Modern aeronautical structures are increasingly made of composite materials due to their well-known benefits. Optimizing the design of aerospace composites vis-à-vis the entire range of operational constraints (i.e. reliability, stability, strength, weight, noise, manufacturability and cost) to which the aircraft structures are subject, results in a particularly challenging task for the structural designer. Despite the volume of recent work dedicated to new Multidisciplinary Design Optimization (MDO) models and techniques, the ‘No free lunch theorem in optimisation’ is constantly confirmed. A genuine need is therefore identified for a programme that will: i)Develop, deliver and implement novel and efficient structural MDO technological tools for the European aerospace industry, ii)Nurture and train the next European generation of MDO research professionals. OptiMACS has an intersectoral character, drawing know-how from both academic and industrial research and innovation teams. It also has an intensely multi-disciplinary character, coupling expertise from mechanical, aerospace, manufacturing and software engineering, as well as from the area of applied mathematics. On the research side, OptiMACS will focus on improving the accuracy and efficiency of the MDO platform currently employed by AIRBUS. This will be achieved by enhancing the design models and criteria related to composites failure and manufacturing, developing and implementing multiscale models for composites as well as investigating advanced MDO algorithms and architectures for enhancing efficiency. On the training side, OptiMACS will provide a fully supportive environment for 5 ESRs. A training programme aiming at developing both the research as well as the transferable skills of the Fellows has been designed. All Fellows will have the opportunity to work in a multi-disciplinary environment, spending at least 50% of their time at the premises of the industrial beneficiaries.

The goal of COCOBOTS is to develop conversational assistants and cobots capable of interacting with human coworkers in sophisticated ways. One crucial such way is through the development of a natural language programming toolkit that will allow human users to teach new actions to conversational cobots and construct joint actions with them through natural conversation in an interactive way. Programming through conversation would allow a human user without sophisticated programming skills or access to massive amounts of training data to program an assistant or cobot on the spot in the way that we teach other humans, without having to rely on an expert programmer to intervene. One could try out an idea with the robot and then modify it just as one would do with another human when teaching or developing a joint action. Such a toolkit would open up a wide range of new markets for companies, such as LINAGORA, who specialize in the development of conversational assistants or cobots that need to perform actions such as alerting workers on an assembly line to malfunctioning equipment or physically intervening to fix that equipment. It would also bring increased value to companies, such as Airbus, that seek to boost their manufacturing output by adding cobots to assembly lines or maintenance tasks. For the moment, the utility of conversational assistants or cobots is limited to carrying out commands and performing actions that are pre-defined via hard-coding or, in the case of robots, learned through demonstration or manual manipulation. A natural language programming toolkit would give a user without programming expertise the power to adapt their assistant to their needs via on the spot training. To demonstrate the efficacy of our toolkit, COCOBOTS will develop a proof of concept featuring a simulated assembly cobot that is able to learn new concepts associated with manufacturing, such as a torx, and new actions, such as how to build a certain kind of bridge, by stringing together atomic actions as instructed by a human user. Bringing together conversational models with the capacity of cobots to physically interact in their environments will be crucial for testing our approach, as we think that the capacity to understand situated conversation (and in fact, any conversation at all) is greatly enhanced by physical interaction with the outside world. Observing a robot's interaction with objects in a physical environment or its ability to string together primitive actions based on multimodal, conversational instructions, will also provide clear criteria to evaluate our approach and show that following a program specified via natural language is more effective than hard coding, demonstration, or manual manipulation of a robot. To develop the model of multimodal dialogue needed to make cobots truly conversational, we will build on a solid foundation of expertise of COCOBOTS members in semantic grounding (ANITI/CerCo, Airbus), dialogue models (University of Potsdam, LINAGORA, ANITI/IRIT), conversational assistants (LINAGORA) and robotics (ANITI/LAAS, Airbus). The novelty of our approach will lie in bringing together work on semantic and conversational grounding, which is generally pursued by separate communities, to develop a hybrid model that exploits the way that these processes influence each other. Our approach will require us to overcome three major challenges. First, we will need to bring the compositionality of referential meaning to bear on the semantic grounding of complex expressions using a hybrid AI approach. Second, we will need to account for the different ways that the nonlinguistic environment can ground and contributed to discourse meaning. Third, we will need to develop a model of situation discourse that provides a symbolic skeleton that we can then flesh out with subsymbolic pairing of nonlinguistic content with linguistic expressions.

The accretion of ice represents a severe problem for aircraft, as the presence of even a scarcely visible layer can severely limit the function of wings, propellers, windshields, antennas, vents, intakes and cowlings. The PHOBIC2ICE Project aims at developing technologies and predictive simulation tools for avoiding or mitigating this phenomenon. The PHOBIC2ICE project, by applying an innovative approach to simulation and modelling, will enable the design and fabrication of icephobic surfaces with improved functionalities. Several types of polymeric, metallic and hybrid coatings using different deposition methods will be developed. Laser treated and anodized surfaces will be prepared. Consequently, the Project focuses on collecting fundamental knowledge of phenomena associated with icephobicity issues. This knowledge will give better understanding of the ice accretion process on different coatings and modified surfaces. Certified research infrastructure (ice wind tunnel) and flight tests planned will aid in developing comprehensive solutions to address ice formation issue and will raise the Project’s innovation level. The proposed solution will be environment-friendly, will contribute to the reduction of energy consumption, and will help eliminate the need for frequent on-ground de-icing procedures. This in turn will contribute to the reduction of cost, pollution and flight delay.

The PUMA project sets clear and measurable main objectives to reach a TRL 5 from TRL 3 as follows: 1. Define future FPGA requirements for high performance space applications. 2. Provide benchmark to assess technology requirements 3. Define future FPGA architecture to meet applications requirements 4. Design of all FPGA architecture building blocks taking into consideration performance requirements 5. Test chip to validate reliability and radiation hardening performance of 7nm FinFET

The main objective of the proposal is development of active multi-functional surfaces with high level of self-healing ability on the basis of Layered Double Hydroxide (LDH) structures formed on different industrially relevant metallic substrates. The main idea of the project is based on “smart” triggered release on demand for functional organic or inorganic anionic compounds intercalated into intergallery spaces of LDHs. The active functionality is achieved via controllable substrate-governed growth of LDH architectures on Al, Mg and Zn based alloys. The functional anions such as corrosion inhibitors, biocides, drugs, or hydrophobic agents are introduced into the intergallery spaces during the growth of LDH or upon a post-treatment stage. The release of the functional agents occurs only on demand when the respective functionality is triggered by the relevant external stimuli such as presence of anions or local pH change. The proposal focuses on two main applications, namely aeronautical and automotive. The active LDH treatments can bring significant benefits when applied in these situations. The respective relevant substrates are chosen as the main objects of interest: Mg alloys for both applications; Al alloys for both transportation industries as well; galvanized steel as a main material for automobiles. Moreover the suggested surface treatments, especially the one with active self-healing ability, are also considered for light-weight multi-material structures which are prone to fast galvanically-induced corrosion. The increase of the fault tolerance and reliability of hybrid designs is aimed in this case. The suggested surface treatments can offer possibility for fast implementation of the process at industrial level. The main expected impacts are related to the improvement of the life cycle of the light-weight structures utilized in transport industries via optimization of the maintenance schedules and increasing the fault tolerance.