Data from Technical University Munich (TU Munich) Advance Knowledge in Machine L earning (Ultrasonic Mode Conversion for In-line Foam Structure Measurement In Hi ghly Aerated Batters Using Machine Learning)
Data from Technical University Munich (TU Munich) Advance Knowledge in Machine L earning (Ultrasonic Mode Conversion for In-line Foam Structure Measurement In Hi ghly Aerated Batters Using Machine Learning)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating in Freising-Weihenstepha n, Germany, by NewsRx journalists, research stated, "An ultrasonicbased method was developed to enable in-line measurements of foam structure parameters for hi ghly aerated batters by mode conversion. Biscuit batters were foamed to differen t degrees (density: 364-922 g/L) by varying the mixing head speed and pressure." Financial support for this research came from Bundesministerium fur Wirtschaft u nd Klimaschutz. The news reporters obtained a quote from the research from Technical University Munich (TU Munich), "Density and foam structure changes were detected by efficie nt offline analytics (nref measurement = 96). Ultrasonic signal data were record ed using two ultrasonic sensors attached to an industry-standard tube. Mode conv ersion effects in the ultrasonic signals were obtained to predict the rheologica l parameters of the batters. The frequency range in which surface waves are expe cted was particularly suitable for detecting rheological changes in highly aerat ed batters. An ultrasonic-based, online-capable method for process monitoring wa s implemented and established regarding feature selection in combination with ma chine learning and 5-fold cross-validation. The developed ultrasonic sensor syst em shows high accuracy for online density measurement (R2 = 0.98) and offers dec ent accuracy for measurements of foam structure parameters (Bubble count: R2 = 0 .95, Relative span: R2 = 0.93, Sauter diameter: R2 = 0.83)."
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Freising-Weihenstephan/Germany/Europe/Cyborgs/Emerging Technologies/Machine Learning/Technical University Munich ( TU Munich)
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