NDLI: Statistical process monitoring approach for high-density point clouds

Content Provider	Springer Nature Link
Author	Wells, Lee J. Megahed, Fadel M. Niziolek, Cory B. Camelio, Jaime A. Woodall, William H.
Copyright Year	2012
Abstract	Statistical process control (SPC) methods have been extensively applied to monitor the quality performance of manufacturing processes to quickly detect and correct out-of-control conditions. As sensor and measurement technologies advance, there is a continual need to adapt and refine SPC methods to effectively and efficiently use these new data-sets. One of the most state-of-the-art dimensional measurement technologies currently being implemented in industry is the 3D laser scanner, which rapidly provides millions of data points to represent an entire manufactured part’s surface. Consequently, this data has a great potential to detect unexpected faults, i.e., faults that are not captured by measuring a small number of predefined dimensions. However, in order for this potential to be realized, SPC methods capable of handling these large data-sets need to be developed. This paper presents an approach to performing SPC using point clouds obtained through a 3D laser scanner. The proposed approach transforms high-dimensional point clouds into linear profiles through the use of Q–Q plots, which can be monitored by well established profile monitoring techniques. In this paper point clouds are simulated to determine the performance of the proposed approach under varying fault scenarios. In addition, experimental studies were performed to determine the effectiveness of the proposed approach using actual point cloud data. The results of these experiments show that the proposed approach can significantly improve the monitoring capabilities for manufacturing parts that are characterized by complex surface geometries.
Starting Page	1267
Ending Page	1279
Page Count	13
File Format	PDF
ISSN	09565515
Journal	Journal of Intelligent Manufacturing
Volume Number	24
Issue Number	6
e-ISSN	15728145
Language	English
Publisher	Springer US
Publisher Date	2012-06-16
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Coordinate measuring machines High-density data Noncontact scanning systems Phase II control charts Profile monitoring Statistical quality control Production/Logistics/Supply Chain Manufacturing, Machines, Tools Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Artificial Intelligence Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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