![]() Therefore, you test whether the construct you are measuring 'loads' onto all (or just some) of your variables. If these variables are highly correlated, you might want to include only those variables in your measurement scale (e.g., your questionnaire) that you feel most closely represent the construct, removing the others (b) you want to create a new measurement scale (e.g., a questionnaire), but are unsure whether all the variables you have included measure the construct you are interested in (e.g., depression). There are a number of common uses for PCA: (a) you have measured many variables (e.g., 7-8 variables, represented as 7-8 questions/statements in a questionnaire) and you believe that some of the variables are measuring the same underlying construct (e.g., depression). Its aim is to reduce a larger set of variables into a smaller set of 'artificial' variables, called 'principal components', which account for most of the variance in the original variables. Principal components analysis (PCA, for short) is a variable-reduction technique that shares many similarities to exploratory factor analysis. Salem, JOM 63, 34 (2011).Principal Components Analysis (PCA) using SPSS Statistics Introduction Anandan, Applied Mechanics and Materials (Trans Tech Publ, Bach, 2016), pp 397–401.Ī. Barto, Reinforcement Learning: An Introduction (MIT Press, Cambridge, 2018). Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, Berlin, 2009). Murphy, Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, 2012). Bengio, Deep Learning (MIT Press, Cambridge, 2016). Tibshirani, An Introduction to Statistical Learning (Springer, Berlin, 2013). ![]() Tian, Detection, Estimation, and Modulation Theory Part I (Wiley, New York, 1968). The Next Step in Digital Transformation: Is Artificial Intelligence Production-ready for Green Sand Foundries? (2020). ![]() Gao, Applied Mechanics and Materials (Trans Tech Publ, Bach, 2013), pp 2129–2134. Du, in Intelligent Computing Methodologies. We conclude that ML methods are promising not only to predict various properties but also to automate microstructural analysis and optimization of manufacturing MMCs.Ī. However, ML methods (e.g., computer vision, which is suitable for microstructural characterization and defect detections) and optimization algorithms (e.g., reinforcement learning) have not been fully utilized for design, processing, and characterization of metal matrix composites despite their enormous capacities. ML algorithms have been successfully applied for prediction of mechanical, tribological, corrosion, and wetting properties of different MMCs. We have demonstrated that ML methods can be applied in three distinct categories, namely property prediction, microstructure analysis, and process optimization, which are associated with three major classes of ML techniques, i.e., regression, classification, and optimal control, respectively. ![]() In this article we provide an overview on the current and emerging applications of machine learning (ML) in the design, synthesis, and characterization of metal matrix composites (MMC).
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