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Improved quality prediction model for multistage machining process based on geometric constraint equation

  • Production Scheduling and Quality Control
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Abstract

Product variation reduction is critical to improve process efficiency and product quality, especially for multistage machining process (MMP). However, due to the variation accumulation and propagation, it becomes quite difficult to predict and reduce product variation for MMP. While the method of statistical process control can be used to control product quality, it is used mainly to monitor the process change rather than to analyze the cause of product variation. In this paper, based on a differential description of the contact kinematics of locators and part surfaces, and the geometric constraints equation defined by the locating scheme, an improved analytical variation propagation model for MMP is presented. In which the influence of both locator position and machining error on part quality is considered while, in traditional model, it usually focuses on datum error and fixture error. Coordinate transformation theory is used to reflect the generation and transmission laws of error in the establishment of the model. The concept of deviation matrix is heavily applied to establish an explicit map** between the geometric deviation of part and the process error sources. In each machining stage, the part deviation is formulized as three separated components corresponding to three different kinds of error sources, which can be further applied to fault identification and design optimization for complicated machining process. An example part for MMP is given out to validate the effectiveness of the methodology. The experiment results show that the model prediction and the actual measurement match well. This paper provides a method to predict part deviation under the influence of fixture error, datum error and machining error, and it enriches the way of quality prediction for MMP.

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References

  1. ZHOU Shiyu, CHEN Yong, SHI Jianju. Statistical estimation and testing for variation root-cause identification of multistage manufacturing processes[J]. Automation Science and Engineering, IEEE Transactions on, 2004, 1(1): 73–83.

    Article  Google Scholar 

  2. WANG Hui, KATZ R, HUANG Qiang. Multi-operational machining processes modeling for sequential root cause identification and measurement reduction[J]. Journal of manufacturing science and engineering, 2005, 127(3): 512–521.

    Article  Google Scholar 

  3. CAMELIO J, HUS Jack, ZHONG Wei**. Diagnosis of multiple fixture faults in machining processes using designated component analysis[J]. Journal of manufacturing systems, 2004, 23(4): 309–315.

    Article  Google Scholar 

  4. RONG Y, HU Wei. Locating error analysis and tolerance assignment for computer-aided fixture design[J]. International Journal of Production Research, 2001, 39(15): 3529–3545.

    Article  Google Scholar 

  5. ABELLAN-NEBOT J V, LIU Jian, SUBIRON F R. Process-oriented tolerancing using the extended stream of variation model[J]. Computers in Industry, 2013, 64(5): 485–498.

    Article  Google Scholar 

  6. JIAO Yibo, DJURDJANOVIC D. Compensability of errors in product quality in multistage manufacturing processes[J]. Journal of Manufacturing Systems, 2011, 30(4): 204–213.

    Article  Google Scholar 

  7. XIANG Liming, TSUNG F. Statistical monitoring of multi-stage processes based on engineering models[J]. IIE Transactions, 2008, 40(10): 957–970.

    Article  Google Scholar 

  8. LIU Jian. Variation reduction for multistage manufacturing processes: a comparison survey of statistical process control vs stream of variation methodologies[J]. Quality and Reliability Engineering International, 2010, 26(7): 645–661.

    Article  Google Scholar 

  9. WAN **n**, XIONG Caihua, ZHAO Can, et al. A unified framework of error evaluation and adjustment in machining[J]. International Journal of Machine Tools and Manufacture, 2008, 48(11): 1198–1210.

    Article  Google Scholar 

  10. ZHANG Min, DJURDJANOVIC D, JUN Ni Diagnosibility and sensitivity analysis for multi-station machining processes[J]. International Journal of Machine Tools and Manufacture, 2007, 47(3): 646–657.

    Article  Google Scholar 

  11. LIU Qinyan, DING Yu, CHEN Yong. Optimal coordinate sensor placements for estimating mean and variance components of variation sources[J]. IIE Transactions, 2005, 37(9): 877–889.

    Article  Google Scholar 

  12. MANDROLI S S, SHRIVASTAVA A K, DING Yu. A survey of inspection strategy and sensor distribution studies in discrete-part manufacturing processes[J]. IIE Transactions, 2006, 38(4): 309–328.

    Article  Google Scholar 

  13. HU S Jack, KOREN Yoram. Stream-of-variation theory for automotive body assembly[J]. CIRP Annals-Manufacturing Technology, 1997, 46(1): 1–6.

    Article  Google Scholar 

  14. LAWLESS J F, MACKAY R J, ROBINSONJ A. Analysis of variation transmission in manufacturing processes-part I[J]. Journal of Quality Technology, 1999, 31(2):131–142.

    Google Scholar 

  15. JIN Jionghua, SHI Jianjun. State space modeling of sheet metal assembly for dimensional control[J]. Journal of Manufacturing Science and Engineering, 1999, 121(4): 756–762.

    Article  Google Scholar 

  16. HUANG Qiang, SHI Jianjun, YUAN **gxia. Part dimensional error and its propagation modeling in multi-operational machining processes[J]. Journal of manufacturing science and engineering, 2003, 125(2): 255–262.

    Article  Google Scholar 

  17. HUANG Qiang ZHOU Nairong, SHI Jianjun. Stream of variation modeling and diagnosis of multi-station machining processes[J]. Ann Arbor, 2000, 1001: 48109–2117.

    Google Scholar 

  18. ZHOU Shiyu, HUANG Qiang, SHI Jianjun. State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors[J]. Robotics and Automation, IEEE Transactions on, 2003, 19(2): 296–309.

    Article  Google Scholar 

  19. DJURDJANOVIC D, NI Jun. Linear state space modeling of dimensional machining errors[J]. Transactions-North American Manufacturing research institution of SME, 2001: 541–548.

    Google Scholar 

  20. HUANG Qiang, SHI Jianjun. Variation transmission analysis and diagnosis of multi-operational machining processes[J]. IIE Transactions, 2004, 36(9): 807–815.

    Article  MathSciNet  Google Scholar 

  21. HUANG Qiang, ZHOU Shiyu, SHI Jianjun. Diagnosis of multi-operational machining processes through variation propagation analysis[J]. Robotics and Computer-Integrated Manufacturing, 2002, 18(3): 233–239.

    Article  Google Scholar 

  22. LOOSEJ P, ZHOU Shiyu, CEGLAREK D. Kinematic analysis of dimensional variation propagation for multistage machining processes with general fixture layouts[J]. Automation Science and Engineering, IEEE Transactions on, 2007, 4(2): 141–152.

    Article  Google Scholar 

  23. DJURDJANOVIC D, NI Jun. Dimensional errors of fixtures, locating and measurement datum features in the stream of variation modeling in machining[J]. Journal of Manufacturing Science and Engineering, 2003, 125(4): 716–730.

    Article  Google Scholar 

  24. ABELLAN-NEBOTJ V, LIU Jian, SUBIRONF R. Design of multi-station manufacturing processes by integrating the stream-of-variation model and shop-floor data[J]. Journal of Manufacturing Systems, 2011, 30(2): 70–82.

    Article  Google Scholar 

  25. ABELLAN-NEBOTJ V, LIU Jian, SUBIRONF R, et al. Limitations of the current state space modelling approach in multistage machining processes due to operation variations[C]//AIP Conference Proceedings, 2009, 1181(1): 231.

    Google Scholar 

  26. ABELLAN-NEBOTJ V, LIU Jian, SUBIRONF R, et al. State space modeling of variation propagation in multistage machining processes considering operation-induced variation[J]. Journal of Manufacturing Science and Engineering: Transactions of the ASME, under revision, 2012, 134(2):1–13.

    Google Scholar 

  27. ZHANG Fa**, LIU Jian, TANG Shuiyuan, et al. Locating error considering dimensional errors modeling for multistation manufacturing system[J]. Chinese Journal of Mechanical Engineering, 2010, 23(6): 765–743.

    Article  Google Scholar 

  28. QIN Guohua, ZHANG Weihong, WAN Min. A machining dimension based approach to locating scheme design[J]. Journal of Manufacturing Science and Engineering, 2008, 130(5): 051010.

    Article  Google Scholar 

  29. XIONG Caihua, RONG Yimin, TANG Yong, et al. Fixturing model and analysis[J]. International journal of computer applications in technology, 2007, 28(1): 34–45.

    Article  Google Scholar 

  30. RONG Yiming, HU Wei. Locating error analysis and tolerance assignment for computer-aided fixture design[J]. International Journal of Production Research, 2001, 39(150): 3529–3545.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tian** University, Tian**, 300072, China

    Limin Zhu & Gaiyun He

  2. School of Science, Tian** University, Tian**, 300072, China

    Limin Zhu & Zhanjie Song

Authors
  1. Limin Zhu
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  2. Gaiyun He
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  3. Zhanjie Song
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Corresponding author

Correspondence to Gaiyun He.

Additional information

Supported by National Natural Science Foundation of China (Grant Nos. 51205286, 51275348)

Biographical notes

ZHU Limin, born in 1988, is currently a graduate student, majoring in computational mathematics at School of Science, Tian** University, China. She is now participating in projects at Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tian** University, China.

HE Gaiyun, born in 1965, is currently a professor at Tian** University, China. She received her PhD degree from Tian** University, China, in 2006. Her research interests include modern manufacturing quality control, evaluation methods of geometric errors and CAD/CAM/CAI integration technology.

SONG Zhanjie, received his PhD degree in probability theory and mathematical statistics, from School of Mathematical Science, Nankai University, China, in 2006. He is currently a Professor at School of Science and a Fellow at Liuhui Center for Applied Mathematics, Tian** University, China. His current research interests are in approximation of deterministic signals, reconstruction of random signals and statistical analysis of random processes.

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Zhu, L., He, G. & Song, Z. Improved quality prediction model for multistage machining process based on geometric constraint equation. Chin. J. Mech. Eng. 29, 430–438 (2016). https://doi.org/10.3901/CJME.2016.0106.003

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  • DOI: https://doi.org/10.3901/CJME.2016.0106.003

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