A Fast and Robust Nonparametric Monitoring Scheme for Free-Form Surface Scanning Data

The advance of new sensing technologies, such as the 3-D laser scanning, creates a data-rich environment for quality control in modern industries. The free-form surfaces of complex manufactured parts can be quickly scanned, producing thousands of data points. To monitor these large-scale surface scanning data, three major challenges have to be solved simultaneously: 1) simple parametric models are no longer sufficient to describe free-form surfaces; 2) the massive data points need fast computations; and 3) the presence of outliers calls for robust analytics. To fulfill this task, this paper proposes a novel monitoring scheme where the control chart is designed based on a new robust bilateral kernel smoothing method. A fast approximation algorithm is also developed for efficient online monitoring. This fast and robust nonparametric control chart shows significant superiority for surface monitoring in our numerical simulations. Finally, a real case study demonstrates the effectiveness of our proposed scheme in monitoring the stability of a 3-D printing process. Note to Practitioners—Recently, the widespread use of 3-D laser scanners in industries enables engineers to digitize any free-form surface into thousands of data points, which provides an unprecedented opportunity for quality control of complex manufactured products. This paper proposes a monitoring scheme which caters to arbitrary geometric features on a surface. Practitioners need no prior and specific surface mathematical models. A robust estimate of the surface can be quickly derived from the noisy scanning data by an approximation algorithm. Then, a statistic calculating the deviation of this surface from the target one is plotted in a control chart to monitor the stability of the manufacturing process. In practice, this monitoring scheme can be integrated with the 3-D laser scanner as a new production module for automatic quality control and decision making.