Microstructural development during the quenching and partitioning process in a newly designed low-carbon steel

This paper presents a detailed characterization of the microstructural development of a new quenching and partitioning (Q&P) steel. Q&P treatments, starting from full austenitization, were applied to the developed steel, leading to microstructures containing volume fractions of retained austenite of up to 0.15. The austenite was distributed as films in between the martensite laths. Analysis demonstrates that, in this material, stabilization of austenite can be achieved at significantly shorter time scales via the Q&P route than is possible via a bainitic isothermal holding. The results showed that the thermal stabilization of austenite during the partitioning step is not necessarily accompanied by a significant expansion of the material. This implies that the process of carbon partitioning from martensite to austenite occurs across low-mobility martensite–austenite interfaces. The amount of martensite formed during the first quench has been quantified. Unlike martensite formed in the final quench, this martensite was found to be tempered during partitioning. Measured volume fractions of retained austenite after different treatments were compared with simulations using model descriptions for carbon partitioning from martensite to austenite. Simulation results confirmed that the carbon partitioning takes place at low-mobility martensite–austenite interfaces.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.OLD Metals Processing, Microstructures and PropertiesOLD Surface and Interface Engineerin

Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing

Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment. Common approaches consider service composition as a decision problem whose solution is usually addressed from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host's mobility, and time constraints to tailor composition plans. Thus, our main contribution is the development of a cognitively-inspired agent-based service composition model focused on bounded rationality rather than optimality, which allows the system to compensate for limited resources by selectively filtering out continuous streams of data. Our approach exhibits features such as distributedness, modularity, emergent global functionality, and robustness, which endow it with capabilities to perform decentralized service composition by orchestrating manifold service providers and conflicting goals from multiple users. The evaluation of our approach shows promising results when compared against state-of-the-art service composition models.Comment: This paper will appear on AIMS'19 (International Conference on Artificial Intelligence and Mobile Services) on June 2

Embedding Intelligence Within Data Points for a Machine Learning Framework: “Hex-Elementization”

This paper presents a unique concept of Hex-Elementization (Hex-E) that is applied to data points in order to enable increasingly sophisticated utilization of big data. The premise of this paper is to define data points through 6 attributes which “learn” to connect automatically with other data points through Machine Learning (ML) resulting in suites of analytics. These analytics can grow in multiple directions depending on the needs of the business and the intelligence encoded within the data points through the Hex-E framework. This paper is part of ongoing research that is following a mixed-methods approach to propose, develop and validate Hex-E