Integration of AI-Driven Predictive Maintenance in Nano-Positioning Platforms for High-Throughput Scanning Applications

Integration of AI-Driven Predictive Maintenance in Nano-Positioning Platforms for High-Throughput Scanning Applications | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 15 May 2025 V1 Latest version Share on Integration of AI-Driven Predictive Maintenance in Nano-Positioning Platforms for High-Throughput Scanning Applications Author : Emmanuel Idowu 0009-0009-4245-0599 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174733465.59937940/v1 178 views 113 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Nano-positioning platforms are crucial for high-throughput scanning applications, such as scanning probe microscopy and nanolithography, where precision and reliability are paramount. However, these systems are subject to wear, drift, and other performance degradation over time, which can lead to significant downtime and costly repairs. Predictive maintenance, powered by artificial intelligence (AI), has emerged as a promising solution to enhance the reliability and efficiency of these platforms. This paper presents an AI-driven predictive maintenance framework for nano-positioning systems, aiming to forecast potential failures and optimize maintenance schedules in real-time. By integrating machine learning models-specifically supervised learning, unsupervised anomaly detection, and hybrid models-into the system's feedback loop, the framework can predict failures before they occur, thereby minimizing downtime and maximizing throughput. The approach uses sensor data collected from the positioning system to continuously monitor and analyze its performance, identifying early signs of wear or malfunction. Experimental results demonstrate that the AI-driven predictive maintenance system significantly improves system uptime, reduces maintenance costs, and enhances overall scan performance. This paper discusses the architecture, methodology, and experimental validation of the system, highlighting its potential to revolutionize maintenance practices in high-precision nanopositioning applications. Supplementary Material File (integration of ai.pdf) Download 216.23 KB Information & Authors Information Version history V1 Version 1 15 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords ai-driven predictive maintenance anomaly detection failure prediction high-throughput scanning machine learning maintenance optimization nano-positioning systems nanolithography real-time monitoring scanning probe microscopy Authors Affiliations Emmanuel Idowu 0009-0009-4245-0599 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 178 views 113 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Emmanuel Idowu. Integration of AI-Driven Predictive Maintenance in Nano-Positioning Platforms for High-Throughput Scanning Applications. Authorea . 15 May 2025. DOI: https://doi.org/10.22541/au.174733465.59937940/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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