Point cloud 3D parent surface reconstruction and weld seam feature extraction for robotic grinding path planning

High-performance components with complex geometries make it difficult to determine the position and orientation of grinding tool. In this work, a fast and accurate robotic grinding path planning method is proposed for automatic removal of irregular weldments on a free form surface. The surface of workpiece is digitalized by 3D profile scanner and represented by point cloud data. Statistic filter, weighted least square regression and differences of normal vectors are used for point cloud pre-processing and segmentation. All segments are then modelled by B-spline surfaces to obtain the parent surface. A new superposition method is proposed to create a computer-aided design (CAD) model of the actual workpiece by adding the weld seam to the parent surface. The CAD model is then imported into an off-line simulation system to generate and execute grinding path. With the superposition method, the heights and widths of weld seam are extracted by analysing the difference between point cloud data and the reconstructed parent surface in order to determine the feed rate and size of grinding tool. Experimental results show that the proposed superposition method has the maximum absolute percentage error 5.3% and 41% saving in computation time in comparison with the conventional reverse engineering method.

[1]  Babak Taati,et al.  Difference of Normals as a Multi-scale Operator in Unorganized Point Clouds , 2012, 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission.

[2]  Yiming Rong,et al.  Framework of grinding process modeling and simulation based on microscopic interaction analysis , 2011 .

[3]  Brian Boswell,et al.  A review identifying the effectiveness of minimum quantity lubrication (MQL) during conventional machining , 2017 .

[4]  Heping Chen,et al.  Automatic Programming for Robotic Grinding Using Real Time 3D Measurement , 2017, 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER).

[5]  Ming Sun,et al.  Optimum Path Planning of Robotic Free Abrasive Polishing Process , 2008, ICIRA.

[6]  Jiawen Chen,et al.  Real-time edge-aware image processing with the bilateral grid , 2007, SIGGRAPH 2007.

[7]  A. E. Diniz,et al.  Evaluation of grinding process using simultaneously MQL technique and cleaning jet on grinding wheel surface , 2019, Journal of Materials Processing Technology.

[8]  Virgínia Infante,et al.  Study of the fatigue behavior in welded joints of stainless steels treated by weld toe grinding and subjected to salt water corrosion , 2008 .

[9]  Yanbin Du,et al.  An integrated approach of reverse engineering aided remanufacturing process for worn components , 2017 .

[10]  Jindrich Liska,et al.  Hand-Eye Calibration of a Laser Profile Scanner in Robotic Welding , 2018, 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).

[11]  H. Ding,et al.  Experimental investigation and modeling of material removal characteristics in robotic belt grinding considering the effects of cut-in and cut-off , 2020 .

[12]  Alexandre Boulch,et al.  Railway Detection: From Filtering to Segmentation Networks , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[13]  Bruno Siciliano,et al.  Fast Statistical Outlier Removal Based Method for Large 3D Point Clouds of Outdoor Environments , 2018, SyRoCo.

[14]  Jon Atli Benediktsson,et al.  Advanced directional mathematical morphology for the detection of the road network in very high resolution remote sensing images , 2010, Pattern Recognit. Lett..

[15]  Mark J. Clayton,et al.  Virtual construction of architecture using 3D CAD and simulation , 2002 .

[16]  E. Bianchi,et al.  Effect of CBN grain friability in hardened steel plunge grinding , 2019, The International Journal of Advanced Manufacturing Technology.

[17]  Eduardo Carlos Bianchi,et al.  Minimum quantity of lubrication (MQL) as an eco-friendly alternative to the cutting fluids in advanced ceramics grinding , 2019, The International Journal of Advanced Manufacturing Technology.

[18]  Lei Gao,et al.  A review of algorithms for filtering the 3D point cloud , 2017, Signal Process. Image Commun..

[19]  Hans Hagen,et al.  Surface Reconstruction from Unorganized 3D Point Clouds , 2012 .

[20]  P. Gong,et al.  Filtering airborne laser scanning data with morphological methods , 2007 .

[21]  Wei Huang,et al.  SLGC: A fast point-in-area algorithm based on scan-line algorithm and grid compression , 2016, 2016 11th International Conference on Computer Science & Education (ICCSE).

[22]  Eduardo Carlos Bianchi,et al.  Application of minimum quantity lubrication with addition of water in the grinding of alumina , 2018 .

[23]  Said Jahanmir,et al.  Finite element simulation of straight plunge grinding for advanced ceramics , 2003 .

[24]  Jiu-hua Xu,et al.  Chip formation of nickel-based superalloy in high speed grinding with single diamond grit , 2012 .

[25]  E. Zelniker,et al.  Detection and vectorization of roads from lidar data , 2007 .

[26]  Huabin Chen,et al.  Acoustic signal-based tool condition monitoring in belt grinding of nickel-based superalloys using RF classifier and MLR algorithm , 2018 .

[27]  Rongxing Li,et al.  SHORELINE EXTRACTION FROM THE INTEGRATION OF LIDAR POINT CLOUD DATA AND AERIAL ORTHOPHOTOS USING MEAN SHIFT SEGMENTATION , 2009 .

[28]  H. J. Mello,et al.  Contribution to cylindrical grinding of interrupted surfaces of hardened steel with medium grit wheel , 2018 .

[29]  Berend Denkena,et al.  Engine blade regeneration: a literature review on common technologies in terms of machining , 2015 .

[30]  H. J. Mello,et al.  Application of a wheel cleaning system during grinding of alumina with minimum quantity lubrication , 2019, The International Journal of Advanced Manufacturing Technology.

[31]  Huabin Chen,et al.  A novel material removal prediction method based on acoustic sensing and ensemble XGBoost learning algorithm for robotic belt grinding of Inconel 718 , 2019, The International Journal of Advanced Manufacturing Technology.

[32]  Benkai Li,et al.  Experimental evaluation of the lubrication performance of mixtures of castor oil with other vegetable oils in MQL grinding of nickel-based alloy , 2017 .

[33]  XiaoQi Chen,et al.  Robotic grinding and polishing for turbine-vane overhaul , 2002 .

[34]  X. Q. Chen,et al.  SMART Robotic System for 3D Profile Turbine Vane Airfoil Repair , 2003 .

[35]  Ekkard Brinksmeier,et al.  Ultra-precision grinding , 2010 .

[36]  Kazem Kazerounian,et al.  Accurate robotic belt grinding of workpieces with complex geometries using relative calibration techniques , 2009 .

[37]  Dahu Zhu,et al.  An improved chip-thickness model for surface roughness prediction in robotic belt grinding considering the elastic state at contact wheel-workpiece interface , 2019, The International Journal of Advanced Manufacturing Technology.

[38]  Nabil Gindy,et al.  A repair and overhaul methodology for aeroengine components , 2010 .

[39]  Junwei Wang,et al.  3D curvature grinding path planning based on point cloud data , 2016, 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA).

[40]  Wei Wang,et al.  A Path Planning Method for Robotic Belt Surface Grinding , 2011 .

[41]  Peter Wonka,et al.  Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road Footprints , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[42]  Yung C. Shin,et al.  Remanufacturing of turbine blades by laser direct deposition with its energy and environmental impact analysis , 2014 .