DEVELOPMENT OF MACHINE LEARNING TECHNIQUES FOR APPLICATIONS IN THE STEEL INDUSTRY
thesisposted on 08.05.2020, 01:28 by Alex Joseph Raynor
For a long time, the collection of data through sensors and other means was seen as inconsequential. However, with the somewhat recent developments in the areas of machine learning, data science, and statistical analysis, as well as in the rapid growth of computational power being allotted by the ever-expanding computer industry, data is not just being seen as secondhand information anymore. Data collection is showing that it currently is and will continue to be a major driving force in many applications, as the predictive power it can provide is invaluable. One such area that could benefit dramatically from the use of predictive techniques is the steel industry. This thesis applied several machine learning techniques to predict steel deformation issues collectively known as the hook index problem .
The first machine learning technique utilized in this endeavor was neural networking. The neural networks built and tested in this research saw the use of classification and regression prediction models. They also implemented the algorithms of gradient descent and adaptive moment estimation. Through the employment of these networks and learning strategies, as well as through the line process data, regression-based networks made predictions with average percent error ranging from 106-114%. In similar performance to the regression-based networks, classification-based networks made predictions with average accuracy percentage ranges of 38-40%.
To remedy the problems relating to neural networks, Bayesian networking techniques were implemented. The main method that was used as a model for these networks was the Naïve Bayesian framework. Also, variable optimization techniques were utilized to create well-performing network structures. In the same vein as the neural networks, Bayesian networks used line process data to make predictions. The classification-based networks made predictions with average accuracy ranges of 64-65%. Because of the increased accuracy results and their ability to draw causal reasoning from data, Bayesian networking was the preferred machine learning technique for this research application.