This work focuses on additive manufacturing by Directed Energy Deposition (DED) using a 6-axis robot. The objective is to generate an optimized trajectory in the joint space, taking into account axis redundancy for parts of revolution produced with a coaxial deposition system. To achieve this goal, a new layer-by-layer method coupled with a trajectory constrained optimization is presented. The optimization results are theoretically compared to a non-optimized trajectory and a point-by-point optimized trajectory. The layer-by-layer generation of optimized trajectories is validated experimentally on a 6-axis robot using a PLA extrusion system. Experimental results show that the layer-by-layer trajectory optimization strategy applied to parts of revolution provides better geometrical accuracy while improving the efficiency of the manufacturing device compared to non-optimized solutions.

这项工作的重点是使用6轴机器人通过定向能量沉积（DED）进行增材制造。目的是在关节空间中生成优化的轨迹，同时考虑到同轴沉积系统产生的旋转部分的轴冗余。为了实现这一目标，提出了一种新的逐层方法，结合了轨迹约束优化。理论上将优化结果与非优化轨迹和逐点优化轨迹进行比较。优化轨迹的逐层生成已在使用PLA挤出系统的6轴机器人上进行了实验验证。