Radiography is an important technique to inspect objects, with applications in airports and hospitals. X-ray imaging is also essential in industry, for instance in food safety checks for the... Show moreRadiography is an important technique to inspect objects, with applications in airports and hospitals. X-ray imaging is also essential in industry, for instance in food safety checks for the presence of foreign objects. Computed tomography (CT) enables more accurate visualizations of an object in 3D, but requires more computation time. Spectral X-ray imaging is an important recent development to optimize these conflicting goals of speed and accuracy. This technique enables separation of detected X-ray photons in terms of energy. More information can be extracted from spectral images, which allows for better separation of materials. Deep learning is another important recent technique enabling machines to quickly carry out processing tasks, by training these with large volumes of data for these specific tasks.In this dissertation we present new processing methods that use spectral imaging and machine learning, with a special focus on industrial processes. We design a workflow using CT to efficiently generate large volumes of machine learning training data. In addition, we develop a compression method for efficient processing of large volumes of spectral data and two new spectral CT methods to produce more accurate reconstructions. The presented methods are designed for effective use in industry. Show less
Industrial manufacturing processes, such as the production of steel or the stamping of car body parts, are complex semi-batch processes with many process steps, machine parameters and quality... Show moreIndustrial manufacturing processes, such as the production of steel or the stamping of car body parts, are complex semi-batch processes with many process steps, machine parameters and quality indicators. To optimize these complex processes, for example by reducing the number of defects or increasing the throughput, a great number of requirements need to be taken into consideration. In this dissertation a framework for monitoring and optimizing these complex industrial processes is presented. The framework is specifically tailored to the production processes of Tata Steel and BMW Group. Both are industrial partners of the PROMIMOOC project. The framework consists of several components of which; preprocessing, outlier detection, predictive modeling and optimization are the main technical components that are the focus of this work. For each of these components a possible implementation is proposed and the challenges in implementing these components in an industrial manufacturing setting are discussed Show less
