This article introduces a new geometric vector modeling method of serial kinematic robot consistent with the identification process. This method is based on the definition of position and orientation of the robot joint invariants. For example, the invariant of the rotational joint is a straight-line (rotational joint axis). Thus, only independent geometrical parameters are introduced to model the joint axis position and orientation in space. Note that, the orientation is not constrained as in the Denavit-Hartenberg (DH) formalism. This article presents the methodology to define these geometrical parameters and the geometrical model. In this context, the identification method relies on "Circle Point Analysis". The points are measured with a laser tracker. Indeed, with a relevant processing of the measured points, we directly identify the invariants of joints. This method is applied to a SCARA robot geometric modeling. After an identification process, this methodology allows improving inverse kinematic error compared to the classical DH geometrical model with first and second-order defects. Moreover, the obtained residual error mean value is close to the accuracy of the measurement process.

The evolution of current manufacturing processes, such as additive manufacturing processes, enables to produce parts with increasingly complex internal and external geometries, to answer functional requirements. This requires an evolution of the measurement methods to analyze the complete part produced. In this context, the use of computed tomography (CT) is increasing. Considering the problem of surface quality control, and also considering the cost of such a measuring system, it becomes necessary to evaluate the capability of tomography techniques to characterize surface geometry despite an inadequate resolution. To this end, the study proposed in this paper aims at assessing the quality of surface roughness characterization by CT in comparison with classical optical measure means. Special attention is given to thresholding, which isnecessary to extract the surface from CT measurements, which are the basis to evaluate roughness parameters. An advanced analysis is also performed to bring out surface typologies that are more appropriate for CT measurements with poor resolution.