{"id":15072,"date":"2025-05-22T10:03:00","date_gmt":"2025-05-22T08:03:00","guid":{"rendered":"https:\/\/www.sensitec.com\/xmr-symposium\/contributions2025\/talk18\/"},"modified":"2025-05-28T10:29:00","modified_gmt":"2025-05-28T08:29:00","slug":"talk18","status":"publish","type":"page","link":"https:\/\/www.sensitec.com\/en\/xmr-symposium\/contributions2025\/talk18\/","title":{"rendered":"talk18"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"15072\" class=\"elementor elementor-15072 elementor-15061\" data-elementor-post-type=\"page\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-89a8bb9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"89a8bb9\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-53b053e\" data-id=\"53b053e\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ce5f7c9 elementor-widget elementor-widget-heading\" data-id=\"ce5f7c9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Talk Nr. 18<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9bd2b14 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9bd2b14\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-adc0829\" data-id=\"adc0829\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d5d594e elementor-widget elementor-widget-text-editor\" data-id=\"d5d594e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n<li>Conference: 17th XMR Symposium<\/li>\n<li>Title: Machine learning based approach for the consideration of production-related variations in the simulation of magnetic sensors<\/li>\n<li>Authors: Hagen Schmidt, Tim Becker, J\u00f6rg Seewig<\/li>\n<li>Abstract: Simulations are an important tool in the development and design of magnetic sensors. The disadvantage of these simulations lies in frequent assumption of perfect conditions, without considering production-related variations. Depending on the system, these variations can have a significant effect on the sensor output signals, leading to an offset between simulation and reality. Running multiple simulations to cover all variations is often too time consuming. This weakness is addressed in this study. Machine learning techniques are used to train a model on production-related variations with simulated data. The model is then able to predict sensor signal deviations in real time based on the ideal signals. This allows designers to check their sensor design within the specified production variations without the need for additional time-consuming simulations. The approach is implemented using a virtual module of a supporting magnet and a GMR sensor array called GLM712 from Sensitec GmbH. This type of sensor is used to detect the angle of rotation and speed of tooth structures without contact. A characteristic of this type of sensor technology is the need for a specific design depending on the geometry of the tooth structure. In the manufacturing of these sensors, there are variations in sensor position, orientation and the properties of the supporting magnet, which have been shown to affect the signal. These are the variations for which the machine learning model is trained.<\/li>\n<li>Keywords: virtual measurement, simulation, AI, magnetic sensors<\/li>\n<\/ul>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6bd08fa elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6bd08fa\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7230f26\" data-id=\"7230f26\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-88a97fb elementor-align-center elementor-widget elementor-widget-button\" data-id=\"88a97fb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/www.sensitec.com\/wp-content\/uploads\/2025\/05\/18.-Schmidt_Becker_Machine-Learning-based-Approach-to-include-Production-Related-Variations.pdf\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Conference paper<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Talk Nr. 18 Conference: 17th XMR Symposium Title: Machine learning based approach for the consideration of production-related variations in the simulation of magnetic sensors Authors: Hagen Schmidt, Tim Becker, J\u00f6rg Seewig Abstract: Simulations are an important tool in the development and design of magnetic sensors. The disadvantage of these simulations lies in frequent assumption of [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":0,"parent":15053,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-15072","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>talk18 - Sensitec<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.sensitec.com\/en\/xmr-symposium\/contributions2025\/talk18\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"talk18 - Sensitec\" \/>\n<meta property=\"og:description\" content=\"Talk Nr. 18 Conference: 17th XMR Symposium Title: Machine learning based approach for the consideration of production-related variations in the simulation of magnetic sensors Authors: Hagen Schmidt, Tim Becker, J\u00f6rg Seewig Abstract: Simulations are an important tool in the development and design of magnetic sensors. 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