Detection and monitoring of defects on three-dimensional curved surfaces based on high-density point cloud data

Abstract The surface quality of three-dimensional (3-D) curved surfaces is one of the most important factors that can directly influence the performance of the final product. This paper presents a systematic approach for detection and monitoring of defects on 3-D curved surfaces based on high-density point cloud data. Firstly, an algorithm to remove outliers and a boundary recognition algorithm are proposed to divide the entire 3-D curved surface including millions of measured points into multiple sub-regions. Secondly, two new evaluation indexes based on wavelet packet entropy and normal vector are explored to represent the features of the multiple sub-regions to determine whether the sub-regions are out-of-limit (OOL) of specifications. Thirdly, three quality parameters representing quality characteristics of a curved surface are presented and their values are calculated based on the clusters of OOL sub-regions. Finally, three individual control charts are presented to monitor the three quality parameters. As long as any quality parameter is out of the control range, the manufacturing process of the curved surface is determined to be out-of-control (OOC). The results of a case study show that the proposed approach can effectively identify the OOC manufacturing process and detect defects on 3-D curved surfaces.

[1]  Jan-Michael Frahm,et al.  USAC: A Universal Framework for Random Sample Consensus , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Hui Wang,et al.  High-Definition Metrology Enabled Surface Variation Control by Cutting Load Balancing , 2016 .

[3]  Massimo Pacella,et al.  From Profile to Surface Monitoring: SPC for Cylindrical Surfaces Via Gaussian Processes , 2014 .

[4]  Douglas C. Montgomery,et al.  Using Control Charts to Monitor Process and Product Quality Profiles , 2004 .

[5]  Fadel M. Megahed,et al.  An image-based multivariate generalized likelihood ratio control chart for detecting and diagnosing multiple faults in manufactured products , 2016 .

[6]  Joe H. Sullivan,et al.  Detection of Multiple Change Points from Clustering Individual Observations , 2002 .

[7]  Shichang Du,et al.  A Systematic Approach for Online Minimizing Volume Difference of Multiple Chambers in Machining Processes Based on High-Definition Metrology , 2017 .

[8]  Jun Lv,et al.  On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines , 2012 .

[9]  Chenhui Shao,et al.  Progressive measurement and monitoring for multi-resolution data in surface manufacturing considering spatial and cross correlations , 2015 .

[10]  Shichang Du,et al.  Co-Kriging Method for Form Error Estimation Incorporating Condition Variable Measurements , 2016 .

[11]  Wei Jiang,et al.  A spatial variable selection method for monitoring product surface , 2016 .

[12]  Lifeng Xi,et al.  Tool Wear Monitoring of Wiper Inserts in Multi-Insert Face Milling Using Three-Dimensional Surface Form Indicators , 2015 .

[13]  Helmut Pottmann,et al.  Fat surfaces: a trivariate approach to triangle-based interpolation on surfaces , 1992, Comput. Aided Geom. Des..

[14]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[15]  Fugee Tsung,et al.  Using Profile Monitoring Techniques for a Data‐rich Environment with Huge Sample Size , 2005 .

[16]  Shichang Du,et al.  Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes , 2013 .

[17]  Lifeng Xi,et al.  3D surface form error evaluation using high definition metrology , 2014 .

[18]  Shichang Du,et al.  A shearlet-based separation method of 3D engineering surface using high definition metrology , 2015 .

[19]  Meng Wang,et al.  A diffusion filter for discontinuous surface measured by high definition metrology , 2015 .

[20]  Joshua A. Tarbutton,et al.  Application of the continuous wavelet transform in periodic error compensation , 2016 .

[21]  Jaime A. Camelio,et al.  Automated Surface Defect Detection Using High-Density Data , 2016, Journal of Manufacturing Science and Engineering.

[22]  Jaime A. Camelio,et al.  Statistical process monitoring approach for high-density point clouds , 2012, Journal of Intelligent Manufacturing.

[23]  Shichang Du,et al.  A fast and adaptive bi-dimensional empirical mode decomposition approach for filtering of workpiece surfaces using high definition metrology , 2018 .

[24]  Ryutaro Tanaka,et al.  Effect of different features to drill-wear prediction with back propagation neural network , 2014 .

[25]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.

[26]  Hui Wang,et al.  An Adaptive Support Vector Machine-Based Workpiece Surface Classification System Using High-Definition Metrology , 2015, IEEE Transactions on Instrumentation and Measurement.

[27]  Qinghu Chen,et al.  Surface roughness evaluation by using wavelets analysis , 1999 .

[28]  Lifeng Xi,et al.  A selective multiclass support vector machine ensemble classifier for engineering surface classification using high definition metrology , 2015 .