High-Entropy/multicomponent rare-earth oxides (HECs and MCCs) show promise as alternative materials for thermal barrier coatings (TBC) with the ability to tailor properties based on the combination of rare-earth elements present. By enabling the substitution of scarce or supply-risk rare-earths with more readily available alternatives while maintaining comparable material performance, HECs and MCCs offer a valuable path towards alternative TBC material design. However, navigating this search space of compositionally complex materials is both time and resource intensive. In this study, an active learning (AL) framework was employed to identify HEC/MCC materials with a pyrochlore structure, with acceptable thermal conductivity (TC) for TBC applications. The AL framework was applied through a Bayesian optimisation (BO) strategy, coupled with a random forest surrogate model. TC was selected as the optimisation criterion as that is the most fundamental requirement of TBC materials. Over two iterations of the AL cycle, four compositions were generated and synthesised in the lab for experimental evaluation. The first iteration yielded two single-phase pyrochlores, ( La 0 . 29 Nd 0 . 36 Gd 0 . 36 ) 2 Zr 2 O 7 and ( La 0 . 333 Nd 0 . 26 Gd 0 . 15 Ho 0 . 15 Yb 0 . 111 ) 2 Zr 2 O 7 , with measured thermal conductivities of 2.03 and 1.90 W/mK, respectively. The surrogate model predicted a TC of 2.009 W/mK for both compositions, demonstrating its accuracy for completely new compositions. The second iteration compositions showed dual-phase when synthesised, highlighting the need to take into account phase formation in the AL framework.

The first-of-its-kind use of the active learning (AL) framework in thermal spray is adapted to enhance the prediction accuracy of the in-flight particle characteristics. The successful AL framework implementation via Bayesian Optimisation is beneficial in, first, reducing the maximum uncertainty, which greatly improves the prediction accuracy and informativeness of the existing database. Second, it reduces local uncertainty around a contrived test point that offers the capability to find improvement in a limited search area, allowing an accurate prediction around a desired test point. The dataset for Machine Learning (ML) training consists of 26 atmospheric plasma spray (APS) parameters of silicon and a further six AL-guided spray runs carried out to reduce maximum uncertainty in the initial database. On average, a 52.9% improvement (error reduction) of RMSE and an R2 increase of 8.5% were reported on the predicted in-flight particle velocities and temperatures after the AL-driven optimisation. Furthermore, the contrived test point optimisation to predict the best possible characteristics in a limited search space resulted in a three-fold increase in prediction accuracy compared to the non-optimised prediction. The AL-driven optimisation proved to be greatly beneficial for resource-intensive thermal spraying, as the framework not only allowed an accurate prediction of the in-flight particle characteristics but also found expected improvement around a desired in-flight characteristic. Furthermore, the framework uses the Gaussian Process (GP) ML model as a surrogate that generalises a global solution without necessarily involving physical and underlying mechanisms, thus extending the framework to other thermal spraying methods.

In manufacturing automation domain safety and availability are the most important factors to ensure productivity. In modern software intensive networked automation systems it became quite hard to ensure which non-functional requirements are related to these factors as well as whether these are satisfied or not. This is due to the prevalence of manual efforts in several analyses phases where complexity of the system often makes it hard to obtain comprehensive overview and thus makes it difficult to ascertain the presence of certain undesired consequences. Since design, development and following verification and validation activities are largely dependent upon the result of the analyses the product is largely affected. To address these problems automated fault tree generation is presented in this paper. It uses distinct modeling artifacts and information to automatically compose formal models of the system. Embedding hardware and network failures it is then ascertained through model checking whether the system satisfies certain safety and availability properties or not. This information is used to compose the fault tree. Proposed approach will improve completeness and correctness in fault trees and will consequently help in improving the quality of the system. Furthermore, it is also shown how the artifacts of this analysis can be used to produce test goals and test cases to validate the software constituents of the system and assure traceability between testing activity and safety requirements.

With the advent of new computing paradigms, such as Ubiquitous Computing, Ambient Intelligence, and Cyber Physical Systems, promising application domains like Ambient Assisted Living (AAL) and Car2Car have emerged. One key concern in these application domains is that systems are required to dynamically adapt in reaction to changes within the system or its environment. As a consequence, it is indispensable to incorporate corresponding facilities within systems, yielding adequate adaptation behavior to appropriately react on such dynamic changes. The definition of adaptation behavior is, however, a complex task in its own. In order to help engineers working on adaptive systems, we propose in this paper a generic testbed that allows specifying adaptation strategies and evaluating them in a runtime context to improve the adaptation behavior. Our approach further introduces basic visualization features as a means for the engineers to better evaluate the complex dynamic behavior resulting from runtime adaptation.

The distributed control applications of today are usually assumed to be run on a hardware consisting of heterogeneous processing elements. A standard like IEC 61499 attempts to provide an unanimous software infrastructure for these applications. An important research interest thereof is to find possible deployment of the components of the distributed application on these heterogeneous nodes. Since most of such applications are hard real-time ones the complexity of the problem is further increased by strict temporal restrictions. This paper addresses this issue as algorithms are presented for finding feasible and optimal solutions of such deployment problems. Moreover, the problems are also considered in the context of two different - while conforming to the standard - execution semantics realized in certain implementations of IEC 61499.

Development of distributed control applications within a standard framework was actually the strongest motivation behind the introduction of IEC 61499. Yet, scarcity of its application is due to the fact that the development is more complex as compared to the existing languages and standards. This paper addresses an UML-based development process for IEC 61499 which attempts to consider the aspects related to the utilization of the unique features that this standard brought into the field of industrial control applications. Reliability is an important consideration in this particular realm of software development and apart from formal verification tests are often performed to verify the software components. An automatic test-case generation methodology is introduced to complement the proposed development process.

IEC 61499 ushers in a new trend of software development in the area of Industrial Process Measurement and Control System (IPMCS). This new standard simplifies the development of distributed IPMCS applications through inclusion of re-usability, encapsulation and modularity. IEC 61499, due to its close resemblance with Object-Oriented (OO) paradigm also paves the way to integrate modeling techniques like UML into the development process of the distributed IPMCS applications. Yet a remarkable challenge is there to integrate or re-design the systems, elements or components designed using a monolithic language like the ones on Programmable Logic Controllers (PLCs). In this work it is attempted to share the experiences faced while attempting to migrate a PLC controlled centralized laboratory application into an IEC 61499 compliant distributed control application. The targeted distributed control system consists of network-enabled controllers where the IEC 61499 compliant control sub-applications should run. It needs to be mentioned that the development process did not start from analysis of the specification of the instrument to be controlled but rather from a formal specification of it written in Signal Interpreted Petri Nets (SIPN).