Obtaining optimal properties in various places of a structure is a major issue in metallic additive manufacturing or repair. The solution is based on an in-depth knowledge of the links between properties and microstructures and their control during the entire process. Moreover the link is present at different time and space scales and controls the solidification process as well as the evolution of the microstructure during the subsequent thermomechanical cycles. The aim of the MIFASOL project is to propose a manufacturing strategy to control jointly geometry and microstructure for direct energy deposition (DED) processes. However, such strategies come up against three main scientific and technical obstacles. The first is that any control strategy requires predictive simulations of the formation and evolution of the microstructure during the process. The second is due to the real-time control strategies necessary to adjust the process parameters to avoid a drift in thermal kinetics. The third difficulty concerns the definition of the manufacturing strategy and the control of the evolution of process parameters to guarantee geometry and microstructure. The MIFASOL project therefore proposes: 1) rapid models coupling temperature and microstructure formation / evolution on the scale of the whole process, allowing to establish a manufacturing strategy, 2) in-situ measurements coupled with machine-learning algorithms to correct in real time the manufacturing parameters and 3) precise modeling and control of the kinematics of the material deposition in order to define the manufacturing strategy in the case of complex structures. The expected results of the project are: 1) an efficient fast calculation tool to simulate heat transfers as a function of all the process parameters as well as the formation and evolution of microstructures, 2) an experimental setup allowing in-situ temperature measurements of a large part during the process as well as a neural network (trained on a large number of simulations) allowing to use this measurement in real time to correct the manufacturing parameters and achieve the desired microstructure and 3) the creation of a digital twin based on the digital additive manufacturing chain, integrating knowledge and models allowing the synthesis of deposit strategies by performing virtual testing of the process or in real time by coupling digital models and in-situ measurements. The MIFASOL project therefore will clearly work on different complementary analysis paths: measurements and analyzes in real time associated with fast simulations of the process. It is therefore interested in materials and processes, but being resolutely turned towards innovative measurement and control instrumentations, control-command learning techniques by neural networks in order to propose a better integration of additive manufacturing among innovative technologies allowing simultaneous optimization of the material, its microstructure and the manufactured part. The success of the project therefore rests on the perfect synergy between the project partners and by the recruitment of two doctoral students as part of the project, one responsible for making the link between the fast models and the manufacturing strategies in order to go towards the development of a digital twin and the second responsible for carrying out quantitative in-situ measurements coupled with real-time monitoring by neural network.

The industrial use of components manufactured using additive manufacturing (AM) processes has strong growth potential in various challenging fields such as aeronautics, automotive, medical or nuclear. The desire to produce structural parts implies that they are inevitably subjected to Non-Destructive Testing (NDT) and that it is essential to master the process during manufacturing to give the parts a microstructure with the characteristics that make them usable. Implementing an online NDT monitoring strategy would lead to more efficient and optimal control by acting on the AM parameters to avoid process drifts or by repelling defective parts as soon as possible. However, despite increasingly developed studies on the influence of AM parameters on the resulting microstructures, actual knowledge remains incomplete, particularly for the wire-laser process (WLAM), which is more recent and of which certain advantages arouse growing interest among industrials. The availability of an online NDT would allow a significant advance in AM. COLUMBO aims to demonstrate the ultrasonic-laser (UL) controllability of WLAM parts by quantifying the material parameters that make these controls possible and by defining the detectable characteristic quantities, in order to prove the feasibility of an effective online monitoring strategy. Ultrasonic NDT methods, proven by their fundamental sensitivity to local mechanical characteristics, are methods of choice both for the flaws probing (heat-affected zones, microporosities or cracks) and for the multiscale characterization of microstructures. Besides, UL techniques allow contactless inspection in hostile environments, such as the AM. Finally, the use of Rayleigh waves seems well suited to successively control and characterize the deposited metal layer-by-layer. It is clear that the efficiency of such an NDT procedure can only be guaranteed subject to a relevant physical interpretation and optimal use of the data of the real-time online control data. However, the WLAM parts constitute a challenge because of the complex phenomena of ultrasonic diffusion linked to their very marked microstructures (surface roughness, porosities, entanglement of columnar/equiaxial grains), very different from those resulting from conventional metallurgical processes. The associated signals, potentially rich in information, are therefore complex to interpret. The scientific issue to be addressed is the mastery of the correlation between the WLAM parameters, the characteristics of the obtained microstructure and its ultrasonic signature. COLUMBO proposes to meet this challenge by developing multiscale and multi-physics modelling/ simulations of the WLAM process and the ultrasound propagation, both by comparing with characterization data and experimental measurements. Thanks to the manufacturing/measurement/modelling complementarity between the five partners, the considered methodology consists of establishing a hybrid benchmark of the carefully chosen parts with a microstructure of increasing complexity and with sets of well-identified and classified WLAM parameters, to continuously advance the knowledge on involved phenomena, the modelling of underlying physical mechanisms, and the quantification of measurable quantities. The ultimate goal is an optimal exploitation of real-time in situ control data using simplified models with validated physical content, and based on machine learning (ML). To achieve this ambitious and topical objective, COLUMBO brings together a consortium of five partners with skills that fulfil the entire chain of expertise required: from WLAM process modelling/simulation, to online testing of WLAM process, including ultrasound modelling/simulation and characterization (EBSD), towards inversion of data by ML. This complementarity with a fair balance between theoretical/numerical modelling and experimental validation constitutes the key point of the rigor and the success of COLUMBO.

The development of innovative manufacturing processes leads to design innovative and optimized products with both complex architecture and original geometries. These technological breakthroughs forced the engineers to rethink the way we design and integrate these new functionalities. The control of the geometric variability of mechanical systems is based on the modeling of tolerances and the main objective is to qualify the conformity of a mechanical system with respect to functional requirements (clearance, flush, alignment, etc.) according to the geometric variations of the parts. Actually, we are facing to a significant disrupting between the continuous increasing functionalities and capacities of production and metrology equipment, while tolerance simulation tools have serious limitations, mainly due to the complexity of the mathematical tools to be implemented. Closing these critical gaps to obtain realistic simulations of the behavior of the assemblies requires major developments at two different scales: • At the part level, to integrate representative geometric details of the production processes, • At the mechanism level, to integrate a contact architecture coupled with a behavior representative of the functioning of a mechanical system. At the part scale, the integration of shape defects drastically increases the complexity of contact modeling. This leads tolerance analysis tools to make assumptions that limit the type of part variations while specializing the types of architecture (isostatic or hyperstatic), integrating (or not) the mobility of the mechanism, addressing (or not) local deformations of contact surfaces and part stiffness. Due to the complexity of the physical phenomena to address, it is necessary to rethink and develop an adaptive acuity of the system modeling in relation to the functional requirements to be met. The AToPAd project aims to overcome these scientific bottlenecks, in particular by developing realistic tools for characterizing shape defects at the part scale. The integration of these defects must be combined with the deformation of the contact surfaces. Simulation of these defects and deformations at the mechanism scale will ensure continuity between these different scales and validate their overall performance. To this end, a further study of research works in geometric for a multi-physical tolerancing approach is proposed, considering a discrete and realistic representation of shapes (Skin Model Shapes) developed by LURPA coupled with tolerance analysis methods based on polyhedra models carried out in I2M. Through this innovative coupling, the project will contribute: • To characterize the geometric variability throughout the product life cycle, • To simulate the realistic behavior of mechanism and to develop new theoretical basis for multi-physical tolerance. The theoretical developments and digital tools implemented in the AToPAd project will systematically validate on laboratory examples, on first, and then applied on industrial mechanism proposed by industrial partners. One of the major originality of this project is that the developed tools will be shared in open source format. In addition, a CAD library of studied systems will be freely available. Finally, guidelines will be defined concerning the acuity level to be met and the associated modeling to develop according to the functional requirement and expected behavior of the system.

The overall objective of the EXOMAN project is to improve the symbiosis between humans and exoskeletons. To do so, we aim to advance fundamental knowledge about Human-Exoskeleton Interaction (HEI) by focusing on the human dimension. The potential use of exoskeletons holds much promise in the fields of ergonomics and healthcare, be it for preventing musculoskeletal disorders or overcoming motor deficits. Regardless of the specific end-goal (e.g. augmentation of an operator’s physical capacity or physical assistance for a patient), active exoskeletons may provide a means to assist the movements of a patient/worker with all the advantages offered by robotics: repeatability, accuracy, adaptability. Following an exponential increase in exoskeleton research over the last decade, several types of robotic exoskeletons have been developed. In particular, upper-limb exoskeletons have generated considerable interest, with many potential applications related to reaching and manipulation of objects in clinical and industrial settings. To date however, the generalization of this technology from research into practical applications (i.e. out of the lab) has been limited. Furthermore, the benefits of these devices over existing techniques (e.g. proactive ergonomics or occupational therapy) have not been scientifically established. Clearly, certain aspects of exoskeleton design are limiting their effectiveness and applicability in real life applications. Beyond the inherent technological challenges (actuators, large weight, energy supply...), a fundamental issue is our limited understanding of human motor control in interaction with an exoskeleton. Our research hypothesis is that breakthroughs in robotic exoskeletons will go hand in hand with a better comprehension of the human contribution to HEI. Quantifying and deciphering how humans adapt to moving while wearing an active upper-limb exoskeleton (and why they do so) will thus be a leading theme of this project. The EXOMAN project will be organized around 4 scientific work packages. Experimental tests will be conducted with ABLE, a highly-reversible upper-limb robotic exoskeleton. Firstly, a technological platform to measure an exhaustive set of human movement parameters (kinematics, dynamics, and energetics) during HEI will be established (work package 1). This will allow conducting exhaustive analyses of human motor behavior in interaction with ABLE (work package 2). Different movement patterns should be observed as a function of the control applied by the exoskeleton and the physical coupling of the person’s upper limb to the exoskeleton. Two complementary research efforts are thus proposed. On the one hand, anthropomorphic high-level control laws for the upper limb exoskeleton will be developed (work package 3). On the other hand, the design of physical interfaces between the person and the exoskeleton will be optimized (work package 4). A standard applied task will be to help/assist an operator to move their limb and a load, from one location to another, in an ecological, comfortable and effortless way. In summary, this interdisciplinary project involving motor control scientists and roboticists will tackle both fundamental and technological issues to boost HEI research and bring active exoskeletons closer to real world applications.

Producing complex shaped parts with high added value by hybridizing WXAM and 5-axis machining processes is a major challenge that may contribute to the competitiveness of companies. This requires obtaining rough parts of "near net shape" quality to minimize machining operations and requires controlling the parameters and execution of the WXAM process on 6-axis anthropomorphic robots or 5-axis machine tools. Finally, this necessarily relies on the development of a digital chain integrating additive manufacturing processes and which is lacking, on a topological division of parts into entities and an optimal sequence of operations, based on thermal, geometrical and kinematical constraints as well as new trajectories. Thus, the objective of the AWESOME project is to contribute to the development of an integrated hybrid manufacturing process for the production of complex shaped parts. More specifically, the aim is to develop manufacturing strategies by hybridization between Directed Energy Deposition processes and 5-axis machining as well as the key elements of the associated digital chain for producing parts such as stainless-steel hydraulic turbine blades. To achieve this goal, the project aims to address the following scientific and technological challenges: 1) optimize the topological decomposition of the part into entities and the hybridization between additive and subtractive manufacturing, 2) synthesize the manufacturing parameters to obtain a "near net shape" target geometry, 3) model the influence of disturbances during the execution of the WXAM trajectories, and 4) perform closed-loop control of the process trajectories. The expected results of the AWESOME project are: 1) the definition of additive entities and algorithms to define the trajectories of the associated manufacturing processes and a multicriteria analysis method to optimize their scheduling with the machining operations to form the manufacturing sequence; 2) a model to determine the parameters of the WXAM processes from the geometries of the "near net shape" entities to be produced; 3) an experimental set-up for in-process acquisition of the beads’ shape and in-situ acquisition of the additive manufacturing entities ; 4) Numerical treatments of measurements in a feedback loop towards the design of the process and the control of the process in real time ; 4) a defect library of parts produced in 5-axis WXAM caused by the thermal and kinematic behaviors, associated with geometric models including defects. The innovative character of the AWESOME project lies in the will to tightly integrate additive and subtractive processes and to cover not only the definition of the processes but also their execution on the production means. This is made possible by the integration of multidisciplinary skills (CAD/CAM, thermal modeling, multi-body kinematics, in-situ / in-process geometric measurements, control command) to obtain in fine an integrated hybrid manufacturing process as well as its digital chain through an industrial demonstrator. The success of the project is therefore based on the complementarity between the academic and industrial partners, on the experience of the actors in joint collaborative projects and on the recruitment of three Ph.D. students in the project. The first one will be in charge of developing "near net shape" additive manufacturing entities and optimizing the hybrid manufacturing sequence; the second one will be in charge of transforming an entity into bead geometry and process parameters and guaranteeing their correct formation during the process; the third Ph.D. student will develop an in-situ measurement method by image correlation and the numerical processing in order to continuously readjust the deposition or machining trajectories during the manufacturing process.