To improve the mechanical properties of additively manufactured parts, specific heat treatments must be developed. Annealing of electron beam-melted Ti-6Al-4V was performed at sub-transus temperatures and followed by water quenching. Such treatments generate an α + α dual-phase microstructure. Microstructural and mechanical characterizations revealed that the heat-treated specimens show a broad range of tensile properties, depending on the fraction of martensite. The specimens treated between 850°C and 920°C exhibit an increase in strength and ductility, which is related to a remarkable hardening behavior. Work-hardening is attributed to kinematic hardening arising from the mechanical contrast between the α and α phases. IMPACT STATEMENTInnovative heat treatments leading to α + α dual-phase microstructures are developed on Ti-6Al-4V parts produced by additive manufacturing. They lead to unprecedented work-hardening capabilities for this alloy.

Robotic machining is a promising manufacturing technology combining the agility of an industrial robot with the potential of a net shape process. Nowadays, robotic machining is springing up everywhere. A large number of companies attempt the conversion of their process using this technology, but without great success. Indeed, the material removal using a robot is very different from the conventional milling. Even though it is cheaper for a larger working envelope, the understanding of robotic process must be widened in order to overcome technological hurdles. Among them, the lack of stiffness at joints and the inherent low frequency structure are mainly accountable for the quality depletion of the machined workpiece. The simulation of an industrial process can be an interesting approach to better perceive the on-going phenomena during the machining phase. For this reason, a multibody modelling of a robot coupled with the simulation of milling was implemented. The robot was modelled as an anthropomorphic arm comprising 4 degrees of freedom and including the joint stiffness. Results were finally confronted with experimental data in terms of cutting forces, vibrations and roughness for which a good accordance could be observed.

Robotic machining is a fast-growing technology in the field of mechanical manufacturing. Indeed, it is generally accepted that for the same working space, a fully equipped robotic machining cell can cost 30 to 50 % less than a conventional machine tool. However, inaccuracies resulting either from vibrations or deflections occur while the robot is subjected to cutting forces, inherent to its flexible structure. As an order of magnitude, the stiffness at the tool-tip is about 1N/µm for industrial robots against more than 50N/µm for CNC machine tools. The flexibility source has been investigated and appears to be caused by the robot articulations in a proportion of 80% while the remaining flexibility issues from the structural elasticity. In order to improve the accuracy of robotic machining operations, several approaches have been carried out such as the study of stable cutting conditions and the online/offline compensation of the tool trajectory.Two aspects of the operation must be modeled, on the one hand the model of the cutting machine, being an industrial robot in robotic machining, and on the other hand, the machining model including the resulting geometry of the workpiece. A coupled model is then proposed with the multi-body model of the robot subjected to machining forces. The multi-body model includes the flexibility induced by the structure and the articulations. In order to compensate the deviations, a solution is proposed where the trajectory is discretized in nodes with a compensation taking the system dynamics into account by successive simulations of the operation. The algorithm involves two steps, firstly it aims to detect critical locations of the path and add or reposition nodes to reduce the deviation and secondly an optimization layer modifies nodes positions and velocities for a finer reduction. The method is deployed for three systems of increasing complexity for a face milling operation, showing a machining error reduction.