Browse > Article
http://dx.doi.org/10.7232/IEIF.2011.24.1.058

Calculating a Sigma Level for Quality Measurement of 3D CAD Models from Their Error Occurrence Characteristics  

You, Hyo-Sun (Department of Industrial Engineering, Ajou University)
Yang, Jeong-Sam (Division of Industrial and Information Systems Engineering, Ajou University)
Park, Jae-Il (Division of Industrial and Information Systems Engineering, Ajou University)
Publication Information
IE interfaces / v.24, no.1, 2011 , pp. 58-70 More about this Journal
Abstract
As more individuals and organizations participate in the complex design process of manufacturing industry, collaborative product development and management of the global supply chain have become more popular. Although the product quality concerns once focused on the manufacturing process, they are now directed at earlier stages of the design cycle where the engineering product is created as a 3D CAD model. In this paper, we describe the current state of product data quality activities in the manufacturing industry and the yardstick to measure 3D CAD data quality. Moreover we introduce a quality assurance method through the result of statistical analysis of 3D CAD models and suggest a six sigma level of CAD data quality by analyzing 76 samples provided from three Korean automotive companies.
Keywords
sigma level; CAD model quality; quality assurance; statistical analysis;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Finn, G. A. (1999), Six Sigma in the Engineering Design Process, Technical Report, Prescient Technologies Inc., MA, USA.
2 Harry, M. J. (1998), A Breakthrough Strategy for Profitability, Quality Progress, 31(5), 60-64.
3 Valenta, B., Brajlih, T., Drstvensek, I., and Balic, J. (2006), Evaluation of Shape Complexity based on STL Data, Journal of Achievements in Materials and Manufacturing Engineering, 17(1-2), 293-296.
4 Yang, J., Han, S., Park, S., and Jang, J. (2005), Investigation of Product Data Quality in the Korean Automotive Industry, Transaction of CAD/CAM Engineers, 10(4), 274-283.
5 Lubell, J., Mani, M., Subrahmanian, E., and Rachuri, S. (2008), Long Term Sustainment Workshop Report, NIST, USA.
6 Yang, J., Han, S., Kang, H., and Kim, J. (2006), Product Data Quality Assurance for E-manufacturing in the Automotive Industry, International Journal of Computer Integrated Manufacturing, 19(2), 136-147.   DOI   ScienceOn
7 Porter, M. (2001), You only compete in two dimensions, Harvard Business Review March.
8 SASIG (2005), Product Data Quality Guidelines for the Global Automotive Industry, ISO TC184/SC4 N1944, 2(1).
9 Shanks, G. and Corbitt, B. (1999), Understanding Data Quality : Social and Cultural Aspects, Proc. 10th Australasian Conference on Information Systems, 785-797.
10 ISO 10303-59 (2008), Product Data Representation and Exchange-Part 59 : Integrated Generic Resource-Quality of Product Shape Data.
11 Phelps, T. (1999), Extending Quality Concepts to Product Data, AIAG Actionline, 19(7), 38-42.
12 Brunnermeier, S. and Martin, S. (1999), Interoperability Cost Analysis of the US Automotive Supply Chain, National Institute of Standards and Technology, Planning Report #99-1, Gaithersburg, USA, www.nist.gov/director/progofc/ report99-1.pdf