Machine Learning-Aided Synthesis Approach for 3D Printed Ridge Gap Waveguides

Machine Learning-Aided Synthesis Approach for 3D Printed Ridge Gap Waveguides | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 September 2025 V1 Latest version Share on Machine Learning-Aided Synthesis Approach for 3D Printed Ridge Gap Waveguides Authors : Mohammed Farouk Nakmouche 0000-0003-1152-2800 [email protected] , Dominic Deslandes , and Ghyslain Gagnon Authors Info & Affiliations https://doi.org/10.22541/au.175683870.05010119/v1 209 views 168 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In this paper, we propose a scalable machine learning (ML)-based inverse design approach for the automatic design of ridge gap waveguide (RGW) unit cells, targeting frequency bands and applications ranging from 3 to 300 GHz. This approach aims to improve reliability and reduce the high computational costs typically associated with the trial-and-error method or conventional optimization techniques in electromagnetic (EM) simulators, especially when the target frequency band changes. The proposed ML-based approach significantly reduces computation time, making predictions in less than 1 minute, compared to the 120, 72, and 96 hours required by the trial-anderror method, PSO, and GA optimizers, respectively. To validate this method, we designed a 2-port RGW structure operating within the 24-26 GHz range, tailored for the 5G n258 band (24-24.25 GHz) and midband applications (24.2-25.5 GHz). We conducted an experimental validation by fabricating the 2-port RGW structure with overall dimensions of 15 mm × 15 mm × 5.5 mm using laser powder bed fusion (LPBF) technology and measuring it with standard methods. The results demonstrate a measured return loss below-25 dB across the operating band and an average insertion loss of ≈ 1.5 dB, underscoring the effectiveness of the proposed ML-based design approach and the potential of 3D-printed technology for RGW-based component prototyping. Supplementary Material File (machine learning-aided synthesis approach for 3d printed ridge gap waveguides.pdf) Download 3.29 MB Information & Authors Information Version history V1 Version 1 02 September 2025 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License Keywords 3d printing additive manufacturing automatic modeling machine learning ridge gap waveguide Authors Affiliations Mohammed Farouk Nakmouche 0000-0003-1152-2800 [email protected] View all articles by this author Dominic Deslandes View all articles by this author Ghyslain Gagnon View all articles by this author Metrics & Citations Metrics Article Usage 209 views 168 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mohammed Farouk Nakmouche, Dominic Deslandes, Ghyslain Gagnon. Machine Learning-Aided Synthesis Approach for 3D Printed Ridge Gap Waveguides. Authorea . 02 September 2025. 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