Skip to main content
Log in

Effect of Speed, Acceleration, and Jerk on Surface Roughness of FDM-Fabricated Parts

  • Original Research Article
  • Published:
Journal of Materials Engineering and Performance Aims and scope Submit manuscript

Abstract

FDM is the world's most widely used and certified additive manufacturing technology because of its simplicity of use and production flexibility compared to other additive manufacturing techniques. Several input process variables impact the surface quality of manufactured components. The present study investigated the effect of print speed, acceleration, and jerk on the surface roughness of FDM-fabricated parts. The surface roughness of PLA-fabricated items is minimized by optimizing all three input parameters. To investigate the effect of input variables on surface roughness, a total of 20 test specimens were developed and fabricated using a face-centered central composite design technique. The surface roughness has been measured on the lateral sides of components in two directions (along the x-axes and y-axes). Further, the hybrid genetic algorithms-artificial neural networks (GA-ANN) heuristic optimization tool, in which GA is integrated with ANN, is employed to find the best feasible combination of input variables. It is observed that the surface roughness obtained for the x-axis direction is 0.0551512 μm at (speed: 20 mm/s; acceleration: 1137 mm/s2; jerk: 29.36 mm/s) and for the y-axis direction is 11.8919 μm at (speed: 98.60 mm/s; acceleration: 988.35 mm/s2; jerk: 16.508 mm/s). The optimized values are validated experimentally.

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
Fig. 10

Similar content being viewed by others

References

  1. N. Shahrubudin, T.C. Lee, and R.J.P.M. Ramlan, An Overview on 3D Printing Technology: Technological, Materials, and Applications, Procedia Manuf., 2019, 35, p 1286–1296.

    Article  Google Scholar 

  2. J.Y. Lee, J. An, and C.K. Chua, Fundamentals and Applications of 3D Printing for Novel Materials, Appl. Mater. Today, 2017, 7, p 120–133.

    Article  Google Scholar 

  3. K. V. Wong and A. Hernandez, A Review of Additive Manufacturing, Int. Sch. Res. Notices, 2012.

  4. W.E. Frazier, Metal Additive Manufacturing: A Review, J. Mater. Eng. Perform., 2014, 23, p 1917–1928.

    Article  CAS  Google Scholar 

  5. R. Asthana, A. Kumar, and N.B. Dahotre, Coatings and Surface Engineering, Mater. Process. Manuf. Sci., 2006, 65(5), p 313–395.

    Google Scholar 

  6. S. Deshwal, A. Kumar, and D. Chhabra, Exercising Hybrid Statistical Tools GA-RSM, GA-ANN and GA-ANFIS to Optimize FDM Process Parameters for Tensile Strength Improvement, CIRP J. Manuf. Sci. Technol., 2020, 31, p 189–199.

    Article  Google Scholar 

  7. G. Ćwikła, C. Grabowik, K. Kalinowski, I. Paprocka, and P. Ociepka, The Influence of Printing Parameters on Selected Mechanical Properties of FDM/FFF 3D-Printed Parts, In IOP Conference Series: Materials Science and Engineering, 2017, 227(1), p 012033). IOP Publishing.

  8. M. Samykano, S.K. Selvamani, K. Kadirgama, W.K. Ngui, G. Kanagaraj, and K. Sudhakar, Mechanical Property of FDM Printed ABS: Influence of Printing Parameters, Int. J. Adv. Manuf. Technol., 2019, 102, p 2779–2796.

    Article  Google Scholar 

  9. M.D. Vasilescu and I.V. Groza, Influence of Technological Parameters on the Roughness and Dimension of Flat Parts Generated by FDM 3D Printing, Revista de Tehnologii Neconventionale, 2017, 21(3), p 18–23.

    Google Scholar 

  10. L. Yang, S. Li, Y. Li, M. Yang, and Q. Yuan, Experimental Investigations for Optimizing the Extrusion Parameters on FDM PLA Printed Parts, J. Mater. Eng. Perform., 2019, 28, p 169–182.

    Article  CAS  Google Scholar 

  11. N. VinothBabu, N. Venkateshwaran, N. Rajini, S.O. Ismail, F. Mohammad, H.A. Al-Lohedan, and S. Suchart, Influence of Slicing Parameters on Surface Quality and Mechanical Properties of 3D-Printed CF/PLA Composites Fabricated by FDM Technique, Mater. Technol., 2022, 37(9), p 1008–1025.

    Article  CAS  Google Scholar 

  12. N. Rajesh, G. Guru Mahesh, and P. Venkataramaiah, Study of Machining Parameters on Tensile Strength and Surface Roughness of ABS Samples Printed by FDM, Adv. Mater. Process Technol., 2021, p 1-13.

  13. M. Shirmohammadi, S.J. Goushchi, and P.M. Keshtiban, Optimization of 3D Printing Process Parameters to Minimize Surface Roughness with Hybrid Artificial Neural Network Model and Particle Swarm Algorithm, Prog. Addit. Manuf., 2021, 6, p 199–215.

    Article  Google Scholar 

  14. P. Sammaiah, K. Rushmamanisha, N. Praveenadevi, and I. R. Reddy, The Influence of Process Parameters on the Surface Roughness of The 3d Printed Part in FDM Process. In IOP Conference Series: Materials Science and Engineering, 2020, 981(4), p 42021). IOP Publishing.

  15. M.S. Saad, A.M. Nor, M.E. Baharudin, M.Z. Zakaria, and A.F. Aiman, Optimization of Surface Roughness in FDM 3D Printer Using Response Surface Methodology, Particle Swarm Optimization, and Symbiotic Organism Search Algorithms, Int. J. Adv. Manuf. Technol, 2019, 105, p 5121–5137.

    Article  Google Scholar 

  16. M.S. Alsoufi and A.E. Elsayed, How Surface Roughness Performance of Printed Parts Manufactured by Desktop FDM 3D Printer with PLA+ is Influenced by Measuring Direction, Am. J. Mech. Eng, 2017, 5(5), p 211–222.

    Google Scholar 

  17. V. N. Malleswari, G. K. Manaswy, and P. G. Pragvamsa, Prediction of Surface Roughness for Fused Deposition in Fabricated Work Pieces by RSM and ANN Technique, Mater. Today: Proc., 2023.

  18. K. Kandananond, Surface Roughness Prediction of FFF-Fabricated Workpieces by Artificial Neural Network and Box–Behnken Method, Int. J. Metrol. Qual. Eng. 2021, 12(17).

  19. M. Khandelwal and D.J. Armaghani, Prediction of Drillability of Rocks with Strength Properties Using a Hybrid GA-ANN Technique, Geotech. Geol. Eng., 2016, 34(2), p 605–620.

    Article  Google Scholar 

  20. M.S. Alsoufi and A.E. Elsayed, Surface Roughness Quality and Dimensional Accuracy-A Comprehensive Analysis of 100% Infill Printed Parts Fabricated by a Personal/Desktop Cost-Effective FDM 3D Printer, Mater. Sci. Appl., 2018, 9(01), p 11.

    CAS  Google Scholar 

  21. O.A. Mohamed, S.H. Masood, J.L. Bhowmik, M. Nikzad, and J. Azadmanjiri, Effect of Process Parameters on Dynamic Mechanical Performance of FDM PC/ABS Printed Parts Through Design of Experiment, J. Mater. Eng. Perform., 2016, 25, p 2922–2935.

    Article  CAS  Google Scholar 

  22. M. Yadav, D. Yadav, R. K. Garg, R. K. Gupta, S. Kumar, and D. Chhabra, Modeling and Optimization of Piezoelectric Energy Harvesting System under Dynamic Loading. In Advances in Fluid and Thermal Engineering: Select Proceedings of FLAME 2020, 2021, p 339-353. Springer, Singapore.

  23. D. Yadav, D. Chhabra, R.K. Garg, A. Ahlawat, and A. Phogat, Optimization of FDM 3D Printing Process Parameters for Multi-Material Using Artificial Neural Network, Mater. Today: Proc., 2020, 21, p 1583–1591.

    CAS  Google Scholar 

  24. S. Deswal, R. Narang and D. Chhabra, Modeling and Parametric Optimization of FDM 3D Printing Process Using Hybrid Techniques for Enhancing Dimensional Preciseness, Int. J. Interact. Des. Manuf., 2019, 13, p 1197–1214.

    Article  Google Scholar 

  25. M. Yadav, A. Kaushik, R.K. Garg, M. Yadav, D. Chhabra, S. Rohilla, and H. Sharma, "Enhancing Dimensional Accuracy of Small Parts Through Modelling and Parametric Optimization of the FDM 3D Printing Process using GA-ANN," International Conference on Computational Modelling, Simulation and Optimization (ICCMSO), 2022, Pathum Thani, Thailand, 2022, pp. 89-94, doi: https://doi.org/10.1109/ICCMSO58359.2022.00030.

  26. P. Badhwar, A. Kumar, A. Yadav, P. Kumar, R. Siwach, D. Chhabra, and K.K. Dubey, Improved Pullulan Production and Process Optimization using Novel GA-ANN and GA-ANFIS Hybrid Statistical Tools, Biomolecules, 2020, 10(1), p 124.

    Article  CAS  Google Scholar 

  27. A. Sharma, D. Chhabra, R. Sahdev, A. Kaushik, and U. Punia, Investigation of Wear Rate of FDM Printed TPU, ASA and Multi-Material Parts using Heuristic GANN Tool, Mater. Today: Proc., 2022, 63, p 559–565.

    CAS  Google Scholar 

  28. A. Phogat, D. Chhabra, V. Sindhu, and A. Ahlawat, Analysis of Wear Assessment of FDM Printed Specimens with PLA, Multi-Material, and ABS via Hybrid Algorithms, Mater. Today: Proc., 2022, 62, p 37–43.

    CAS  Google Scholar 

  29. D. Chhabra and S. Deswal, Optimization of Significant Factors for Improving Compressive Strength of ABS in Fused Deposition Modeling by using GA & RSM, IOP Conf. Ser. Mater. Sci. Eng., 2020, 748(1), p 012007.

    Article  CAS  Google Scholar 

  30. D. Chhabra and R.K. Gupta, Optimization of FDM Printing Parameters for Surface Quality Improvement of Carbon Based Nylon (PA-CF) Composite Material Fabricated Parts using Evolutionary Algorithm, J. Nano- Electron. Phys., 2021, 13(2).

  31. D. Chhabra, S. Deswal, A. Kaushik, R.K. Garg, A. Kovács, R. Khargotra, and T. Singh, Analysis of Fused Filament Fabrication Parameters for Sliding Wear Performance of Carbon Reinforced Polyamide Composite Material Fabricated Parts Using a Hybrid Heuristic Tool, Polym. Test, 2023, 118, p 107910.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The author duly acknowledges Deenbandhu Chhotu Ram University of Science and Technology, for giving access to surface roughness testing facilities.

Funding

There is no funding/any grant from any institute/organization for the purposed work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Deepak Chhabra.

Ethics declarations

Competing interests

The authors state that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yadav, K., Rohilla, S., Ali, A. et al. Effect of Speed, Acceleration, and Jerk on Surface Roughness of FDM-Fabricated Parts. J. of Materi Eng and Perform (2023). https://doi.org/10.1007/s11665-023-08476-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11665-023-08476-2

Keywords

Navigation