# Impact of a Robot Manipulation on the Dimensional Measurements in an SPC-Based Robot Cell
## 1 Introduction
[22] showed that using a CMM in comparator mode eliminates the kinematic part of the measurement uncertainty and reduces the part associated with environmental impacts. Systematic effects associated with the measurement system apply both to the measurements of the test artefact and the master artefact, and a substantial proportion of the systematic effect associated with the two sets of measurements cancel out. In industry, there have already been demonstrations of an automated measurement system with a reduced influence of the operator. However, the current literature does not present automated solutions that would involve a combination of a robot and contact measurements where the robot would replace the operator to serve the measuring machine (including object grasping and manipulation, insertion, and removal). This work investigates another previously unstudied, but today important, dimension to show how the measurement outcome varies during automation. More specifically, we are interested in the influence of the robot's manipulation (that occurs before a dimensional measurement) on the object's final dimensional measurements. More in detail, measurement data scattering. We identified some important and problematic robot manipulation actions that affected the scattering of dimensional measurement data. We divided actions into grasping, robot trajectory, object insertion, and measurements (Figure 1). Furthermore, object insertion can be divided into translation and rotation. Meanwhile, robot trajectory can be divided into linear and joint motions. Figure 1 also shows that multiple grasps, as well as several trajectory sections, appear in regular manipulation to measurement cycle. The measurement itself is ultimately the last phase, which means that all previous grasping or manipulation actions might influence 6D pose repeatability of the measured part. The geometry of the measured part is not ideal. Inherited are many dimensional tolerances that mirror better measurement system analysis (MSA) parameters [23; 24] if repeatability in all previous steps is high. In the case of poor repeatability, we, indeed, measure different points with different tolerances on the object. Advanced use-case examples of robots are the core pillars of Industry 4.0 and quality assurance, with both gaining importance in a product-adaptive or modularly composed environment. In this way, such a question is essential for state-of-the-art Industry 4.0. The primary role of quality assurance is elementary, i.e., always measure in the same way. This is not the case if the robot's handling of the object is not constant, something that would introduce more variability into the measurement procedure. For instance, the manipulator mechanics of an old robot, with many working hours or loose grasping, add more and more positional and rotational uncertainty to an otherwise \"constant\" robot's individual actions and set of movements. Finally, this leads to positional and orientational (i.e., relating to the pose) uncertainty of the measured object [25; 26]. Pose uncertainty influences the measuring equipment's capability, as well as the repeatability and reproducibility, which are all well-defined for today's acknowledged quality-assurance producers (see Section 2). The influence of robot manipulation on the capability of measuring systems is studied for nine different robot scenarios. The robot provides complex and straightforward object handling, while the Equator (parallel robot) contact measuring device is used for the measurement. The initial assumption that a higher degree of robot manipulation directly affects the capability factor \(C_{p}\) is verified and confirmed. It turns out that the sampling strategy and measurement method (scanning and touch-triggering mode) have a much smaller impact on the capability factor \(C_{p}\) than the robot grasping and the manipulation itself. Normal distribution of grasping, manipulation and measurement data is confirmed. Using appropriate sequences, the MSA statistical parameters can be improved.