Exponentially Weighted Moving Average Charts for the Quantification of Dimensional Measurement Variability Using 3D Laser Scanners




Stadick, James

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Industries are adopting 3D laser scanner technology to inspect parts that require more points than can be feasibly provided by Coordinate Measurement Machines (CMM). These laser scanners may provide millions of points representing the 3D surface of a part. The first part of this research work investigates Statistical Process Control (SPC) methods using Exponentially Weighted Moving Average (EWMA) control charts of point cloud data deviations. A designed full factorial experiment was performed in R calculating the out-of-control average run length (ARL) for several simulated 3D surfaces with defects to assess the effectiveness of these control charts. Results indicated that the methodology can detect defects with a maximum out-of-control mean ARL of approximately 15 for the smallest tested defect of 0.01 inches deep and 0.03 inches wide. However, defects that are smaller than 0.01 inches in depth are needed to address the needs of industries with tighter tolerances. Results also showed that the order in which defects appear in the manufacturing line may not be significant.

The second part of this research work proposes a method to quantify an uncertainty budget as there is no current standard for 3D laser scanners. A designed full factorial experiment was performed on several artifacts to collect the response data of a specified measurand. The designed experiment analysis provided a regression equation that was used in a Monte Carlo simulation to provide the uncertainty budget. The uncertainty budget for a curve block diameter was 4.0003 inches to 4.0147 inches with a nominal of 4 inches. The uncertainty budget for a cylinder diameter was 0.4992 inches to 0.5076 inches with a nominal of 0.5002 inches. The uncertainty budget for the angle of a block was 24.9540 degrees to 26.5203 degrees with a nominal of 25 degrees. The uncertainty coverage interval serves as an aid in assessing if 3D laser scanners can be utilized for part inspection and quality assurance activities in a reliable manner. Moreover, the empirical distributions for each measurands can be used in computational performance models.


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3D Laser Scanner, Big Data, EWMA Control Chart, Point Cloud, Statistical Process Control, Uncertainty



Mechanical Engineering