A quick deviation zone fitting in coordinate metrology of NURBS surfaces using principle component analysis. (October 2016)

• Record Type: Journal Article
• Authors: Gohari, H.; Barari, A.
• Is Part Of: Measurement. Volume 92(2016)
• Page Start: 352
• Page End: 364
• Publication Date: 2016-10
• Subjects: Dynamic Principle Component Alignment (DPCA) -- Total least square -- Deviation zone -- Principal Component Analysis (PCA) -- Coordinate Measurement Machine (CMM) -- Geometric deviation -- Non-Uniform Rational B-Spline (NURBS) -- Localization -- Fitting; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2016.05.050
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A quick and reliable fitting algorithm called Dynamic Principle Components Alignment (DPCA) is presented. DPCA a suitable candidate for Coordinate Measurement Machines (CMM) with large sets of measurement points. The algorithm efficiently eliminates the necessity of using commonly used optimization methods for the fitting process. DPCA has the ability to be implemented in real-time and direct control application. Abstract: Evaluation of the tolerance zone using discrete measured points plays a critical role in today's manufacturing, metrology, and many industrial applications. The deviation zone is typically evaluated using a fitting method that locates an ideal desired geometry corresponding to a set of measured points while a function of the Euclidean distances of the measured points to the ideal surface becomes minimum. This paper presents a quick and reliable algorithm called Dynamic Principle Component Alignment (DPCA) for fitting complex surfaces to the coordinate metrology measured points using the information that is dynamically generated by Principal Component Analysis (PCA) of the measurement data and the corresponding fitted geometry. The developed algorithm efficiently eliminates the necessity for applying commonly used optimization methods for the fitting (localization) process, which decreases the computational cost and uncertainty of the evaluation process. Moreover, DPCA is very reliable and practical in coordinate metrology with large data sets in processes such as laser scanning and other optical methods. The results show that the proposed methodology more accurately finds fitting parameters in comparison with the other commonly used methods while the computational cost is considerably reduced.