A Nonlinear Error Compensator for FDM 3D Printed Part Dimensions Using a Hybrid Algorithm Based on GMDH Neural Network
Document Type : Research Paper
Authors
1 Engineering Graphics Center, Sharif University of Technology, Tehran, Iran
2 School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
3 Mechanical Science & Engineering, Grainger College of Engineering, University of Illinois at Urbana Champaign, USA
4 Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran
Abstract
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.
Keywords
[1] S. O. Onuh, Y. Y. Yusuf, Rapid prototyping technology: applications and benefits for rapid product development, Journal of intelligent manufacturing, Vol. 10, No. 3, pp. 301-311, 1999.
[2] C. Lee, S. Kim, H. Kim, S.-H. Ahn, Measurement of anisotropic compressive strength of rapid prototyping parts, Journal of materials processing technology, Vol. 187, pp. 627-630, 2007.
[3] J.-P. Kruth, M.-C. Leu, T. Nakagawa, Progress in additive manufacturing and rapid prototyping, Cirp Annals, Vol. 47, No. 2, pp. 525-540, 1998.
[4] B. H. Lee, J. Abdullah, Z. A. Khan, Optimization of rapid prototyping parameters for production of flexible ABS object, Journal of materials processing technology, Vol. 169, No. 1, pp. 54-61, 2005.
[5] B. N. Turner, R. Strong, S. A. Gold, A review of melt extrusion additive manufacturing processes: I. Process design and modeling, Rapid Prototyping Journal, 2014.
[6] A. Sood, R. Ohdar, S. Mahapatra, Parametric appraisal of fused deposition modelling process using the grey Taguchi method, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 224, No. 1, pp. 135-145, 2010.
[7] L. Villalpando, H. Eiliat, R. J. Urbanic, An optimization approach for components built by fused deposition modeling with parametric internal structures, Procedia Cirp, Vol. 17, pp. 800-805, 2014.
[8] I. El‐Katatny, S. Masood, Y. Morsi, Error analysis of FDM fabricated medical replicas, Rapid Prototyping Journal, 2010.
[9] G. Percoco, L. Galantucci, F. Lavecchia, Validation study of an analytical model of FDM accuracy, DAAAM International Scientific Book, Published by DAAAM International Vienna, Austria, pp. 585-592, 2011.
[10] O. A. Mohamed, S. H. Masood, J. L. Bhowmik, Modeling, analysis, and optimization of dimensional accuracy of FDM-fabricated parts using definitive screening design and deep learning feedforward artificial neural network, Advances in Manufacturing, Vol. 9, No. 1, pp. 115-129, 2021.
[11] O. E. Akbaş, O. Hıra, S. Z. Hervan, S. Samankan, A. Altınkaynak, Dimensional accuracy of FDM-printed polymer parts, Rapid Prototyping Journal, 2019.
[12] J.-M. Park, J. Jeon, J.-Y. Koak, S.-K. Kim, S.-J. Heo, Dimensional accuracy and surface characteristics of 3D-printed dental casts, The Journal of prosthetic dentistry, 2020.
[13] A. Peng, X. Xiao, R. Yue, Process parameter optimization for fused deposition modeling using response surface methodology combined with fuzzy inference system, The International Journal of Advanced Manufacturing Technology, Vol. 73, No. 1-4, pp. 87-100, 2014.
[14] R. Mendricky, D. Fris, Analysis of the Accuracy and the Surface Roughness of FDM/FFF Technology and Optimisation of Process Parameters, Tehnički vjesnik, Vol. 27, No. 4, pp. 1166-1173, 2020.
[15] J. S. Chohan, R. Singh, K. S. Boparai, R. Penna, F. Fraternali, Dimensional accuracy analysis of coupled fused deposition modeling and vapour smoothing operations for biomedical applications, Composites Part B: Engineering, Vol. 117, pp. 138-149, 2017.
[16] A. Garg, A. Bhattacharya, A. Batish, On surface finish and dimensional accuracy of FDM parts after cold vapor treatment, Materials and Manufacturing Processes, Vol. 31, No. 4, pp. 522-529, 2016.
[17] J. Nsengimana, J. Van der Walt, E. Pei, M. Miah, Effect of post-processing on the dimensional accuracy of small plastic additive manufactured parts, Rapid Prototyping Journal, 2019.
[18] P. K. Garg, R. Singh, I. Ahuja, Multi-objective optimization of dimensional accuracy, surface roughness and hardness of hybrid investment cast components, Rapid Prototyping Journal, 2017.
[19] K. Tong, S. Joshi, E. A. Lehtihet, Error compensation for fused deposition modeling (FDM) machine by correcting slice files, Rapid Prototyping Journal, 2008.
[20] S. Li, T. Liu, X. Xiao, W. Hu, W. Liao, Study on Size Error Compensation of Connecting Bracket Based on Fused Deposition Modeling, in Proceeding of, IOP Publishing, pp. 012013.
[21] R. Păcurar, V. Buzilă, A. Păcurar, E. Guţiu, S. D. Stan, P. Berce, Research on improving the accuracy of FDM 3D printing process by using a new designed calibrating part, in Proceeding of, EDP Sciences, pp. 01007.
[22] U. Yaman, Shrinkage compensation of holes via shrinkage of interior structure in FDM process, The International Journal of Advanced Manufacturing Technology, Vol. 94, No. 5, pp. 2187-2197, 2018.
[23] U. M. Dilberoglu, S. Simsek, U. Yaman, Shrinkage compensation approach proposed for ABS material in FDM process, Materials and Manufacturing Processes, Vol. 34, No. 9, pp. 993-998, 2019.
[24] A. Noriega, D. Blanco, B. Alvarez, A. Garcia, Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm, The International Journal of Advanced Manufacturing Technology, Vol. 69, No. 9-12, pp. 2301-2313, 2013.
[25] D. A. Bircan, Development of a NURBS based adaptive slicing procedure for fused deposition modeling in rapid prototyping applications, Thesis, PhD thesis, Cukurova University, 2008.
[26] F. Górski, W. Kuczko, R. Wichniarek, Influence of process parameters on dimensional accuracy of parts manufactured using Fused Deposition Modelling technology, Advances in Science and Technology Research Journal, Vol. 7, No. 19, pp. 27--35, 2013.
[27] F. Rayegani, G. C. Onwubolu, Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE), The International Journal of Advanced Manufacturing Technology, Vol. 73, No. 1-4, pp. 509-519, 2014.
[28] A. K. Sood, R. Ohdar, S. S. Mahapatra, Improving dimensional accuracy of fused deposition modelling processed part using grey Taguchi method, Materials & Design, Vol. 30, No. 10, pp. 4243-4252, 2009.
[29] S. K. Panda, S. Padhee, S. Anoop Kumar, S. S. Mahapatra, Optimization of fused deposition modelling (FDM) process parameters using bacterial foraging technique, Intelligent information management, Vol. 1, No. 02, pp. 89, 2009.
[30] A. Arivazhagan, S. Masood, Dynamic mechanical properties of ABS material processed by fused deposition modelling, Int. J. Eng. Res. Appl, Vol. 2, No. 3, pp. 2009-2014, 2012.
[31] O. Luzanin, D. Movrin, M. Plancak, EXPERIMENTAL INVESTIGATION OF EXTRUSION SPEED AND TEMPERATURE EFFECTS ON ARITHMETIC MEAN, Journal for Technology of Plasticity, Vol. 38, No. 2, 2013.
[32] R. K. Sahu, S. Mahapatra, A. K. Sood, A study on dimensional accuracy of fused deposition modeling (FDM) processed parts using fuzzy logic, Journal for Manufacturing Science and Production, Vol. 13, No. 3, pp. 183, 2013.
[33] S. Bhatia, Effect of machine positional errors on geometric tolerances in additive manufacturing, Thesis, University of Cincinnati, 2014.
[34] S. O. Akande, Dimensional accuracy and surface finish optimization of fused deposition modelling parts using desirability function analysis, International Journal of Engineering Research and Technology, Vol. 4, No. 4, pp. 196-202, 2015.
[35] L. Baich, G. Manogharan, H. Marie, Study of infill print design on production cost-time of 3D printed ABS parts, International Journal of Rapid Manufacturing, Vol. 5, No. 3-4, pp. 308-319, 2015.
[36] A. Equbal, A. K. Sood, A. Ansari, A. Equbal, Optimization of process parameters of FDM part for minimiizing its dimensional inaccuracy, International Journal of Mechanical and Production Engineering Research and Development, Vol. 7, No. 2, pp. 57-65, 2017.
[37] R. Narang, D. Chhabra, Analysis of process parameters of fused deposition modeling (FDM) technique, International Journal on Future Revolution in Computer Science & Communication Engineering, Vol. 3, No. 10, pp. 41-48, 2017.
[38] A. Qattawi, B. Alrawi, A. Guzman, Experimental optimization of fused deposition modelling processing parameters: a design-for-manufacturing approach, Procedia Manufacturing, Vol. 10, pp. 791-803, 2017.
[39] Y. Y. Aw, C. K. Yeoh, M. A. Idris, P. L. Teh, K. A. Hamzah, S. A. Sazali, Effect of printing parameters on tensile, dynamic mechanical, and thermoelectric properties of FDM 3D printed CABS/ZnO composites, Materials, Vol. 11, No. 4, pp. 466, 2018.
[40] X. Deng, Z. Zeng, B. Peng, S. Yan, W. Ke, Mechanical properties optimization of poly-ether-ether-ketone via fused deposition modeling, Materials, Vol. 11, No. 2, pp. 216, 2018.
[41] M. Leite, J. Fernandes, A. M. Deus, L. Reis, M. F. Vaz, Study of the influence of 3D printing parameters on the mechanical properties of PLA, in Proceeding of.
[42] A. Dey, N. Yodo, A systematic survey of FDM process parameter optimization and their influence on part characteristics, Journal of Manufacturing and Materials Processing, Vol. 3, No. 3, pp. 64, 2019.
[43] J. Lyu, S. Manoochehri, Error modeling and compensation for FDM machines, Rapid Prototyping Journal, 2019.
[44] L. Natrayan, M. S. Kumar, An integrated artificial neural network and Taguchi approach to optimize the squeeze cast process parameters of AA6061/Al2O3/SiC/Gr hybrid composites prepared by novel encapsulation feeding technique, Materials Today Communications, Vol. 25, pp. 101586, 2020.
[45] F. Halladj, A. Boukhiar, H. Amellal, S. Benamara, Optimization of traditional date vinegar preparation using full factorial design, Journal of the American Society of Brewing Chemists, Vol. 74, No. 2, pp. 137-144, 2016.
[46] S. Ree, Y. H. Park, H. Yoo, A study on education quality using the Taguchi method, Total Quality Management & Business Excellence, Vol. 25, No. 7-8, pp. 935-943, 2014.
[47] H. Rangaswamy, I. Sogalad, S. Basavarajappa, S. Acharya, G. Manjunath Patel, Experimental analysis and prediction of strength of adhesive-bonded single-lap composite joints: Taguchi and artificial neural network approaches, SN Applied Sciences, Vol. 2, pp. 1-15, 2020.
[48] C. Camposeco-Negrete, Optimization of FDM parameters for improving part quality, productivity and sustainability of the process using Taguchi methodology and desirability approach, Progress in Additive Manufacturing, Vol. 5, No. 1, pp. 59-65, 2020.
[49] K. Elbaz, S.-L. Shen, A. Zhou, Z.-Y. Yin, H.-M. Lyu, Prediction of Disc Cutter Life during Shield Tunneling with AI via the Incorporation of a Genetic Algorithm into a GMDH-Type Neural Network, Engineering, 2020.
[50] L. Anastasakis, N. Mort, The development of self-organization techniques in modelling: a review of the group method of data handling (GMDH), RESEARCH REPORT-UNIVERSITY OF SHEFFIELD DEPARTMENT OF AUTOMATIC CONTROL AND SYSTEMS ENGINEERING, 2001.
[51] M. H. Ahmadi, M. Sadeghzadeh, A. H. Raffiee, K.-w. Chau, Applying GMDH neural network to estimate the thermal resistance and thermal conductivity of pulsating heat pipes, Engineering Applications of Computational Fluid Mechanics, Vol. 13, No. 1, pp. 327-336, 2019.
[52] M. Bildirici, Ö. Ersin, Modeling Markov switching ARMA-GARCH neural networks models and an application to forecasting stock returns, The Scientific World Journal, Vol. 2014, 2014.
[53] H. Elçiçek, E. Akdoğan, S. Karagöz, The use of artificial neural network for prediction of dissolution kinetics, The Scientific World Journal, Vol. 2014, 2014.
[54] A. Gonzalez-Sanchez, J. Frausto-Solis, W. Ojeda-Bustamante, Attribute selection impact on linear and nonlinear regression models for crop yield prediction, The Scientific World Journal, Vol. 2014, 2014.
[55] D. J. Armaghani, M. Hasanipanah, H. B. Amnieh, D. T. Bui, P. Mehrabi, M. Khorami, Development of a novel hybrid intelligent model for solving engineering problems using GS-GMDH algorithm, Engineering with Computers, pp. 1-13, 2019.
[56] D. Li, D. J. Armaghani, J. Zhou, S. H. Lai, M. Hasanipanah, A GMDH predictive model to predict rock material strength using three non-destructive tests, Journal of Nondestructive Evaluation, Vol. 39, No. 4, pp. 1-14, 2020.
[57] M. Dorn, A. L. Braga, C. H. Llanos, L. S. Coelho, A GMDH polynomial neural network-based method to predict approximate three-dimensional structures of polypeptides, Expert Systems with Applications, Vol. 39, No. 15, pp. 12268-12279, 2012.
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Volume 52, Issue 3
September 2021Pages 451-477
- Receive Date: 09 June 2021
- Revise Date: 02 September 2021
- Accept Date: 03 September 2021