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Parametric optimization of FDM using the ANN-based whale optimization algorithm

Published online by Cambridge University Press:  08 August 2022

Praveen Kumar Open the ORCID record for Praveen Kumar [Opens in a new window] ,
Pardeep Gupta  and
Indraj Singh
Praveen Kumar*
Affiliation:
Department of Mechanical Engineering, Sant Longowal Institute of Engineering and Technology, Longowal 148106, Punjab, India
Pardeep Gupta
Affiliation:
Department of Mechanical Engineering, Sant Longowal Institute of Engineering and Technology, Longowal 148106, Punjab, India
Indraj Singh
Affiliation:
Department of Mechanical Engineering, Sant Longowal Institute of Engineering and Technology, Longowal 148106, Punjab, India
*
Author for correspondence: Praveen Kumar, E-mail: praveen_pme1802@sliet.ac.in
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Abstract

Surface roughness (SR) is one of the major parameters used to govern the quality of the fused deposition modeling (FDM)-printed products, and the FDM process parameters can be easily regulated in order to obtain a good surface finish. The surface quality of the product produced by the FDM is generally affected by the staircase effect that needs to be managed. Also, the production time (PT) to fabricate the product and volume percentage error (VPE) should be minimized to make the FDM process more efficient. The aim of this paper is to accomplish these three objectives with the use of the parametric optimization technique integrating the artificial neural network (ANN) and the whale optimization algorithm (WOA). The FDM parameters which have been taken into consideration are layer thickness, nozzle temperature, printing speed, and raster width. Experimentation has been conducted on printed samples to examine the impact of the input parameters on SR, VPE, and PT according to Taguchi's L27 orthogonal array. The ANN model has been built up using the experimental data, which was further used as an objective function in the WOA with an aim to minimize output responses. The robustness of the proposed method has been validated on the optimal combinations of FDM process parameters.

Keywords

Artificial neural networkfused deposition modelinghybrid AI algorithmsurface roughnesswhale optimization technique
Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e27
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Bintara, RD, Lubis, DZ and Aji Pradana, YR (2021) The effect of layer height on the surface roughness in 3D printed polylactic acid (PLA) using FDM 3d printing. IOP Conference Series: Materials Science and Engineering 1034, 012096.CrossRefGoogle Scholar
Brennan, MC, Keist, JS and Palmer, TA (2021) Defects in metal additive manufacturing processes. Journal of Materials Engineering and Performance 30, 4808–4818.10.1007/s11665-021-05919-6CrossRefGoogle Scholar
Burke, C, Dalal, A, Abukhalaf, A and Noorani, R (2020) Effects of process parameter variation on the surface roughness of polylactic acid (PLA) materials using design of experiments (DOE). IOP Conference Series: Materials Science and Engineering 897, 012003.CrossRefGoogle Scholar
Chauhan, S and Vashishtha, G (2021) Mutation-based arithmetic optimization algorithm for global optimization. 2021 International Conference on Intelligent Technologies (CONIT), pp. 1–6.CrossRefGoogle Scholar
Chauhan, S, Singh, B and Singh, M (2021) Modified ant colony optimization based PID controller design for coupled tank system. Engineering Research Express 3, 45005.10.1088/2631-8695/ac2bf3CrossRefGoogle Scholar
Fountas, NA, Kitsakis, K, Aslani, K-E, Kechagias, JD and Vaxevanidis, NM (2021) An experimental investigation of surface roughness in 3D-printed PLA items using design of experiments. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 135065012110593. http://dx.doi.org/10.1177/13506501211059306CrossRefGoogle Scholar
Gibson, I, Rosen, D, Stucker, B and Khorasani, M (2020) Additive manufacturing technologies. Yosetsu Gakkai Shi/Journal of the Japan Welding Society, Vol. 3. USA: Springer.Google Scholar
Guleria, V, Kumar, V and Singh, PK (2022 a) Classification of surface roughness during turning of forged EN8 steel using vibration signal processing and support vector machine. Engineering Research Express 4, 015029. http://dx.doi.org/10.1088/2631-8695/ac57faCrossRefGoogle Scholar
Guleria, V, Kumar, V and Singh, PK (2022 b) A novel approach for prediction of surface roughness in turning of EN353 steel by RVR-PSO using selected features of VMD along with cutting parameters. Journal of Mechanical Science and Technology 36, 2775–2785. doi: http://dx.doi.org/10.1007/s12206-022-0510-2CrossRefGoogle Scholar
Hafenecker, J, Papke, T and Merklein, M (2021) Influence of stress states on forming hybrid parts with sheet metal and additively manufactured element. Journal of Materials Engineering and Performance 30, 5159–5169.10.1007/s11665-021-05674-8CrossRefGoogle Scholar
Hartcher-O'Brien, J, Evers, J and Tempelman, E (2019) Surface roughness of 3D printed materials: comparing physical measurements and human perception. Materials Today Communications 19, 300–305.10.1016/j.mtcomm.2019.01.008CrossRefGoogle Scholar
Heshmat, M and Adel, M (2021) Investigating the effect of hot air polishing parameters on surface roughness of fused deposition modeling PLA products: ANOVA and regression analysis. Progress in Additive Manufacturing 6, 679–687.10.1007/s40964-021-00190-6CrossRefGoogle Scholar
Horvath, D, Noorani, R and Mendelson, M (2007) Improvement of Surface Roughness nn ABS 400 Polymer Materials Using Design of Experiments (DOE). Materials Science Forum 565, 2389–2392.Google Scholar
Jayanth, N, Senthil, P and Prakash, C (2018) Effect of chemical treatment on tensile strength and surface roughness of 3D-printed ABS using the FDM process. Virtual and Physical Prototyping 13, 155–163.10.1080/17452759.2018.1449565CrossRefGoogle Scholar
Kesvarakul, R and Limpadapun, K (2019) The study and development of factor affecting the smoothness in 3D printed part surface treatment via acetone vapor. Key Engineering Materials 821, 174–180.10.4028/www.scientific.net/KEM.821.174CrossRefGoogle Scholar
Kovan, V, Tezel, T, Topal, ES and Camurlu, HE (2018) Printing parameters effect on surface characteristics of 3D printed Pla materials. Machines Technologies Materials 12, 266–269.Google Scholar
Kumar, S, Kannan, V and Sankaranarayanan, G (2014) Parameter Optimization of ABS-M30i Parts Produced by Fused Deposition Modeling for Minimum Surface Roughness.International Journal of Current Engineering and Technology 3, 93–97.Google Scholar
Li, B, Yang, J, Gu, H, Jiang, J, Zhang, J and Sun, J (2021) Surface roughness of PLA parts by FDM with chemical treatment. Journal of Physics: conference Series 1948, 012199.Google Scholar
Liu, S and Shin, YC (2019) Additive manufacturing of Ti6Al4V alloy: a review. Materials and Design 164, 107552.10.1016/j.matdes.2018.107552CrossRefGoogle Scholar
Liu, Z, Liu, S, Li, Y and Meehan, PA (2014) Modeling and optimization of surface roughness in incremental sheet forming using a multi-objective function. Materials and Manufacturing Processes 29, 808–818.10.1080/10426914.2013.864405CrossRefGoogle Scholar
Mafarja, M and Mirjalili, S (2018) Whale optimization approaches for wrapper feature selection. Applied Soft Computing 62, 441–453.10.1016/j.asoc.2017.11.006CrossRefGoogle Scholar
Mirjalili, S and Lewis, A (2016) The whale optimization algorithm. Advances in Engineering Software 95, 51–67.10.1016/j.advengsoft.2016.01.008CrossRefGoogle Scholar
Mitra, AC, Jawarkar, M, Soni, T and Kiranchand, GR (2016) Implementation of taguchi method for robust suspension design. Procedia Engineering 144, 77–84.CrossRefGoogle Scholar
Narang, R and Chhabra, D (2017) Analysis of process parameters of fused deposition modeling (FDM) technique. International Journal on Future Revolution in Computer Science & Communication Engineering (IJFRCSCE), May 2018.Google Scholar
Pramanik, D, Mandal, A and Kuar, AS (2019) An experimental investigation on improvement of surface roughness of ABS on fused deposition modelling process. Materials Today: proceedings 26, 860–863.Google Scholar
Radhwan, H, Shayfull, Z, Farizuan, MR, Effendi, MSM and Irfan, AR (2019) Optimization parameter effects on the quality surface finish of the three-dimensional printing (3D-printing) fused deposition modeling (FDM) using RSM. AIP Conference Proceedings 2129, 020155.10.1063/1.5118163CrossRefGoogle Scholar
Saad, MS, Nor, AM, Baharudin, ME, Zakaria, MZ and Aiman, A (2019) Optimization of surface roughness in FDM 3d printer using response surface methodology, particle swarm optimization, and symbiotic organism search algorithms. The International Journal of Advanced Manufacturing Technology 105, 5121–5137.10.1007/s00170-019-04568-3CrossRefGoogle Scholar
Samadianfard, S, Hashemi, S, Kargar, K, Izadyar, M, Mostafaeipour, A, Mosavi, A, Nabipour, N and Shamshirband, S (2020) Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm. Energy Reports 6, 1147–1159.10.1016/j.egyr.2020.05.001CrossRefGoogle Scholar
Sammaiah, P, Rushmamanisha, K, Praveenadevi, N and Rajasri Reddy, I (2020) The Influence Of Process Parameters On The Surface Roughness Of The 3d Printed Part In FDM Process. IOP Conference Series: Materials Science and Engineering 981, 042021. http://dx.doi.org/10.1088/1757-899X/981/4/042021CrossRefGoogle Scholar
Sharifabad, SS, Derazkola, HA, Esfandyar, M, Elyasi, M and Khodabakhshi, F (2021) Mechanical properties of HA@Ag/PLA nanocomposite structures prepared by extrusion-based additive manufacturing. Journal of the Mechanical Behavior of Biomedical Materials 118, 104455. http://dx.doi.org/10.1016/j.jmbbm.2021.104455CrossRefGoogle ScholarPubMed
Shirmohammadi, M, Goushchi, SJ and Keshtiban, PM (2021) Optimization of 3D printing process parameters to minimize surface roughness with hybrid artificial neural network model and particle swarm algorithm. Progress in Additive Manufacturing 6, 199–215.10.1007/s40964-021-00166-6CrossRefGoogle Scholar
Singh, G, Missiaen, JM, Bouvard, D and Chaix, JM (2021) Copper extrusion 3D printing using metal injection moulding feedstock: analysis of process parameters for green density and surface roughness optimization. Additive Manufacturing 38, 101778.CrossRefGoogle Scholar
Taghavifar, H and Mardani, A (2014) Application of artificial neural networks for the prediction of traction performance parameters. Journal of the Saudi Society of Agricultural Sciences 13, 35–43.CrossRefGoogle Scholar
Talalwa, L, Natour, G, Mishra, A, Srivastava, V and Gupta, K (n.d.) The influence of process parameters on the surface roughness of the 3d printed part in FDM process.Google Scholar
Thakur, A, Guleria, V and Lal, R (2022) Multi-response optimization in turning of EN-24 steel under MQL. Engineering Research Express 4, 025052. http://dx.doi.org/10.1088/2631-8695/ac7a0cCrossRefGoogle Scholar
Vaheddoost, B, Guan, Y and Mohammadi, B (2020) Application of hybrid ANN-whale optimization model in evaluation of the field capacity and the permanent wilting point of the soils. Environmental Science and Pollution Research 27, 13131–13141.10.1007/s11356-020-07868-4CrossRefGoogle ScholarPubMed
Vashishtha, G and Kumar, R (2021 a) An effective health indicator for the Pelton wheel using a Levy flight mutated genetic algorithm. Measurement Science and Technology 32, 94003.10.1088/1361-6501/abeea7CrossRefGoogle Scholar
Vashishtha, G and Kumar, R (2021 b) Autocorrelation energy and aquila optimizer for MED filtering of sound signal to detect bearing defect in Francis turbine. Measurement Science and Technology 33, 15006.10.1088/1361-6501/ac2cf2CrossRefGoogle Scholar
Vashishtha, G and Kumar, R (2022) An amended grey wolf optimization with mutation strategy to diagnose bucket defects in Pelton wheel. Measurement 187, 110272.CrossRefGoogle Scholar
Vashishtha, G, Chauhan, S, Singh, M and Kumar, R (2021) Bearing defect identification by swarm decomposition considering permutation entropy measure and opposition-based slime mould algorithm. Measurement: Journal of the International Measurement Confederation 178, 109389.CrossRefGoogle Scholar
Wimpenny, DI, Pandey, PM and Jyothish Kumar, L (2016) Advances in 3D Printing & Additive Manufacturing Technologies.1.Singapore: Springer.Google Scholar
Yadav, N and Kumar, R (2021) Energy harvesting from low-frequency sinusoidal vibrations using diaphragm type piezoelectric element. Indian Journal of Engineering and Materials Sciences 28, 265–270.Google Scholar
Yadav, N and Kumar, R (2021 b) Study on piezoelectric ceramic under different pressurization conditions and circuitry. Journal of Electroceramics 2021, 1–10.Google Scholar
Yang, L, Li, S, Li, Y, Yang, M and Yuan, Q (2019) Experimental investigations for optimizing the extrusion parameters on FDM PLA printed parts. Journal of Materials Engineering and Performance 28, 169–182.10.1007/s11665-018-3784-xCrossRefGoogle Scholar
Yu, H and Wilamowski, BM (2011) Levenbeg-Marquardt Training, In IntellIgent systems, 2nd edition, CRC Press, p. 572.Google Scholar
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