Modeling and parametric optimization of FDM 3D printing process using hybrid techniques for enhancing dimensional preciseness

  • Sandeep Deswal
  • Rajan Narang
  • Deepak ChhabraEmail author
Technical Paper


In this study, significant process parameters (layer thickness, build orientation, infill density and number of contours) are optimized for enhancing the magnitude/dimensional preciseness of fused deposition modeling (FDM) devise units. Hybrid statistical tools such as response surface methodology–genetic algorithm (RSM–GA), artificial neural network (ANN) and artificial neural network-genetic algorithm (ANN-GA) in MAT LAB 16.0 are utilized for training and optimization. An attempt has been made to build up a mathematical model in order to set up an indirect correlation between various FDM process parameters and magnitude preciseness. Sequentially to verify the different developed models and the optimum process parameters setting validation tests were also performed. The results showed that various hybrid statistical tools such as RSM-GA, ANN and ANN-GA are very adequate tools for FDM process parameter optimization. The minimum percentage variation in length = 0.06409%, width = 0.03961% and thickness = 0.85689% can be obtained by using ANN-GA.


Response surface methodology (RSM) Artificial neural network (ANN) Genetic algorithm (GA) Fused deposition modeling (FDM) Process parameters 



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© Springer-Verlag France SAS, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, UIETMaharshi Dayanand UniversityRohtakIndia

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