[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119961-en":3,"doc-seo-119961-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119961,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","MACHINE LEARNING - ENHANCED MASS FLOW MEASUREMENTS USING A CORIOLIS MASS FLOW SENSOR","Machine learning-enhanced mass flow measurements are presented for microfabricated Coriolis mass flow sensors with integrated pressure and temperature sensing. Linear regression and support vector regression are trained on four features extracted from raw sensor signals to improve mass flow estimation. Using information from all integrated sensors, the proposed regression-based approach increases full-scale accuracy by a factor of 4 for trained fluids, compared with conventional mass flow detection that relies on fewer signals.","MACHINE LEARNING-ENHANCED MASS FLOW MEASUREMENTS USING A CORIOLIS MASS FLOW SENSOR  \nR. Zubavicius, D. Alveringh1 , M. Poel2 , J. Groenesteijn1,3 ,  \nR. G. P. Sanders1 , R. J. Wiegerink1 , J. C. Lo¨tters1,3  \n1 MESA+ Institute, University of Twente, Enschede, The Netherlands  \n2 Data Management and Biometrics, University of Twente, Enschede, Netherlands  \n3 Bronkhorst High-Tech BV, Ruurlo, The Netherlands  \nABSTRACT  \nWe present machine learning-enhanced mass flow measurements based on microfabricated Coriolis mass flow sensors with integrated pressure and temperature sensors. Two machine learning techniques have been applied: linear regression (LR) and support vector regression (SVR) on four features extracted from the raw sensor data to improve mass flow estimation. In contrast to conventional mass flow detection, LR and SVR use information from all integrated sensors to estimate the mass flow, which results in a full-scale accuracy improvement of a factor 4 for trained fluids.  \nKEYWORDS  \nCoriolis, mass flow, machine learning, linear regression, support vector regression, sensors, microfluidics.  \nINTRODUCTION  \nMiniaturization of flow sensors using microfabrication techniques has enabled the measurement and control of very low flows, i.e., in the order of a few micro or nano liters per minute [1] . Accurate flow sensing and control has applications in pharmaceutical, biomedical and many other fields [2, 3, 4] . Thermal flow sensors are capable of measuring down to a few nano liters per minute. However, thermal flow sensing principles are highly dependent on liquid properties; the sensors need to be calibrated per liquid [5] .  \nIn contrast to thermal flow sensors, Coriolis mass flow sensors (CMFS) are capable of measuring the true mass flow without the need for calibration per fluid. Last decades, a few different fabrication techniques have been developed to miniaturise these type of flow sensors [6, 7, 8] . The latter has been used to integrate multiple fluid sensors into a single chip [9] and to find accuracy limits for CMFS [10, 11] . Recent work has focused on applying machine learning for fluid classification [12] and extraction of fluid parameters [13] . In the work presented in this paper, the improvement of the accuracy for mass flow sensing by applying machine learning to the raw sensor signals has been investigated.  \nFigure 1: Illustration of the proposed principle using a CMFS, temperature and pressure sensors, with (a) the chip,(b) the swing and twist mode of the CMFS,(c) the pressure sensor, (d) different fluids, (e) five output signals, (f) preprocessing and (g) the regression-based machine learning. Adapted from [12].  \nSENSOR THEORY AND DESIGN  \nThe sensor chip consists of a CMFS, two pressure sensors (upstream and downstream with respect to the CMFS) and a temperature sensor, see Figure 1a. The  \n2  \n0  \n2  \n4  \nThe 5th Conference on MicroFluidic Handling Systems , 21–23 February 2024 , Munich , Germany  \n65  \nCMFS utilizes the Coriolis principle, where inertia caused by a fluid flow ('m) in a twisting microchannel induces a swing mode (Figure 1b) . The swing mode is proportional to the mass flow and is theoretically independent of fluid properties. However, temperature and pressure influence the movement of the channel [11, 14], which includes the magnitude of the swing mode. Therefore use of the signals from the already integrated temperature and pressure sensors potentially improves the accuracy.  \nThe CMFS has two capacitive readout structureson each side with respect to the twist mode axis. The phase shift at the fundamental frequency between these electrodes is proportional (for low flows) to the mass flow [8] . The pressure sensors consist of a membrane that deforms proportional to the pressure in the channel. To measure the pressure, there are golden resistive tracks deposited on top of the channel and connected in Wheatstone bridge configuration. On top of the CMFS, there are also a resistive track deposited to enable temperature","cbCaitUtRYWu0s5m","https://ap.wps.com/l/cbCaitUtRYWu0s5m","pdf",1581956,1,4,"English","en",105,"# Abstract\n# Introduction\n# Sensor Theory and Design\n# Regression Based Approach","[{\"question\":\"Which machine learning methods are used to enhance mass flow estimation?\",\"answer\":\"The study applies linear regression (LR) and support vector regression (SVR) to improve mass flow estimation from extracted sensor features.\"},{\"question\":\"What sensor signals and features are used as inputs for the regression models?\",\"answer\":\"Four features are selected: phase shift Δ', resonance frequency f0, temperature-dependent track resistance R, and upstream pressure P1, derived from raw CMFS signals.\"},{\"question\":\"How much accuracy improvement is reported compared with conventional mass flow detection?\",\"answer\":\"For trained fluids, the regression approach yields a full-scale accuracy improvement by a factor of 4.\"}]","MACHINE LEARNING - ENHANCED MASS FLOW MEASUREMENTS USING A CORIOLIS MASS FLOW SENSOR | PDF",1785727237,10,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"machine-learning-enhanced-mass-flow-measurements-using-a-coriolis-mass-flow-sensor","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/machine-learning-enhanced-mass-flow-measurements-using-a-coriolis-mass-flow-sensor/119961/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Which machine learning methods are used to enhance mass flow estimation?","Question",{"text":74,"@type":75},"The study applies linear regression (LR) and support vector regression (SVR) to improve mass flow estimation from extracted sensor features.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What sensor signals and features are used as inputs for the regression models?",{"text":79,"@type":75},"Four features are selected: phase shift Δ', resonance frequency f0, temperature-dependent track resistance R, and upstream pressure P1, derived from raw CMFS signals.",{"name":81,"@type":72,"acceptedAnswer":82},"How much accuracy improvement is reported compared with conventional mass flow detection?",{"text":83,"@type":75},"For trained fluids, the regression approach yields a full-scale accuracy improvement by a factor of 4.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]