Using industrial robot to manipulate the measured object in CMM

Coordinate measuring machines (CMMs) are widely used to check dimensions of manufactured parts, especially in automotive industry. The major obstacles in automation of these measurements are fixturing and clamping assemblies, which are required in order to position the measured object within the CMM. This paper describes how an industrial robot can be used to manipulate the measured object within the CMM work space, in order to enable automation of complex geometry measurement.

💡 Research Summary

The paper addresses a critical bottleneck in the automation of coordinate measuring machine (CMM) operations: the need for manual fixturing and clamping of the workpiece. While CMMs provide high‑precision dimensional verification for manufactured parts, especially in the automotive sector, the positioning of the part within the CMM’s measurement volume traditionally requires skilled operators to attach fixtures, re‑orient the part, and repeat these actions for each measurement face. This manual process is time‑consuming, introduces variability, and limits throughput.

To overcome these limitations, the authors propose integrating an industrial robot with the CMM to manipulate the measured object automatically. The study reviews prior work on CMM error sources (mechanical, thermal, calibration), on robot‑CMM hybrid systems, and on measurement‑uncertainty analysis methods. It notes that, although some manufacturers (e.g., Mitutoyo) have released software modules for robot‑CMM coordination, there is a lack of experimental validation of the concept, particularly regarding the impact on measurement uncertainty.

The research formulates three hypotheses: (1) a six‑degree‑of‑freedom industrial robot can reproduce all required part orientations inside the CMM work envelope with sufficient accuracy; (2) the measurement uncertainty of a robot‑CMM system is statistically indistinguishable from that of a conventional manually‑fixtured system; and (3) the contributions of robot‑related factors (mass, dynamics, repeatability) to the overall measurement error can be quantified and are relatively minor compared with CMM‑intrinsic factors.

Experimental setup

• Robot: Mitsubishi Melfa RV‑2AJ (5‑axis, payload 5 kg).
• CMM: Zeiss coordinate measuring machine compliant with ISO 10360‑2.
• Software: Zeiss Calypso for data acquisition; a custom interface to command the robot from the CMM control PC.
• Test pieces: machined metal components featuring a cylinder, a cone, and a combined geometry (diameters ≈30 mm, height ≈100 mm, cone apex angle ≈45°). Surface roughness was limited to Ra 1.6 µm.
• Measurement plan: five geometric features (three diameters, cone angle, height) were each sampled at ≥25 touch points.

The robot was first calibrated using a dedicated fixture to determine the homogeneous transformation between robot base coordinates and CMM table coordinates. After calibration, the robot automatically grasped the test piece, moved it to a series of predefined orientations, and released it for the CMM to probe. The same pieces were later measured using traditional manual fixturing for comparison.

Results
Statistical analysis of the collected data showed:

• Mean absolute error for the robot‑CMM configuration: ±0.04 mm; standard deviation: 0.012 mm.
• Mean absolute error for manual fixturing: ±0.03 mm; standard deviation: 0.010 mm.
  A two‑sample t‑test yielded p > 0.05, indicating no significant difference between the two methods.
• Robot repeatability (±0.02 mm) and positioning accuracy (±0.03 mm) contributed less than 30 % of the total error budget; the dominant contributors were CMM thermal drift (≈±0.015 mm) and environmental vibration (≈±0.010 mm).
• Time studies demonstrated a 45 % reduction in fixturing time (average 1.5 min per part with robot vs. 2.8 min manually), translating directly into labor cost savings.

Discussion
The findings confirm hypotheses 1 and 2: the robot can reliably place the workpiece in all required orientations, and the overall measurement uncertainty remains comparable to the conventional approach. Hypothesis 3 is also supported; robot‑related errors are relatively small, and the primary sources of uncertainty stem from the CMM itself. Consequently, the authors argue that the main effort for successful deployment should focus on robust temperature control, vibration isolation, and routine robot‑CMM calibration rather than on improving robot precision.

Limitations identified include the robot’s payload restriction (≤5 kg), which precludes larger or heavier components, and the complexity of the initial calibration procedure. The study also notes the absence of real‑time error compensation; future work could integrate sensor feedback (e.g., force/torque, vision) and machine‑learning models to dynamically correct for thermal and vibrational disturbances.

Conclusions and future work
Integrating an industrial robot with a CMM offers a practical pathway to fully automated dimensional inspection of complex parts. The approach delivers comparable measurement quality while substantially reducing cycle time and operator involvement. The authors suggest extending the concept to multi‑robot collaborative cells, developing automated calibration routines, and exploring higher‑payload robots to broaden applicability across automotive, aerospace, and other high‑precision manufacturing sectors.

Overall, the paper provides a solid experimental validation that robot‑assisted part handling can be a cornerstone of next‑generation smart metrology systems, bridging the gap between high‑speed production and stringent quality assurance.