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Three-Dimensional Tolerance Analysis Modelling of Variation Propagation in Multi-stage Machining Processes for General Shape Workpieces

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Abstract

The quality control of general shape workpieces has become one of research hotspots because of the increasing diversity of products. The theory of stream of variation (SoV) for machining processes is a successful method in researching variation propagation rule. However, with the consideration of all key elements in manufacturing system, there is no unified and integrated model for workpieces in different kinds of shapes. In this paper, a new variation propagation model in multi-stage machining processes for general shape workpieces is established. It visually demonstrates the variation propagation chain and expands the universality of current SoV models. The connection of all key elements in manufacturing system is defined as an assembly chain, in which the variations are defined and propagated by modified three-dimensional tolerance analysis method. The equivalent conversion of the connection between workpiece and fixture realizes the modelling of general shape workpiece regardless of its machining method and locating scheme. Real experiments validate the effectiveness and accuracy of the new SoV model for different shape workpieces. This model has great potential to be applied toward multi-scale variation modelling, process control, and fault diagnosis for general shape workpieces.

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References

  1. Shi, J. (2006). Stream of variation modeling and analysis for multistage manufacturing processes. New York: CRC Press.

    Book  Google Scholar 

  2. Hu, S. J. (1997). Stream-of-variation theory for automotive body assembly. CIRP Annals—Manufacturing Technology,46(1), 1–6.

    Article  Google Scholar 

  3. Jin, J., & Shi, J. (1999). State space modeling of sheet metal assembly for dimensional control. Journal of Manufacturing Science and Engineering-Transactions of the Asme,121(4), 756–762.

    Article  Google Scholar 

  4. Mantripragada, R., & Whitney, D. E. (1999). Modeling and controlling variation propagation in mechanical assemblies using state transition models. IEEE Transactions on Robotics and Automation,15(1), 124–140.

    Article  Google Scholar 

  5. Ding, Y., Ceglarek, D., & Shi, J. (2002). Design evaluation of multi-station assembly processes by using state space approach. Journal of Mechanical Design,124(3), 408–418.

    Article  Google Scholar 

  6. Djurdjanovic, D., & Ni, J. (2003). Dimensional errors of fixtures, locating and measurement datum features in the stream of variation modeling in machining. Journal of Manufacturing Science and Engineering-Transactions of the Asme,125(4), 716–730.

    Article  Google Scholar 

  7. Huang, Q., & Shi, J. (2003). Part dimensional error and its propagation modeling in multi-operational machining processes. Journal of Manufacturing Science and Engineering-Transactions of the ASME,125(2), 255–262.

    Article  Google Scholar 

  8. Zhou, S., Huang, Q., & Shi, J. (2003). State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors. IEEE Transactions on Robotics and Automation,19(2), 296–309.

    Article  Google Scholar 

  9. Abellan-Nebot, J. V., Liu, J., Subirón, F. R., & Shi, J. (2012). State space modeling of variation propagation in multistation machining processes considering machining-induced variations. Journal of Manufacturing Science and Engineering-Transactions of the Asme,134(2), 021002.

    Article  Google Scholar 

  10. Luan, X., Zhang, S., & Li, G. (2018). Modified power prediction model based on infinitesimal cutting force during face milling process. International Journal of Precision Engineering and Manufacturing-Green Technology,5(1), 71–80.

    Article  Google Scholar 

  11. Zhang, H., Zhang, Q., Ren, Y., Shay, T., & Liu, G. (2018). Simulation and experiments on cutting forces and cutting temperature in high speed milling of 300 M steel under CMQL and dry conditions. International Journal of Precision Engineering and Manufacturing,19(8), 1245–1251.

    Article  Google Scholar 

  12. Liu, S., & Lin, M. (2019). Thermal-mechanical coupling analysis and experimental study on CNC machine tool feed mechanism. International Journal of Precision Engineering and Manufacturing,20(6), 993–1006.

    Article  Google Scholar 

  13. Loose, J. P., Zhou, S., & Ceglarek, D. (2007). Kinematic analysis of dimensional variation propagation for multistage machining processes with general fixture layouts. IEEE Transactions on Automation Science and Engineering,4(2), 141–152.

    Article  Google Scholar 

  14. Yang, F., Jin, S., & Li, Z. (2017). A modification of DMVs based state space model of variation propagation for multistage machining processes. Assembly Automation,37(4), 381–390.

    Article  Google Scholar 

  15. Qazani, M., Pedrammehr, S., & Nategh, M. (2018). An investigation on the motion error of machine tools’ hexapod table. International Journal of Precision Engineering and Manufacturing,19(4), 463–471.

    Article  Google Scholar 

  16. Liu, T., Li, Z., Jin, S., & Chen, W. (2018). A variation analysis method for linkage mechanism with consideration of joint clearance and deformation. International Journal of Precision Engineering and Manufacturing,19(10), 1495–1506.

    Article  Google Scholar 

  17. Du, S., Yao, X., & Huang, D. (2015). Engineering model-based Bayesian monitoring of ramp-up phase of multistage manufacturing process. International Journal of Production Research,53(15), 4594–4613.

    Article  Google Scholar 

  18. Du, S., Yao, X., Huang, D., & Wang, M. (2015). Three-dimensional variation propagation modeling for multistage turning process of rotary workpieces. Computers and Industrial Engineering,82, 41–53.

    Article  Google Scholar 

  19. Lee, K., Lee, J., & Yang, S. (2018). Optimal on-machine measurement of position-independent geometric errors for rotary axes in five-axis machines with a universal head. International Journal of Precision Engineering and Manufacturing,19(4), 545–551.

    Article  Google Scholar 

  20. Echerfaoui, Y., El Ouafi, A., & Chebak, A. (2018). Experimental investigation of dynamic errors in coordinate measuring machines for high speed measurement. International Journal of Precision Engineering and Manufacturing,19(8), 1115–1124.

    Article  Google Scholar 

  21. Ahn, H., Kang, H., Ghim, Y., & Yang, H. (2019). Touch probe tip compensation using a novel transformation algorithm for coordinate measurements of curved surfaces. International Journal of Precision Engineering and Manufacturing,20(2), 193–199.

    Article  Google Scholar 

  22. Wang, J., Wang, Q., & Li, H. (2019). The method of geometric error measurement of NC machine tool based on the principle of space vector’s direction measurement. International Journal of Precision Engineering and Manufacturing,20(4), 511–524.

    Article  Google Scholar 

  23. Loose, J. P., Zhou, Q., Zhou, S., & Ceglarek, D. (2010). Integrating GD&T into dimensional variation models for multistage machining processes. International Journal of Production Research,48(11), 3129–3149.

    Article  Google Scholar 

  24. Wang, K., Yin, Y., Du, S., Xi, L., & Xia, T. (2017). State space modeling of multi-scale variation propagation in machining process using matrix model. In 2017 IEEE international conference on industrial engineering and engineering management (pp. 770–774).

  25. Yang, F., Jin, S., & Li, Z. (2016). A comprehensive study of linear variation propagation modeling methods for multistage machining processes. International Journal of Advanced Manufacturing Technology,90(5–8), 2139–2151.

    Google Scholar 

  26. Shi, J., & Zhou, S. (2009). Quality control and improvement for multistage systems: A survey. IIE Transactions,41(9), 744–753.

    Article  Google Scholar 

  27. Zeng, W., & Rao, Y. (2019). Modeling of assembly deviation with considering the actual working conditions. International Journal of Precision Engineering and Manufacturing,20(5), 791–803.

    Article  Google Scholar 

  28. Chen, H., Jin, S., Li, Z., & Lai, X. (2014). A comprehensive study of three dimensional tolerance analysis methods. Computer-Aided Design,53(5), 1–13.

    Article  Google Scholar 

  29. Desrochers, A., Ghie, W., & Laperrière, L. (2003). Application of a unified Jacobian–Torsor model for tolerance analysis. Journal of Computing and Information Science in Engineering,3(1), 2–14.

    Article  Google Scholar 

  30. Chen, H., Jin, S., Li, Z., & Lai, X. (2015). A modified method of the unified Jacobian–Torsor model for tolerance analysis and allocation. International Journal of Precision Engineering and Manufacturing,16(8), 1789–1800.

    Article  Google Scholar 

  31. Wang, H., Huang, L., Yao, C., Kou, M., Wang, W., Huang, B., et al. (2015). Integrated analysis method of thin-walled turbine blade precise machining. International Journal of Precision Engineering and Manufacturing,16(5), 1011–1019.

    Article  Google Scholar 

  32. Zuo, X., Li, B., Yang, J., & Jiang, X. (2013). Application of the Jacobian–Torsor theory into error propagation analysis for machining processes. International Journal of Advanced Manufacturing Technology,69(5–8), 1557–1568.

    Article  Google Scholar 

  33. Kamali Nejad, M., Vignat, F., Desrochers, A., & Villeneuve, F. (2010). 3D Simulation of manufacturing defects for tolerance analysis. Journal of Computing and Information Science in Engineering,10(2), 1–13.

    Article  Google Scholar 

  34. Desrochers, A., & Clement, A. (1994). A dimensioning and tolerancing assistance model for CAD/CAM systems. International Journal of Advanced Manufacturing Technology,9(6), 352–361.

    Article  Google Scholar 

  35. Teissandier, D., Couetard, Y., & Gerard, A. (1999). A computer aided tolerancing model: Proportioned assembly clearance volume. Computer-Aided Design,31(13), 805–817.

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 51535007, 51775343). All experiments were performed at Shanghai Automotive Industry Corporation General Motors Wuling Company (SGMW) in Liuzhou, China, we are grateful to SGMW engineers for their experimental support.

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Correspondence to Shichang Du.

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Wang, K., Du, S. & Xi, L. Three-Dimensional Tolerance Analysis Modelling of Variation Propagation in Multi-stage Machining Processes for General Shape Workpieces. Int. J. Precis. Eng. Manuf. 21, 31–44 (2020). https://doi.org/10.1007/s12541-019-00202-0

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