67,5°) have been studied. A total of 29 numbers of experiments had been conducted including two replications at center point. The results showed that all the parameter variables have significant effects on the part surface roughness and dimensional accuracy. Layer thickness are the most dominant factors affecting the surface roughness. Meanwhile, ambient temperature was the most dominant in determining part dimensional accuracy. The responses of various factors had been illustrated in diagnostic plot and interaction graph. Besides, the results also had been illustrated in further surface roughness and cross-sectional sample analysis. The optimum parameter required for minimum surface roughness and dimensional accuracy was at ambient temperature 30.01°C, layer thickness 0.18 mm and part angle 67.38°. The optimization has produced the maximum productivity with RaH 2.78 µm, RaV 12.38 µm and RaS 10.92 µm. Meanwhile, dimensional accuracy height 3.2%, length 2.1%, width 3.7% and angle 0.39°.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Harun, Nurul Hatiqah
author_facet	Harun, Nurul Hatiqah
author_sort	Harun, Nurul Hatiqah
title	Parameter Optimization Of Fused Deposition Modelling Under Ambient Temperature Control
title_short	Parameter Optimization Of Fused Deposition Modelling Under Ambient Temperature Control
title_full	Parameter Optimization Of Fused Deposition Modelling Under Ambient Temperature Control
title_fullStr	Parameter Optimization Of Fused Deposition Modelling Under Ambient Temperature Control
title_full_unstemmed	Parameter Optimization Of Fused Deposition Modelling Under Ambient Temperature Control
title_sort	parameter optimization of fused deposition modelling under ambient temperature control
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Manufacturing Engineering
publishDate	2019
url	http://eprints.utem.edu.my/id/eprint/24675/1/Parameter%20Optimization%20Of%20Fused%20Deposition%20Modelling%20Under%20Ambient%20Temperature%20Control.pdf http://eprints.utem.edu.my/id/eprint/24675/2/Parameter%20Optimization%20Of%20Fused%20Deposition%20Modelling%20Under%20Ambient%20Temperature%20Control.pdf
_version_	1747834085518082048
spelling	my-utem-ep.246752021-10-05T11:01:15Z Parameter Optimization Of Fused Deposition Modelling Under Ambient Temperature Control 2019 Harun, Nurul Hatiqah T Technology (General) TS Manufactures Fused Deposition Modelling (FDM) is one of the most popular RP techniques available in the market. However, there is still limitation in terms of FDM performance such as surface roughness and dimensional accuracy. Creation of a part with good surface roughness and dimensional accuracy is critical as it can affect the part accuracy, post-processing cost and functionality of the parts. Based on the literature review, it was found that studies on the effect of ambient temperature in improving the surface roughness and dimensional accuracy of FDM built parts have been limited to certain extend. Environmental factors such as temperature and relative humidity have been believed to be the sources of error affecting surface finish and dimensional accuracy. Besides, temperature fluctuations during production also believed could lead to delamination and higher surface roughness. Therefore, this research study aims to investigate the effect of parameter variables such as ambient temperature, layer thickness and part angle to the samples fabricated by using FDM machine. The response surface methodology (RSM) was employed by using historical data in the experiment to determine the significant factors and their interactions on the FDM performance. Three levels manipulated factors namely ambient temperature (30°C, 45°C, 60°C), layer thickness (0.178 mm, 0.267 mm, 0.356 mm) and part angle (22.5°, 45°, 67,5°) have been studied. A total of 29 numbers of experiments had been conducted including two replications at center point. The results showed that all the parameter variables have significant effects on the part surface roughness and dimensional accuracy. Layer thickness are the most dominant factors affecting the surface roughness. Meanwhile, ambient temperature was the most dominant in determining part dimensional accuracy. The responses of various factors had been illustrated in diagnostic plot and interaction graph. Besides, the results also had been illustrated in further surface roughness and cross-sectional sample analysis. The optimum parameter required for minimum surface roughness and dimensional accuracy was at ambient temperature 30.01°C, layer thickness 0.18 mm and part angle 67.38°. The optimization has produced the maximum productivity with RaH 2.78 µm, RaV 12.38 µm and RaS 10.92 µm. Meanwhile, dimensional accuracy height 3.2%, length 2.1%, width 3.7% and angle 0.39°. 2019 Thesis http://eprints.utem.edu.my/id/eprint/24675/ http://eprints.utem.edu.my/id/eprint/24675/1/Parameter%20Optimization%20Of%20Fused%20Deposition%20Modelling%20Under%20Ambient%20Temperature%20Control.pdf text en public http://eprints.utem.edu.my/id/eprint/24675/2/Parameter%20Optimization%20Of%20Fused%20Deposition%20Modelling%20Under%20Ambient%20Temperature%20Control.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=116867 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Manufacturing Engineering Kasim, Mohd Shahir 1. Agarwala, M.K., Jamalabad, V.R., Langrana, N. a., Safari, A., Whalen, P.J., and Danforth, S.C., 1996. Structural Quality of Parts Processed by Fused Deposition. Rapid Prototyping Journal, 2(4), pp. 4–19. 2. Akande, S.O., 2015. Dimensional Accuracy and Surface Finish Optimization of Fused Deposition Modelling Parts Using Desirability Function Analysis. International Journal of Engineering Research and Technology (IJERT), 4(04), pp. 196–202. 3. Alhubail, M., 2012. Statistical-Based Optimization Of Process Parameters Of Fused Deposition Modeling For Improved Quality. 4. Anderson, M.J., and Whitcomb, P.J., 2007. DOE Simplified: Practical Tools for Effective Experimentation, Second Edition. 5. Angrish, A., 2014. A Critical Analysis of Additive Manufacturing Technologies for Aerospace Applications. In: 2014 IEEE Aerospace Conference, pp. 1–6. 6. Anitha, R., Arunachalam, S., and Radhakarishnan, P., 2009. Experimental Study Aiming to Enhance the Surface Finish of Fused Deposition Modeled Parts. CIRP Annals - Manufacturing Technology, 58(1), pp. 189–192. 7. Anitha, R., Arunachalam, S., and Radhakrishnan, P., 2001. Critical Parameters Influencing the Quality of Prototypes in Fused Deposition Modelling. Journal of Materials Processing Technology, 118(1–3), pp. 385–388. 8. Antony, J., 2003. Design of Experiments for Engineers and Scientists. Materials & Mechanical, (October). 9. Azari, A., and Nikzad, S., 2009. The Evolution of Rapid Prototyping in Dentistry: A Review. Rapid Prototyping Journal, 15(3), pp. 216–225. 10. Bakar, N.S.A., Alkahari, M.R., and Boejang, H., 2010. Analysis on Fused Deposition Modelling Performance. Journal of Zhejiang University-SCIENCE A, 11(12), pp. 972–977. 11. Bewick, V., Cheek, L., and Ball, J., 2004. Statistics Review 9: One-Way Analysis of Variance. Critical Care (London, England), 8(2), pp. 130–163. 12. Bharath Vasudevarao, B., Dharma Prakash Natarajan, D.P.., and Henderson, M., 2000. Sensitivitiy of RP Surface Finish to Process Parameter Variation. Solid Freeform Fabrication Proceedings, pp. 251–258. 13. Boejang, H., Basar, M.F.M., and Yahya, M.S., 2013. Time Compression Technologies For Engineering Technology. 14. Boschetto, A., and Bottini, L., 2015. Roughness Prediction in Coupled Operations of Fused Deposition Modeling and Barrel Finishing. Journal of Materials Processing Technology, 219, pp. 181–192. 15. Boschetto, A., Giordano, V., and Veniali, F., 2013. Surface Roughness Prediction in Fused Deposition Modelling by Neural Networks. International Journal of Advanced Manufacturing Technology, 67(9–12), pp. 2727–2742. 16. Box3d, 2017. Warping and Cracking with Closed Environment FDM3D Printers. [online] Available at: https://box3d.eu/warping-cracking-closed-environment-3d-printers/ [Accessed on 10 June 2018]. 17. Bual, G.S., 2014. Methods to Improve Surface Finish of Parts Produced by Fused Deposition Modeling. Manufacturing Science and Technology, 2(3), pp. 51–55. 18. Campbell, R.I., Martorelli, M., and Lee, H.S., 2002. Surface Roughness Visualisation for Rapid Prototyping Models. CAD Computer Aided Design, 34(10), pp. 717–725. 19. Chua, C.K., Leong, K.F., and Lim, C.S., 2003. Rapid Prototyping: Principles and Applications. Building. 20. Deepa, P., 2014. Fused Deposition Modeling – A Rapid Prototyping Technique for Product Cycle Time Reduction Cost Effectively in Aerospace Applications. IOSR Journal of Mechanical and Civil Engineering, 5, pp. 62–68. 21. Filaments.ca, 2016. Starter Printer & Temperature Guide. [online] Available at: https:// filaments.ca/pages/temperature-guide [Accessed on 8 April 2018]. 22. Forrest W. Breyfogle, I., 2003. Implementing Six Sigma: Smarter Solutions Using Statistical Methods, 2nd Edition. 23. Gajdoš, I., and Slota, J., 2013. Influence of Printing Conditions on Structure in FDM Prototypes. Technical Gazette, 20(2), pp. 231–236. 24. Galantucci, L.M., Lavecchia, F., and Percoco, G., 2009. Experimental Study Aiming to Enhance the Surface Finish of Fused Deposition Modeled Parts. CIRP Annals - Manufacturing Technology, 58(1), pp. 189–192. 25. Górski, F., Kuczko, W., and Wichniarek, R., 2013. Influence of Process Parameters on Dimensional Accuracy of Parts Manufactured Using Fused Deposition Modelling Technology. Advances in Science and Technology – Research Journal, 7(19), pp. 27–35. 26. Grieser, F., 2016. PLA vs ABS: Filaments Explained & Compared. All3dp.com. [online] Available at: https://all3dp.com/1/pla-filament-3d-printing/ [Accessed on 14 April 2018]. 27. Hills, R., and Trucano, T., 1999. Statistical Validation of Engineering and Scientific Models: Background. Sandia National Laboratories, SAND99-1256, (May), pp. 1–84. 28. Hopkinson, N., Hague, R., and Dickens, P., 2006. Rapid Manufacturing: An Industrial Revolution for the Digital Age. 29. Instruments, M., 2013. Precision Cutter. [online] Available at: https://www.metkon.com/Products-Categories/1/132/Precision%20Cutting/ [Accessed on 11 November 2017]. 30. Ivanova, O., Williams, C., and Campbell, T., 2013. Additive Manufacturing (AM) and Nanotechnology: Promises and Challenges. Rapid Prototyping Journal, 19(4), pp. 353–364. 31. Jain, P., and Kuthe, A.M., 2013. Feasibility Study of Manufacturing Using Rapid Prototyping: FDM Approach. In: Procedia Engineering, 63, pp. 4–11. 32. Jin, Y., Wan, Y., Zhang, B., and Liu, Z., 2016. Modeling of the Chemical Finishing Process for Polylactic Acid Parts in Fused Deposition Modeling and Investigation of Its Tensile Properties. Journal of Materials Processing Tech., 240, pp. 233–239. 33. Kai, C.C., Meng, C.S., Ching, L.S., Hoe, E.K., and Fah, L.K., 1998. Rapid Prototyping Assisted Surgery Planning. The International Journal of Advanced Manufacturing Technology, 14(9), pp. 624–630. 34. Kheirollahi, H., and Abbaszadeh, F., 2011. Application of Rapid Prototyping Technology in Dentistry. International Journal of Rapid Manufacturing, 2(1/2), pp. 104–120. 35. Krishna, N.J., 2014. Improving the Surface Roughness of FDM Parts By Using Hybrid Methods. International Journal of Engineering Research & Technology (IJERT), 3(12), pp. 650–654. 36. Kumar, A., and Ghafoor, H., 2016. Rapid Prototyping : A Future in Orthodontics. Journal of Orthodontic Research \|, 4, pp. 1–7. 37. Lawson, J.S., and Erjavec, J., 2000. Modern Statistics for Engineering and Quality Improvement. 38. Levy, G.N., Schindel, R., and Kruth, J.P., 2003. Rapid Manufacturing and Rapid Tooling with Layer Manufacturing Technologies, State of the Art and Future Perspectives. CIRP Annals, 52(2), pp. 589–609. 39. Liu, Q., Leu, M.C., and Schmitt, S.M., 2006. Rapid Prototyping in Dentistry: Technology and Application. International Journal of Advanced Manufacturing Technology, 29(3–4), pp. 317–335. 40. LulzBot, 2014. 3D Printing Filament Guide. [online] Available at: https://devel.lulzbot.com/filament/Archieve/LulzBot_3D_Printing_Filament_Guide.pdf [Accessed on 8 February 2018]. 41. Madhav, V.N. V, and Daule, R., 2013. Rapid Prototyping and Its Application in Dentistry. Journal of Dental and Allied Sciences, 2(2), pp. 57–61. 42. Mahindru, D. V., and Mahendru, P., 2013. Review of Rapid Prototyping-Technology for the Future. Global Journal Of Computer Science And Technology Graphics and Vision, 13(4), pp. 27–38. 43. Mohamed, O. a., Masood, S.H., and Bhowmik, J.L., 2015. Optimization of Fused Deposition Modeling Process Parameters: A Review of Current Research and Future Prospects. Advances in Manufacturing, (February 2015), pp. 42–53. 44. Mohd Halidi, S.N.A., and Abdullah, J., 2012. Moisture and Humidity Effects on the ABS Used in Fused Deposition Modeling Machine. Advanced Materials Research, 576, pp. 641–644. 45. Montgomery, D.C., 2009. Design and Analysis of Experiments, 7th Edition. John Wiley & Sons. 46. Montgomery, D.C., Myers, R.H., and Anderson-Cook, C., 2009. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Wiley Series in Probability and Statistics, (June 2015), pp. 704-715. 47. Nancharaiah, T., Raju, D.R., and Raju, V.R., 2010. An Experimental Investigation on Surface Quality and Dimensional Accuracy of FDM Components. International Journal on Emerging Technologies, 1(2), pp. 106–111. 48. Negi, S., Dhiman, S., and Sharma, R.K., 2014. Basics and Applications of Rapid Prototyping Medical Models. Rapid Prototyping Journal, 20(3), pp. 256–267. 49. Novakova-Marcincinova, L., 2012. Application of Fused Deposition Modeling Technology in 3D Printing Rapid Prototyping Area. Manufacturing and Industrial Engineering Journal, 11(4), pp. 35–37. 50. Onagoruwa, S., Bose, S., and Bandyopadhyay, A., 2001. Fused Deposition of Ceramics (FDC) and Composites. British Library Conference Proceedings, pp. 224–231. 51. Onuh, S.O., and Hon, K.K.B., 2001. Integration of Rapid Prototyping Technology into FMS for Agile Manufacturing. Integrated Manufacturing Systems, 12(3), pp. 179–186. 52. Pal, D., and Ravi, B., 2007. Rapid Tooling Route Selection and Evaluation for Sand and Investment Casting. Virtual and Physical Prototyping, 2(4), pp. 197–207. 53. Pandey, P.M., 2010. Rapid Prototyping Technologies, Applications and Part Deposition Planning. Journal of Materials Processing Technology, pp. 15–29. 54. Pandey, P.M., Venkata Reddy, N., and Dhande, S.G., 2003. Improvement of Surface Finish by Staircase Machining in Fused Deposition Modeling. Journal of Materials Processing Technology, 132(1–3), pp. 323–331. 55. Peng, A., Xiao, X., and Yue, R., 2014. Process Parameter Optimization for Fused Deposition Modeling Using Response Surface Methodology Combined with Fuzzy Inference System. The International Journal of Advanced Manufacturing Technology, 73, pp. 87–100. 56. Rajan Bansal, 2011. Improving Dimensional Accuracy Of Fused Deposition Modeling (FDM) Parts Using Response Surface Methodology. 57. Rao, R.V., 2011. Advanced Modeling and Optimization of Manufacturing Processes: International Research and Development. Springer Series in Advanced Manufacturing. 58. Raol, T.S., Dave, K.G., Patel, D.B., and Talati, V.N., 2014. An Experimental Investigation of Effect of Process Parameters on Surface Roughness of Fused Deposition Modeling Built Parts. International Journal of Engineering and Technology (IJERT), 3(4), pp. 2270–2274. 59. Reddy, N.S.K., and Rao, P. V., 2005. Selection of Optimum Tool Geometry and Cutting Conditions Using a Surface Roughness Prediction Model for End Milling. The International Journal of Advanced Manufacturing Technology, 26, pp. 1202–1210. 60. Rosochowski, A., and Matuszak, A., 2000. Rapid Tooling: The State of the Art. Journal of Materials Processing Technology, 106(1–3), pp. 191–198. 61. Shojib Hossain, M., Espalin, D., Ramos, J., Perez, M., and Wicker, R., 2014. Improved Mechanical Properties of Fused Deposition Modeling-Manufactured Parts Through Build Parameter Modifications. Journal of Manufacturing Science and Engineering, 136(6), pp. 15-29. 62. Silva, D.N., Gerhardt de Oliveira, M., Meurer, E., Meurer, M.I., Lopes da Silva, J.V., and Santa-Bárbara, A., 2008. Dimensional Error in Selective Laser Sintering and 3D-Printing of Models for Craniomaxillary Anatomy Reconstruction. Journal of Cranio-Maxillofacial Surgery, 36(8), pp. 443–449. 63. Singh, S., and Singh, R., 2016. Fused Deposition Modelling Based Rapid Patterns for Investment Casting Applications: A Review. Rapid Prototyping Journal, 22(1), pp. 123–143. 64. Sood, A.K., Ohdar, R.K., and Mahapatra, S.S., 2009. Improving Dimensional Accuracy of Fused Deposition Modelling Processed Part Using Grey Taguchi Method. Materials and Design, 30(10), pp. 4243–4252. 65. Sood, A.K., Ohdar, R.K., and Mahapatra, S.S., 2012. Experimental Investigation and Empirical Modelling of FDM Process for Compressive Strength Improvement. Journal of Advanced Research, 3(1), pp. 81–90. 66. Srivastava, M., Maheshwari, S., Kundra, T.K., and Rathee, S., 2017. ScienceDirect Multi-Response Optimization of Fused Deposition Modelling Process Parameters of ABS Using Response Surface Methodology ( RSM ) -Based Desirability Analysis. Materials Today: Proceedings, 4(2), pp. 1972–1977. 67. Stratasys, 2016a. FDM Technology. [online] Available at: https://www.stratasys.com/fdm-technology [Accessed on 10 October 2017]. 68. Stratasys, 2016b. Investment Casting with FDM Tooling. [online] Available at: https://www.stratasys.com/mx/aplicaciones/additive-manufacturing/manufacturing-tooling/investment-casting [Accessed on 10 October 2017]. 69. Stratasys Ltd, 2017. Consumer Goods Case Studies. [online] Available at: https://www.stratasys.com/br/resources/search/case-studies/unilever [Accessed on 12 November 2017]. 70. Taşcioǧlu, S., and Akar, N., 2003. A Novel Alternative to the Additives in Investment Casting Pattern Wax Compositions. Materials and Design, 24(8), pp. 693–698. 71. Thinker Space, 2015. ABS 3D Printer. Thinker Space Malaysia. [online] Available at: https://store.thinkerspace.my/pla-abs-spool/19-abs-3d-printer.html [Accessed on 15 November 2017]. 72. Thrimurthulu, K., Pandey, P.M., and Venkata Reddy, N., 2004. Optimum Part Deposition Orientation in Fused Deposition Modeling. International Journal of Machine Tools and Manufacture, 44(6), pp. 585–594. 73. Torabi, K., Farjood, E., and Hamedani, S., 2015. Rapid Prototyping Technologies and Their Applications in Prosthodontics, a Review of Literature. Journal of Dentistry (Shīrāz, Iran), 16(1), pp. 1–9. 74. Vashishtha, V.K., 2011. Advancement of Rapid Prototyping in Aerospace Industry -a Review. International Journal of Engineering Science and Technology, 3(3), pp. 286–293. 75. Vijay, P., Danaiah, P., and D. Rajesh, K. V., 2012. Critical Parameters Effecting the Rapid Prototyping Surface Finish. Journal of Mechanical Engineering and Automation, 1(1), pp. 17–20. 76. WhiteClouds, 2013. Toxicity of ABS Plastic in 3D Printing. WhiteClouds Inc. [online] Available at: https:ss.whiteclouds.com/blog/toxicity-abs-plastics-3d-printing [Accessed on 12 February 2018]. 77. Wiedemann, B., and Jantzen, H.-A., 1999. Strategies and Applications for Rapid Product and Process Development in Daimler-Benz AG. Computers in Industry, 39(1), pp. 11–25. 78. Wohlers, T., and Gornet, T., 2014. History of Additive Manufacturing. Wohlers Report 2014 - 3D Printing and Additive Manufacturing State of the Industry, pp. 1–34. 79. Yang, K., and El-haik, B., 2003. Design for Six Sigma A Roadmap for Product Development. McGraw Hill Professional. 80. Zhang, J.Z., Chen, J.C., and Kirby, E.D., 2007. Surface Roughness Optimization in an End-Milling Operation Using the Taguchi Design Method. Journal of Materials Processing Technology, 184(1–3), pp. 233–239.

Parameter Optimization Of Fused Deposition Modelling Under Ambient Temperature Control

Similar Items