University of Tehran Press Journal of Computational Applied Mechanics 2423-6713 52 3 2021 09 01 A Nonlinear Error Compensator for FDM 3D Printed Part Dimensions Using a Hybrid Algorithm Based on GMDH Neural Network 451 477 83807 10.22059/jcamech.2021.325325.628 EN Hamid Haghshenas Gorgani Engineering Graphics Center, Sharif University of Technology, Tehran, Iran Hossein Korani School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran Reihaneh Jahedan Mechanical Science & Engineering, Grainger College of Engineering, University of Illinois at Urbana Champaign, USA Sharif Shabani Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran Journal Article 2021 06 09 Following the advances in Computer-Aided Design (CAD) and Additive Manufacturing (AM), with regards to the numerous benefits of the Fused Deposition Modeling (FDM) as a popular AM process, resolving its weaknesses has become increasingly important. A serious problem of the FDM is the dimensional error or size difference between the CAD model and the actual 3D printed part.<br />In this study, the approach is compensating the error regardless of its source. At First, all parameters affecting the dimensional accuracy of FDM are comprehensively identified. Then, multi-input–single-output (MISO) data is prepared by designing experiments using the Taguchi method and obtaining the results from 3D printed samples. Next, a GMDH neural network is applied, which uses a simple nonlinear regression formula in each neuron but can create very complex neuron combinations. So, it is possible to analyze small or even noisy data. Regulatory parameters of the Neural Net have been optimized to increase efficiency. The case study shows a decrease in the RSME for the Nominal CAD Model from 0.377 to 0.033, displaying the compensator's efficiency. https://jcamech.ut.ac.ir/article_83807_e35bfd45e3504002ce164d4c6bea5306.pdf