Parametric Evaluation of Mechanical and Surface Characteristics in FDM-Based 3D Printing

Authors

  • Pritam Pain Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India
  • Goutam Kumar Bose Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India
  • Bipradas Bairagi Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India

DOI:

https://doi.org/10.13052/jgeu0975-1416.14110

Keywords:

3D Printing, S/N Ratio, ANN, FDM, GA

Abstract

This study investigates the influence of key process parameters – printing speed, temperature, and layer height – on the performance characteristics of three widely used Fused Deposition Modelling (FDM) materials: polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and nylon. The primary objective is to optimise surface roughness, shear strength, and wear resistance using advanced modelling and optimisation techniques. An L9 orthogonal array was used to vary the process parameters in the experimental design systematically. Analysis of Variance (ANOVA) was conducted to identify the factors that significantly influence surface roughness, shear strength, and wear resistance. Regression analysis and signal-to-noise (S/N) ratio charts were employed to assess the impact of each parameter. An Artificial Neural Network (ANN) was trained on the experimental data to predict shear strength and wear resistance accurately. Finally, a Genetic Algorithm (GA) was applied for multi-objective optimisation to simultaneously minimise surface roughness and wear resistance while maximising shear strength. The ANN model exhibited excellent predictive performance, with R-values of 0.99687 for shear strength and 0.91182 for wear resistance. The best parametric combination – 30 mm/s printing speed, 230∘C temperature, and 0.1 mm layer height – yielded 114 MPa shear strength, 94 μm surface roughness, and 0.08 wear resistance. Nylon demonstrated the highest shear strength and wear resistance under specific process conditions, while PLA provided the lowest surface roughness at optimal settings. The integration of ANN and GA provides a robust framework for process optimisation in FDM. The findings offer a basis for developing adaptive control strategies and reducing post-processing costs, contributing to improved performance and manufacturability of 3d-printed components.

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Author Biographies

Pritam Pain, Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India

Pritam Pain is an Assistant Professor in the Department of Mechanical Engineering at Haldia Institute of Technology, India. He received his B.Tech and M.Tech degrees in Mechanical Engineering and is currently pursuing his Ph.D. in Non-Traditional Manufacturing Processes. His research interests include non-conventional and micro-machining processes (EDM, WEDM, μ-EDM), parametric and multi-objective optimization, and the application of metaheuristic and soft-computing techniques in advanced manufacturing. He has published several research articles and book chapters in reputed international journals and edited volumes.

Goutam Kumar Bose, Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India

Goutam Kumar Bose is a Professor and Head of the Department of Mechanical Engineering at Haldia Institute of Technology, India, with over 25 years of academic and industrial experience. He holds a Ph.D. in Production Engineering from Jadavpur University. His research interests include advanced and non-conventional manufacturing processes, production management, tribology, and micro-scale manufacturing. He has led and contributed to several sponsored research projects funded by CSIR and AICTE and has co-authored multiple books published by leading international publishers.

Bipradas Bairagi, Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India

Bipradas Bairagi is an academician in the Department of Mechanical Engineering at Haldia Institute of Technology, India. He holds a doctoral degree in Mechanical Engineering and has several years of teaching and research experience. His research interests include manufacturing processes, advanced machining, and related areas of mechanical engineering. He has published research articles in reputed journals and conferences and is actively involved in academic and research activities.

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Published

2026-03-25

How to Cite

Pain, P., Bose, G. K., & Bairagi, B. (2026). Parametric Evaluation of Mechanical and Surface Characteristics in FDM-Based 3D Printing. Journal of Graphic Era University, 14(01), 305–326. https://doi.org/10.13052/jgeu0975-1416.14110

Issue

2026: Vol 14 Iss 1

Section

Articles