首页|Studies from Clarkson University Further Understanding of Machine Learning (Mach ine Learning Modeling for Ultrasonic Quality Attribute Assessment of Pharmaceuti cal Tablets for Continuous Manufacturing and Real-time Release Testing)
Studies from Clarkson University Further Understanding of Machine Learning (Mach ine Learning Modeling for Ultrasonic Quality Attribute Assessment of Pharmaceuti cal Tablets for Continuous Manufacturing and Real-time Release Testing)
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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 originating from Potsdam, New York, by NewsRx correspondents, research stated, “In in-process quality monitoring for Continuou s Manufacturing (CM) and Critical Quality Attributes (CQA) assessment for Real-t ime Release (RTR) testing, ultrasonic characterization is a critical technology for its direct, non-invasive, rapid, and cost-effective nature. In quality evalu ation with ultrasound, relating a pharmaceutical tablet’s ultrasonic response to its defect state and quality parameters is essential.” Our news journalists obtained a quote from the research from Clarkson University , “However, ultrasonic CQA characterization requires a robust mathematical model , which cannot be obtained with traditional first principles-based modeling appr oaches. Machine Learning (ML) using experimental data is emerging as a critical analytical tool for overcoming such modeling challenges. In this work, a novel D eep Neural Network-based MLdriven Non-Destructive Evaluation (ML-NDE) modeling f ramework is developed, and its effectiveness for extracting and predicting three CQAs, namely defect states, compression force levels, and amounts of disintegra nt, is demonstrated. Using a robotic tablet handling experimental rig, each attr ibute’s distinct waveform dataset was acquired and utilized for training, valida ting, and testing the respective ML models. This study details an advanced algor ithmic quality assessment framework for pharmaceutical CM in which automated RTR testing is expected to be critical in developing cost-effective in-process real -time monitoring systems.”
PotsdamNew YorkUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningClarkson University
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