Stream of Variation Testing in order to Fault Diagnosis of Multistage Manufacturing Processes

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Golpayegan University of Engineering, Isfahan, Iran.

2 Department of Industrial Engineering, Islamic Azad University, Sanandaj Branch, Kurdistan, Iran.

Abstract

One of the most popular problems in quality improvement of multistage manufacturing processes is the variation of quality characteristics. Stream of variation methodology models variation propagation of dimensional deviation that comes from key quality characteristics. This research describes new procedure for source of variation identification in variation propagation modeling. At the firs, discriminant function used to separate variations and then future deviations assign to model but detected source may not be real source. In order to find real source, stream of variation testing is used. The applicability and performance of source of variation identification investigate with an illustrative case study in connecting rod production line.

Keywords

Main Subjects


[1]     Apley, DW., Shi, J., (2001). “Factor-analysis method for diagnosing variability in multivariate manufacturing processes”, Technometrics, 43 (1): 84-95.
[2]     Huang, Q., Zhou, N., Shi, J., (2000). “Stream of variation modeling and diagnosis of multi-station machining processes”, Paper presented at the Proceedings of the ASME International Mechanical Engineering Congress & Exposition, Orlando, FL.
[3]     Huang, Q., Zhou, S., Shi, J. (2002). “Diagnosis of multi-operational machining processes through variation propagation analysis”, Robotics and Computer-Integrated Manufacturing, 18 (3-4): 233-239.
[4]     Huang, Q., Shi, J., (2004). “Stream of variation modeling and analysis of serial-parallel multistage manufacturing systems, Journal of Manufacturing Science and Engineering”, Transactions of the ASME 126 (3): 611-618.
[5]     Huang, W., Lin, J., Kong, Z., Ceglarek, D. (2007). “Stream-of-variation (SOVA) modeling - Part II: A generic 3D variation model for rigid body assembly in multistation assembly processes”, Journal of Manufacturing Science and Engineering, Transactions of the ASME 129 (4): 832-842.
[6]     Liu, J., (2010). “Variation reduction for multistage manufacturing processes: A comparison survey of statistical-process-control vs stream-of-variation methodologies”, Quality and Reliability Engineering International, 26(7): 661-645.
[7]     Abellan-Nebot, J.V., Liu, J., Subiron, F.R., Shi, J., (2012). “State Space Modeling of Variation Propagation in Multistation Machining Processes Considering Machining-Induced Variation”, ASME Transactions on Manufacturing Science and Engineering, 134(2): 200-212.
[8]     کوچک‌زاده، احمد، لسانی، سیدعلی، فاطمی قمی، سید محمدتقی، (1394). ارایه یک مدل ترکیبی برای شناسایی و تحلیل الگوهای معنی‌دار در نمودارهای کنترل فرآیند، فصلنامه پژوهش‌های مهندسی صنایع در سیستم‌های تولید، 3(6): 177-189.
[9]     Bazdar, A., Kazemzadeh, R., Niaki, S.T.A., (2015). “Variation source identification of multistage manufacturing processes through discriminant analysis and stream of variation methodology: a case study in automotive industry”, Journal of Engineering Research, 2(3): 1-14.
[10] Bazdar, A., Kazemzadeh, R., Niaki, S.T.A. (2016). “Fault diagnosis within multistage machining processes using linear discriminant analysis: A case study in automotive industry”, To appear in Quality Technology & Quantitative Management.
[11] Shi, J., Zhou, S., (2009). “Quality control and improvement for multistage systems: A survey”, IIE Transactions (Institute of Industrial Engineers), 41 (9): 744-753.
[12] Shi, J., (2007). “Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes”, CRC Press, Taylor & Francis Group.
[13] Sharma, S. (1996). Applied Multivariate Techniques, John Wiley & Sons, Inc.
[14] Rezaei, A., (2012). “Connecting Rod”, Nasir-Kyung, http://nasirkyoung.com.