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1	Improving XRD Analysis with Machine Learning Drapeau, Rachel E. 14 August 2023 (has links) (PDF) X-ray diffraction analysis (XRD) is an inexpensive method to quantify the relative proportions of mineral phases in a rock or soil sample. However, the analytical software available for XRD requires extensive user input to choose phases to include in the analysis. Consequently, analysis accuracy depends greatly on the experience of the analyst, especially as the number of phases in a sample increases (Raven & Self, 2017; Omotoso, 2006). The purpose of this project is to test whether incorporating machine learning methods into XRD software can improve the accuracy of analyses by assisting in the phase-picking process. In order to provide a large enough sample of X-ray diffraction (XRD) patterns and their known compositions to train the machine learning models, I created a dataset of 1.5 million calculated XRD patterns of realistic mineral mixtures. These synthetic XRD patterns were calculated using crystal structure files from the American Mineralogist Crystal Structure Database (AMCSD) with mineral occurrence data from the Mineral Evolution Database (MED) to mimic geologic knowledge used by expert analysts. Using this dataset, I trained and refined a variety of machine learning models to determine which model is most accurate in identifying the correct mineral phases. X-ray diffraction analysis XRD machine learning Rietveld method crystal structure classification decision trees bagged decision trees data generation mineral mixture Physical Sciences and Mathematics
2	REAL-TIME PREDICTION OF SHIMS DIMENSIONS IN POWER TRANSFER UNITS USING MACHINE LEARNING Jansson, Daniel, Blomstrand, Rasmus January 2019 (has links) No description available. MATLAB Machine Learning Smart Manufacturing Classification Regression Power Transfer Units Industry 4.0 Assembly Line Support Vector Machines Artificial Neural Network Partial Least Squares Regression Decision Tree Bagging Bagged Decision Trees Support Vector Regression Machines SVR SVM ANN PTU DT Bagged DTs Robotics Robotteknik och automation

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Improving XRD Analysis with Machine Learning

REAL-TIME PREDICTION OF SHIMS DIMENSIONS IN POWER TRANSFER UNITS USING MACHINE LEARNING