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Manufacturing variation models in multi-station machining systems

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Abstract

In product design and quality improvement fields, the development of reliable 3D machining variation models for multi-station machining processes is a key issue to estimate the resulting geometrical and dimensional quality of manufactured parts, generate robust process plans, eliminate downstream manufacturing problems, and reduce ramp-up times. In the literature, two main 3D machining variation models have been studied: the stream of variation model, oriented to product quality improvement (fault diagnosis, process planning evaluation and selection, etc.), and the model of the manufactured part, oriented to product and manufacturing design activities (manufacturing and product tolerance analysis and synthesis). This paper reviews the fundamentals of each model and describes step by step how to derive them using a simple case study. The paper analyzes both models and compares their main characteristics and applications. A discussion about the drawbacks and limitations of each model and some potential research lines in this field are also presented.

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References

  1. Ceglarek D, Huang W, Zhou S, Ding Y, Kumar R, Zhou Y (2004) Time-based competition in multistage manufacturing: stream-of-variation analysis (SOVA) methodology—review. Int J Flex Manuf Syst 16:11–44

    Article  MATH  Google Scholar 

  2. Shi J (2007) Stream of variation modeling and analysis for multistage manufacturing systems. CRC, Boca Raton

    Google Scholar 

  3. Ogata K (2001) Modern control engineering, 4th edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  4. Zhou S, Huang Q, Shi J (2003) State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors. IEEE Trans Robot Autom 19:296–309

    Article  Google Scholar 

  5. Huang Q, Shi J, Yuan J (2003) Part dimensional error and its propagation modeling in multi-operational machining processes. J Manuf Sci Eng 125:255–262

    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. J Manuf Sci Eng Trans ASME 125:716–730

    Article  Google Scholar 

  7. Loose JP, Zhou S, Ceglarek D (2007) Kinematic analysis of dimensional variation propagation for multistage machining processes with general fixture layouts. IEEE Trans Autom Sci Eng 4:141–152

    Article  Google Scholar 

  8. Abellan-Nebot JV, Liu J, Romero F (2012) State space modeling of variation propagation in multi-station machining processes considering machining-induced variations. J Manuf Sci Eng. doi:10.1115/1.4005790

    Google Scholar 

  9. Zhang M, Djurdjanovic D, Ni J (2007) Diagnosibility and sensitivity analysis for multi-station machining processes. Int J Mach Tools Manuf 47:646–657

    Article  Google Scholar 

  10. Liu J, Shi J, Hu SJ (2009) Quality-assured setup planning based on the stream-of-variation model for multi-stage machining processes. IIE Trans 41:323–334(12)

    Article  Google Scholar 

  11. Abellan-Nebot JV, Liu J, Subiron FR (2011) Design of multi-station manufacturing processes by integrating the stream-of-variation model and shop-floor data. J Manuf Syst 30:70–82

    Article  Google Scholar 

  12. Wang H, Huang Q, Katz R (2005) Multi-operational machining processes modeling for sequential root cause identification and measurement reduction. J Manuf Sci Eng 127:512–521

    Article  Google Scholar 

  13. Ding Y, Shi J, Ceglarek D (2002) Diagnosability analysis of multi-station manufacturing processes. J Dyn Syst Meas Control 124:1–13

    Article  Google Scholar 

  14. Zhou S, Chen Y, Ding Y, Shi J (2003) Diagnosability study of multistage manufacturing processes based on linear mixed-effects models. Technometrics 45:312–325

    Article  MathSciNet  Google Scholar 

  15. Zhou SY, Chen Y, Shi J (2004) Statistical estimation and testing for variation root-cause identification of multistage manufacturing processes. IEEE Trans Autom Sci Eng 1:73–83

    Article  Google Scholar 

  16. Li ZG, Zhou SY, Ding Y (2007) Pattern matching for variation-source identification in manufacturing processes in the presence of unstructured noise. IIE Trans 39:251–263

    Article  Google Scholar 

  17. Djurdjanovic D, Ni J (2003) Bayesian approach to measurement scheme analysis in multistation machining systems. Proc Inst Mech Eng B J Eng Manuf 217:1117–1130

    Article  Google Scholar 

  18. Ding Y, Kim P, Ceglarek D, Jin J (2003) Optimal sensor distribution for variation diagnosis in multistation assembly processes. IEEE Trans Robot Autom 19:543–556

    Article  Google Scholar 

  19. Djurdjanovic D, Ni J (2004) Measurement scheme synthesis in multi-station machining systems. J Manuf Sci Eng 126:178–188

    Article  Google Scholar 

  20. Djurdjanovic D, Ni J (2006) Stream-of-variation (SoV)-based measurement scheme analysis in multistation machining systems. IEEE Trans Autom Sci Eng 3:407–422

    Article  Google Scholar 

  21. Izquierdo L, Shi J, Hu S, Wampler C (2007) Feedforward control of multistage assembly processes using programmable tooling. NAMRI/SME Trans 35:295–302

    Google Scholar 

  22. Djurdjanovic D, Zhu J (2005) Stream of variation based error compensation strategy in multi-stage manufacturing processes. ASME Conference Proceedings

  23. Djurdjanovic D, Ni J (2007) Online stochastic control of dimensional quality in multistation manufacturing systems. Proc Inst Mech Eng B J Eng Manuf 221:865–880

    Article  Google Scholar 

  24. Jiao Y, Djurdjanovic D (2008) Allocation of flexible tooling for optimal stochastic multistation manufacturing process quality control. ASME Conference Proceedings 2008, pp 161–169

  25. Jiao Y, Djurdjanovic D (2010) Joint allocation of measurement points and controllable tooling machines in multistage manufacturing processes. IIE Trans 42:703–720

    Article  Google Scholar 

  26. Abellan-Nebot JV, Liu J, Subiron FR (2012) Quality prediction and compensation in multi-station machining processes using sensor-based fixtures. Robot Comput-Integr Manuf 28:208–219

    Article  Google Scholar 

  27. Chen Y, Ding Y, Jin J, Ceglarek D (2006) Integration of process-oriented tolerancing and maintenance planning in design of multistation manufacturing processes. IEEE Trans Autom Sci Eng 3:440–453

    Article  Google Scholar 

  28. Ding Y, Jin JH, Ceglarek D, Shi J (2005) Process-oriented tolerancing for multi-station assembly systems. IIE Trans 37:493–508

    Article  Google Scholar 

  29. Shirinzadeh B (2002) Flexible fixturing for workpiece positioning and constraining. Assem Autom 22:112–120

    Article  Google Scholar 

  30. Ding Y, Ceglarek D, Jin JH, Shi JJ (2000) Process-oriented tolerance synthesis for multistage manufacturing systems. In: Proceedings of the international mechanical engineering congress and exposition, Orlando, FL, pp 15–22

  31. Ballot E, Bourdet P (1997) A computational method for the consequences of geometric errors in mechanisms. In: Proceedings of the 4th CIRP seminar on computer aided tolerancing, Tokyo, Japan, pp 137–148

  32. Chase KW, Gao J, Magleby SP (1995) General 2-D tolerance analysis of mechanical assemblies with small kinematic adjustments. J Des Manuf 5:263–274

    Article  Google Scholar 

  33. Chase KW, Gao J, Magleby SP, Sorensen CD (1996) Including geometric feature variations in tolerance analysis of mechanical assemblies. IIE Trans 28:795–807

    Google Scholar 

  34. Davidson JK, Mujezinovic A, Shah JJ (2002) A new mathematical model for geometric tolerances as applied to round faces. J Mech Des 124:609–622

    Article  Google Scholar 

  35. Villeneuve F, Legoff O, Landon Y (2001) Tolerancing for manufacturing: a three-dimensional model. Int J Prod Res 39:1625–1648

    Article  MATH  Google Scholar 

  36. Desrochers A (1999) Modeling three-dimensional tolerance zones using screw parameters. In: CD-ROM proceedings of ASME 25th design automation conference, DAC-8587, Las-Vegas

  37. Desrochers A, Ghie W, Laperriere L (2003) Application of a unified Jacobian–torsor model for tolerance analysis. J Comput Inf Sci Eng 3:2–14

    Article  Google Scholar 

  38. Villeneuve F, Vignat F (2007) Simulation of the manufacturing process in a tolerancing point of view: generic resolution of the positioning problem. In: Models for computer aided tolerancing in design and manufacturing, pp 179–189

  39. Vignat F, Villeneuve F (2003) 3D transfer of tolerances using a SDT approach: application to turning process. J Comput Inf Sci Eng 3:45–53

    Article  Google Scholar 

  40. Nejad MK, Vignat F, Desrochers A, Villeneuve F (2010) 3D simulation of manufacturing defects for tolerance analysis. J Comput Inf Sci Eng 10:021001

    Article  Google Scholar 

  41. Ghie W, Laperriére L, Desrochers A (2007) Re-design of mechanical assemblies using the unified Jacobian–torsor model for tolerance analysis. In: Models for computer aided tolerancing in design and manufacturing, pp 95–104

  42. Nejad MK, Vignat F, Villeneuve F (2009) Simulation of the geometrical defects of manufacturing. Int J Adv Manuf Technol 45:631–648

    Article  Google Scholar 

  43. Villeneuve F, Vignat F (2005) Manufacturing process simulation for tolerance analysis and synthesis. In: Advances in integrated design and manufacturing in mechanical engineering, pp 189–200

  44. Nejad MK (2010) Propositions de résolution numérique des problèmes d’analyse de tolérance en fabrication: approche 3D. Dissertation, Université Joseph Fourier

  45. Ayadi B, Anselmetti B, Bouaziz Z, Zghal A (2008) Three-dimensional modelling of manufacturing tolerancing using the ascendant approach. Int J Adv Manuf Technol 39:279–290

    Article  Google Scholar 

  46. Louati J, Ayadi B, Bouaziz Z, Haddar M (2006) Three-dimensional modelling of geometric defaults to optimize a manufactured part setting. Int J Adv Manuf Technol 29:342–348

    Article  Google Scholar 

  47. Tichadou S, Legoff O, Hascoet JY (2005) 3D geometrical manufacturing simulation. In: Advances in integrated design and manufacturing in mechanical engineering, pp 201–214

  48. Kamali Nejad M, Vignat F, Villeneuve F (2012) Tolerance analysis in machining using the model of manufactured part (MMP)—comparison and evaluation of three different approaches. Int J Comput Integr Manuf 25:136–149

    Article  Google Scholar 

  49. Anselmetti B, Louati H (2005) Generation of manufacturing tolerancing with ISO standards. Int J Mach Tools Manuf 45:1124–1131

    Article  Google Scholar 

  50. Vignat F, Villeneuve F (2007) A numerical approach for 3D manufacturing tolerances synthesis. In: 10th CIRP conference on computer aided tolerancing, Erlangen, Germany

  51. Tichadou S, Kamalinejad M, Vignat F, Legoff O (2007) 3-d manufacturing dispersions: two experimental applications. In: 10th CIRP international conference on computer aided tolerancing, Erlangen, Germany

  52. Sergent A, Bui MH, Favreliere H, Duret D, Samper S, Villeneuve F (2010) Identification of machining defects by small displacement torsor and form parameterization method. In: IDMME international conference, Bordeaux, France

  53. Loose J, Zhou Q, Zhou S, Ceglarek D (2010) Integrating GD&T into dimensional variation models for multistage machining processes. Int J Prod Res 48:3129–3149

    Article  MATH  Google Scholar 

  54. Kong Z, Huang W, Oztekin A (2009) Variation propagation analysis for multistation assembly process with consideration of GD&T factors. J Manuf Sci Eng 131:051010

    Article  Google Scholar 

  55. Bourdet P, Ballot E (1995) Geometrical behaviour laws for computer aided tolerancing. In: 4th CIRP seminars on computer aided tolerancing, Tokyo, pp 143–154

  56. Liu J, Shi J, Hu SJ (2008) Engineering-driven factor analysis for variation source identification in multistage manufacturing processes. J Manuf Sci Eng 130:041009

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Industrial Systems Engineering and Design, Universitat Jaume I, Castellón de la Plana, 12071, Spain

    José Vicente Abellán-Nebot, Fernando Romero Subirón & Julio Serrano Mira

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  1. José Vicente Abellán-Nebot
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  2. Fernando Romero Subirón
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  3. Julio Serrano Mira
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Correspondence to José Vicente Abellán-Nebot.

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Abellán-Nebot, J.V., Romero Subirón, F. & Serrano Mira, J. Manufacturing variation models in multi-station machining systems. Int J Adv Manuf Technol 64, 63–83 (2013). https://doi.org/10.1007/s00170-012-4016-4

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  • DOI: https://doi.org/10.1007/s00170-012-4016-4

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