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A Nonlinear Error Compensator for FDM 3D Printed Part Dimensions Using a Hybrid Algorithm Based on GMDH Neural Network | ||
| Journal of Computational Applied Mechanics | ||
| دوره 52، شماره 3، آذر 2021، صفحه 451-477 اصل مقاله (1.77 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jcamech.2021.325325.628 | ||
| نویسندگان | ||
| Hamid Haghshenas Gorgani* 1؛ Hossein Korani2؛ Reihaneh Jahedan3؛ Sharif Shabani4 | ||
| 1Engineering Graphics Center, Sharif University of Technology, Tehran, Iran | ||
| 2School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| 3Mechanical Science & Engineering, Grainger College of Engineering, University of Illinois at Urbana Champaign, USA | ||
| 4Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran | ||
| چکیده | ||
| 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. 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. | ||
| کلیدواژهها | ||
| Additive Manufacturing؛ Fused Deposition Method؛ Error Compensation Model؛ GMDH Neural Network | ||
| مراجع | ||
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