{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T09:01:26Z","timestamp":1790845286358,"version":"4.1.0"},"reference-count":58,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Symbiosis International"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Polymers"],"abstract":"<jats:p>This study systematically investigates the effect of three Fused Deposition Modelling (FDM) printing parameters, i.e., wall thickness, layer height, and infill density, on the compressive behaviour of FDM-printed acrylonitrile butadiene styrene (ABS) and glass-fibre-reinforced ABS (ABS-GF). A Response Surface Methodology (RSM)-based Central Composite Design (CCD) comprising twenty experimental runs was employed to execute the experiments and develop regression models. The statistical significance of the process variables was determined through Analysis of Variance (ANOVA). Additionally, three nature-inspired optimization techniques, namely, the Firefly Algorithm (FA), Big Bang\u2013Big Crunch (BB-BC), and Grey Wolf Optimization (GWO), were applied to identify the optimal printing conditions for achieving maximum compression strength. Experimental results demonstrated that glass-fibre-reinforced ABS exhibited superior compressive strength, greater load-carrying capacity, and more favourable deformation characteristics compared to neat ABS. Among the investigated parameters, infill density was found to be highly influential, as it controlled the internal porosity and structural continuity of the printed parts. The quadratic regression models developed exhibited excellent prediction accuracy, with coefficients of determination exceeding 95% for ABS and 97% for ABS-GF. Optimization results showed that the GWO algorithm achieved the highest fitness value of 97.15% and 98.48% for ABS and ABS-GF, respectively. The proposed optimization approach provides an effective framework for optimizing the compressive behaviour of FDM-printed polymer composites, thereby offering valuable guidance for the design and manufacture of high-performance engineering components through additive manufacturing techniques.<\/jats:p>","DOI":"10.3390\/polym18192398","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T08:19:08Z","timestamp":1790842748000},"page":"2398","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Process Parameter Optimization for Enhanced Compressive Strength of FDM-Printed ABS and Glass-Fibre-Reinforced ABS Using RSM and Metaheuristic Algorithms"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5848-3389","authenticated-orcid":false,"given":"Jaimon Dennis","family":"Quadros","sequence":"first","affiliation":[{"name":"Centre for Advanced Manufacturing, National Centre for Motorsport Engineering, School of Engineering and Built Environment, University of Greater Manchester, Bolton BL3 5AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6649-3627","authenticated-orcid":false,"given":"Yakub Iqbal","family":"Mogul","sequence":"additional","affiliation":[{"name":"Centre for Advanced Manufacturing, National Centre for Motorsport Engineering, School of Engineering and Built Environment, University of Greater Manchester, Bolton BL3 5AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0164-759X","authenticated-orcid":false,"given":"Ganesan","family":"Subramanian","sequence":"additional","affiliation":[{"name":"Department of Computer Studies & Engineering, Symbiosis International University Dubai, Dubai Knowledge Park, Dubai P.O. Box 502930, United Arab Emirates"},{"name":"Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ma","family":"Mohin","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, School of Engineering and Built Environment, University of Greater Manchester, Deane Road, Bolton BL3 5AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suhas","family":"Poojary","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Sahyadri College of Engineering & Management, Mangalore, Affiliated to Visvesvaraya Technological University, Belagavi 575007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishan","family":"Jones","sequence":"additional","affiliation":[{"name":"Centre for Advanced Manufacturing, National Centre for Motorsport Engineering, School of Engineering and Built Environment, University of Greater Manchester, Bolton BL3 5AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph","family":"Horne","sequence":"additional","affiliation":[{"name":"Centre for Advanced Manufacturing, National Centre for Motorsport Engineering, School of Engineering and Built Environment, University of Greater Manchester, Bolton BL3 5AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanumantharaya","family":"Rangaswamy","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, REVA University, Bangaluru 560064, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prashanth","family":"Thalambeti","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Dr. H. N. National College of Engineering, Bengaluru 560070, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9870-5804","authenticated-orcid":false,"given":"Mohammad Shohag","family":"Alam","sequence":"additional","affiliation":[{"name":"Faculty of Textile Technologies and Design, Istanbul Technical University, G\u00fcm\u00fcssuyu, Istanbul 34437, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gibson, I., Rosen, D., Stucker, B., Khorasani, M., Rosen, D., Stucker, B., and Khorasani, M. (2021). Additive Manufacturing Technologies, Springer.","DOI":"10.1007\/978-3-030-56127-7"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s00289-009-0200-6","article-title":"Dynamic mechanical, thermal, and morphological study of ABS\/textile fiber composites","volume":"64","author":"Martins","year":"2010","journal-title":"Polym. Bull."},{"key":"ref_3","first-page":"101299","article-title":"Thermomechanical characterization of short carbon fiber and short glass fiber-reinforced ABS used in large format additive manufacturing","volume":"35","author":"Billah","year":"2020","journal-title":"Addit. Manuf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"114707","DOI":"10.1016\/j.matdes.2025.114707","article-title":"Study and characterization of recycled ABS-GF in large format additive manufacturing to enhance mechanical properties of printed structures","volume":"259","year":"2025","journal-title":"Mater. Des."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3709","DOI":"10.1007\/s00170-025-15392-3","article-title":"Multi-objective optimization of FDM process parameters for 3D-printed polycarbonate using Taguchi-based Gray Relational Analysis","volume":"137","author":"Cicek","year":"2025","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wang, Y., Xue, B., Wang, C., and Yang, B. (2025). Optimization of fused deposition modeling parameters for mechanical properties of polylactic acid parts based on Kriging and Cuckoo Search. Aerospace, 12.","DOI":"10.3390\/aerospace12010038"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chaudhry, M.S., and Czekanski, A. (2020). Evaluating FDM Process Parameter Sensitive Mechanical Performance of Elastomers at Various Strain Rates of Loading. Materials, 13.","DOI":"10.3390\/ma13143202"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.jmrt.2025.06.013","article-title":"In-depth study and optimization of process parameters to enhance tensile and compressive strengths of PETG in FDM technology","volume":"37","author":"Kumaresan","year":"2025","journal-title":"J. Mater. Res. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ek\u015fi, S., and Karakaya, C. (2025). Effects of Process Parameters on Tensile Properties of 3D-Printed PLA Parts Fabricated with the FDM Method. Polymers, 17.","DOI":"10.3390\/polym17141934"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Panes, A., Claver, J., and Camacho, A. (2018). The influence of manufacturing parameters on the mechanical behaviour of PLA and ABS pieces manufactured by FDM: A comparative analysis. Materials, 11.","DOI":"10.3390\/ma11081333"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s001700200016","article-title":"Investigation of 3D non-random porous structures by fused deposition modelling","volume":"19","author":"Too","year":"2002","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.jmapro.2015.11.002","article-title":"Mechanical behaviour of ABS: An experimental study using FDM and injection moulding techniques","volume":"21","author":"Dawoud","year":"2016","journal-title":"J. Manuf. Processes"},{"key":"ref_13","unstructured":"Hernandez, R., Slaughter, D., Whaley, D., Tate, J., and Asiabanpour, B. (2019, January 12\u201314). Analyzing the tensile, compressive, and flexural properties of 3D printed ABS P430 plastic based on printing orientation using fused deposition modeling. Proceedings of the 27th Annual International Solid Freeform Fabrication Symposium, Austin, TX, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Muhamedagic, K., Berus, L., Poto\u010dnik, D., Cekic, A., Begic-Hajdarevic, D., Cohodar Husic, M., and Ficko, M. (2022). Effect of Process Parameters on Tensile Strength of FDM Printed Carbon Fiber Reinforced Polyamide Parts. Appl. Sci., 12.","DOI":"10.3390\/app12126028"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.matdes.2009.06.016","article-title":"Parametric appraisal of mechanical property of fused deposition modelling processed parts","volume":"31","author":"Sood","year":"2010","journal-title":"Mater. Des."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1007\/s40032-022-00848-2","article-title":"A Study of Tensile Characteristics for Glass and Carbon Fiber Along with Sandwiched Reinforced ABS Composites","volume":"103","author":"Radadiya","year":"2022","journal-title":"J. Inst. Eng. (India) Ser. C"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e54007","DOI":"10.1002\/app.54007","article-title":"Flexural, impact, and dynamic mechanical analysis of glass fiber\/ABS and glass fiber\/carbon fiber\/ABS composites","volume":"140","author":"Aravind","year":"2023","journal-title":"J. Appl. Polym. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dhandapani, A., Krishnasamy, S., Nagarajan, R., Selvaraj, A.D.A., Thiagamani, S.M.K., Muthukumar, C., Mohammad, F., Al-Lohedan, H.A., and Ismail, S.O. (2023). Investigation of Wear Behavior in Self-Lubricating ABS Polymer Composites Reinforced with Glass Fiber\/ABS and Glass Fiber\/Carbon Fiber\/ABS Hybrid. Lubricants, 11.","DOI":"10.3390\/lubricants11030131"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e2522951","DOI":"10.1080\/17452759.2025.2522951","article-title":"Advances in interlayer bonding in fused deposition modelling: A comprehensive review","volume":"20","author":"Omer","year":"2025","journal-title":"Virtual Phys. Prototyp."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hozdi\u0107, E., and Hozdi\u0107, E. (2026). Mechanical Parameters and Microstructural Evolution of FDM-Printed PLA and PLA + CF Under Variable Infill Architecture and Lubricant Exposure. Polymers, 18.","DOI":"10.3390\/polym18010072"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.compscitech.2014.10.009","article-title":"Highly oriented carbon fiber\u2013polymer composites via additive manufacturing","volume":"105","author":"Tekinalp","year":"2014","journal-title":"Compos. Sci. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dias Gon\u00e7alves, V.P., Vieira, C.M.F., Simonassi, N.T., Periss\u00e9 Duarte Lopes, F., Youssef, G., and Colorado, H.A. (2024). Evaluation of Mechanical Properties of ABS-like Resin for Stereolithography Versus ABS for Fused Deposition Modeling in Three-Dimensional Printing Applications for Odontology. Polymers, 16.","DOI":"10.3390\/polym16202921"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6800","DOI":"10.1038\/s41598-023-33510-7","article-title":"Achieving effective interlayer bonding of PLA parts during the material extrusion process with enhanced mechanical properties","volume":"13","author":"Kumar","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liao, Y., Liu, C., Coppola, B., Barra, G., Di Maio, L., Incarnato, L., and Lafdi, K. (2019). Effect of porosity and crystallinity on 3D printed PLA properties. Polymers, 11.","DOI":"10.3390\/polym11091487"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e16789","DOI":"10.1016\/j.heliyon.2023.e17053","article-title":"Effect of ultrasonic vibration on the mechanical properties of 3D printed acrylonitrile butadiene styrene and polylactic acid samples","volume":"9","author":"Maidin","year":"2023","journal-title":"Heliyon"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hamat, S., Ishak, M.R., Salit, M.S., Yidris, N., Showkat Ali, S.A., Hussin, M.S., Abdul Manan, M.S., Ahamad Suffin, M.Q.Z., Ibrahim, M., and Mohd Khalil, A.N. (2023). The effects of self-polymerized polydopamine coating on mechanical properties of polylactic acid (PLA)\u2013kenaf fiber (KF) in fused deposition modeling (FDM). Polymers, 15.","DOI":"10.3390\/polym15112525"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100510","DOI":"10.1016\/j.hybadv.2025.100510","article-title":"Enhancing mechanical and surface properties of 3D-Printed Kevlar-reinforced ABS\/PLA composites through FDM process","volume":"11","author":"Ray","year":"2025","journal-title":"Hybrid Adv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5623","DOI":"10.1002\/pc.26876","article-title":"Development of artificial intelligence-based neural network prediction model for responses of additive manufactured polylactic acid parts","volume":"43","author":"Singh","year":"2022","journal-title":"Polym. Compos."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Malleswari, V.N., Manaswy, G.K., and Pragvamsa, P.G. (2023). Prediction of surface roughness for fused deposition in fabricated work pieces by RSM and ANN technique. Mater. Today Proc., in press.","DOI":"10.1016\/j.matpr.2023.03.378"},{"key":"ref_30","first-page":"251","article-title":"Experimental and Investigation of ABS Filament Process Variables on Tensile Strength Using an Artificial Neural Network and Regression Model","volume":"27","author":"Hamed","year":"2024","journal-title":"Al-Nahrain J. Eng. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1007\/s12008-025-02430-3","article-title":"Fused deposition modeling component quality enhancement by experimental investigation and ANN prediction","volume":"20","author":"Sabry","year":"2026","journal-title":"Int. J. Interact. Des. Manuf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1007\/s12541-025-01229-2","article-title":"Prediction of dimensional accuracy and surface quality in additively manufactured biomedical implants using ANN","volume":"26","author":"Karadag","year":"2025","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5849","DOI":"10.1007\/s00170-024-14092-8","article-title":"Comparison of the performances of Statistical and Artificial Neural Network models in the prediction of geometry and density of PLA\/wood bio-composite cubes manufactured by FDM","volume":"133","author":"Contuzzi","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1515\/mt-2025-0307","article-title":"Mechanical strength of PLA parts manufactured by FDM using RSM and fuzzy logic","volume":"68","year":"2026","journal-title":"Mater. Test."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102353","DOI":"10.1016\/j.mtcomm.2021.102353","article-title":"Experimental analysis on density, micro-hardness, surface roughness and processing time of Acrylonitrile Butadiene Styrene (ABS) through Fused Deposition Modeling (FDM) using Box Behnken Design (BBD)","volume":"27","author":"Venkatraman","year":"2021","journal-title":"Mater. Today Commun."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gao, G., Xu, F., and Xu, J. (2022). Parametric optimization of FDM process for improving mechanical strengths using Taguchi method and response surface method: A comparative investigation. Machines, 10.","DOI":"10.3390\/machines10090750"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Benal, M.G.M., GS, P.K., Tambrallimath, V., HR, G., Khan, T.M., Rajhi, A.A., and Baig, M.A.A. (2022). Influence of Short Glass Fibre Reinforcement on Mechanical Properties of 3D Printed ABS-Based Polymer Composites. Polymers, 14.","DOI":"10.3390\/polym14061182"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"09544089231223789","DOI":"10.1177\/09544089231223789","article-title":"Statistical modelling of depth milling in Ti-6Al4V using abrasive water jet machining","volume":"238","author":"Mogul","year":"2024","journal-title":"Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"108810","DOI":"10.1016\/j.ijthermalsci.2023.108810","article-title":"Analysis of bubble departure and lift-off boiling model using computational intelligence techniques and hybrid algorithms","volume":"197","author":"Quadros","year":"2024","journal-title":"Int. J. Therm. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Quadros, J.D., Nagpal, C., Khan, S.A., Aabid, A., and Baig, M. (2022). Investigation of suddenly expanded flows at subsonic Mach numbers using an artificial neural networks approach. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0276074"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"S3292","DOI":"10.1016\/j.arabjc.2013.12.028","article-title":"Use of response surface methodology for optimization of fluoride adsorption in an aqueous solution by Brushite","volume":"10","author":"Mourabet","year":"2017","journal-title":"Arab. J. Chem."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4","DOI":"10.17512\/jamcm.2018.4.07","article-title":"Study of base pressure behavior in a suddenly expanded duct at supersonic Mach number regimes using statistical analysis","volume":"17","author":"Quadros","year":"2018","journal-title":"J. Appl. Math. Comput. Mech."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1504\/IJBIC.2010.032124","article-title":"Firefly algorithm, stochastic test functions and design optimisation","volume":"2","author":"Yang","year":"2010","journal-title":"Int. J. Bio-Inspir. Comput."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"512","DOI":"10.4028\/www.scientific.net\/AMM.421.512","article-title":"Firefly algorithm for optimization problem","volume":"421","author":"Johari","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00366-012-0254-1","article-title":"Multi-objective firefly algorithm for continuous optimization","volume":"29","author":"Yang","year":"2013","journal-title":"Eng. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.advengsoft.2005.04.005","article-title":"A new optimization method: Big bang\u2013big crunch","volume":"37","author":"Erol","year":"2006","journal-title":"Adv. Eng. Softw."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1007\/s00521-014-1613-1","article-title":"A chaotic-based big bang\u2013big crunch algorithm for solving global optimisation problems","volume":"25","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hatamlou, A., Abdullah, S., and Hatamlou, M. (2011). Data clustering using big bang\u2013big crunch algorithm. Proceedings of the International Conference on Innovative Computing Technology, Tehran, Iran, 13\u201315 December 2011, Springer.","DOI":"10.1007\/978-3-642-27337-7_36"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey wolf optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Rezaei, H., Bozorg-Haddad, O., and Chu, X. (2018). Grey wolf optimization (GWO) algorithm. Advanced Optimization by Nature-Inspired Algorithms, Springer.","DOI":"10.1007\/978-981-10-5221-7"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.ijepes.2016.04.034","article-title":"Grey wolf optimization applied to economic load dispatch problems","volume":"83","author":"Pradhan","year":"2016","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3709","DOI":"10.1007\/s00521-019-04456-7","article-title":"On the improvement in grey wolf optimization","volume":"32","author":"Salgotra","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"110944","DOI":"10.1016\/j.engfailanal.2026.110944","article-title":"Effects of stacking thickness on the failure mechanisms of 3D-printed continuous fibre reinforced composite laminates","volume":"194","author":"Liu","year":"2026","journal-title":"Eng. Fail. Anal."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e38232","DOI":"10.1016\/j.heliyon.2024.e38232","article-title":"Mechanical properties of epoxy composites filled with multi-walled carbon nanotube, Nanoaluminum particles and glass fibers at elevated temperatures","volume":"10","author":"Quadros","year":"2024","journal-title":"Heliyon"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Huff, M. (2022). Residual stresses in deposited thin-film material layers for micro- and nano-systems manufacturing. Micromachines, 13.","DOI":"10.3390\/mi13122084"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2350076","DOI":"10.1142\/S0219519423500768","article-title":"Neural network predictive models to determine the effect of blood composition on the patient-specific aneurysm","volume":"23","author":"Quadros","year":"2023","journal-title":"J. Mech. Med. Biol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"114692","DOI":"10.1016\/j.mtcomm.2026.114692","article-title":"Machine Learning-Driven Backpropagation Neural Network for Robust Prediction of Surface Roughness in Ti6Al4V Abrasive Water Jet Machining with Experimental Validation","volume":"51","author":"Mogul","year":"2026","journal-title":"Mater. Today Commun."},{"key":"ref_58","first-page":"2331","article-title":"Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models","volume":"137","author":"Quadros","year":"2023","journal-title":"Comput. Model. Eng. Sci."}],"container-title":["Polymers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4360\/18\/19\/2398\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T08:32:43Z","timestamp":1790843563000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4360\/18\/19\/2398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,1]]},"references-count":58,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["polym18192398"],"URL":"https:\/\/doi.org\/10.3390\/polym18192398","relation":{},"ISSN":["2073-4360"],"issn-type":[{"value":"2073-4360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,1]]}}}