Skip to main content
Log in

Generating interference matrices for automatic assembly sequence planning

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Interference matrix (IM) is an important assembly relation model to automate the assembly sequence planning (ASP). For a complex product with numerous parts, the creation of IMs is time consuming and error prone. Thus, a method is proposed to accurately and quickly generate IMs from a CAD assembly model. First, an improved IM model considering interference quantity is used to discriminate fake interference caused by CAD model errors. Second, the algorithm to automatically generate IMs is presented, in which a collision detection (CD) method is designed to quickly determine interference relationships between parts. This CD method involves three novel methods: extended cruciformly overlapped axis-aligned bounding boxes (COAABBs), improved pseudo face (PF) and hybrid facet projection intersection test. And a system called AutoAssem is utilized to implement the proposed method. Finally, the case study of a gearbox demonstrates that this method can extract IMs from the inaccurate CAD models for accurate ASP. Moreover, case studies of complex products like the milling machine verify that this method is much more efficient than the current methods and greatly shortens the data preprocessing time of ASP.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Boothroyd G, Dewhurst P, Knight WA (2010) Product design for manufacture and assembly, third edn. CRC, Boca Raton

    Google Scholar 

  2. Zha XF, Samuel YEL, Fok SC (1998) Integrated knowledge-based assembly sequence planning. Int J Adv Manuf Technol 14(1):50–64

    Article  Google Scholar 

  3. Rashid MFF, Hutabarat W, Tiwari A (2012) A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches. Int J Adv Manuf Technol 59(1–4):335–349

    Article  Google Scholar 

  4. Jiménez P (2013) Survey on assembly sequencing: a combinatorial and geometrical perspective. J Intell Manuf 24(2):235–250

    Article  Google Scholar 

  5. De Fazio TL, Whitney DE (1987) Simplified generation of all mechanical assembly sequences. IEEE J Robot Autom 3(6):640–658

    Article  Google Scholar 

  6. Wilson RH (1995) Minimizing user queries in interactive assembly planning. IEEE Trans Robot Autom 11(2):308–312

    Article  Google Scholar 

  7. Su Q (2007) Computer aided geometric feasible assembly sequence planning and optimizing. Int J Adv Manuf Technol 33(1–2):48–57

    Article  Google Scholar 

  8. Miltrouchev P, Wang CG, LX L, Li GQ (2015) Selective disassembly sequence generation based on lowest level disassembly graph method. Int J Adv Manuf Technol 80(1):141–159

    Article  Google Scholar 

  9. Wang Y, Tian D (2016) A weighted assembly precedence graph for assembly sequence planning. Int J Adv Manuf Technol 83(1):99–115

    Article  MathSciNet  Google Scholar 

  10. Dini G, Santochi M (1992) Automated sequencing and subassembly detection in assembly planning. Annals of the CIRP 41(1):1–4

    Article  Google Scholar 

  11. Kuo TC (2000) Disassembly sequence and cost analysis for electromechanical products. Robot Comput Integr Manuf 16(1):43–54

    Article  Google Scholar 

  12. Huang YM, Huang CT (2002) Disassembly matrix for disassembly processes of products. Int J Prod Res 40(2):225–273

    Article  MATH  Google Scholar 

  13. Liu XH, Liu YH, BH X (2013) A converse method-based approach for assembly sequence planning with assembly tool. Int J Adv Manuf Technol 69(5–8):1359–1371

    Article  Google Scholar 

  14. Gandhi S, Masehian E (2015) Assembly sequence planning of rigid and flexible parts. J Manuf Syst 36:128–146

    Article  Google Scholar 

  15. Ou LM, Xu X (2012) Relationship matrix based automatic assembly sequence generation from a CAD model. Comput -Aided Design 45(7):1053–1067

    Article  Google Scholar 

  16. Moez T, Riadh BH, Nizar A (2015) An interoperability CAD assembly sequence plan approach. Int J Adv Manuf Technol 79(9–12):1465–1476

    Google Scholar 

  17. Dong TY, Tong RF, Zhang L, Dong JX (2007) A knowledge-based approach to assembly sequence planning. Int J Adv Manuf Technol 32(11–12):1232–1244

    Article  Google Scholar 

  18. Hsu YY, Tai PH, Wang MW, Chen WC (2011) A knowledge-based engineering system for assembly sequence planning. Int J Adv Manuf Technol 55(5–8):763–782

    Article  Google Scholar 

  19. Smith GC, Smith S (2003) Automated initial population generation for genetic assembly planning. Int J Comput Integ Manuf 16(3):219–228

    Article  Google Scholar 

  20. Wang Y, Liu JH (2010) Chaotic particle swarm optimization for assembly sequence planning. Robot Com-Int Manuf 26(2):212–222

    Article  Google Scholar 

  21. Tseng YJ, FY Y, Huang FY (2011) A green assembly sequence planning model with a closed-loop assembly and disassembly sequence planning using a particle swarm optimization method. Int J Adv Manuf Technol 57(9):1183–1197

    Article  Google Scholar 

  22. LD X, Wang C, Bi ZM, JP Y (2012) AutoAssem: an automated assembly planning system for complex products. IEEE Trans Ind Inform 8(3):669–677

    Article  Google Scholar 

  23. Zhang HY, Liu HJ, Li LY (2014) Research on a kind of assembly sequence planning based on immune algorithm and particle swarm optimization algorithm. Int J Adv Manuf Technol 71(5–8):795–808

    Article  Google Scholar 

  24. Wang JF, XH W, Fan XL (2015) A two-stage ant colony optimization approach based on a directed graph for process planning. Int J Adv Manuf Technol 80(5):839–850

    Article  Google Scholar 

  25. Pan CX, Smith SS, Smith GC (2005) Determining interference between parts in CAD STEP files for automatic assembly planning. J Comput Inf Sci Eng 5(1):56–62

    Article  Google Scholar 

  26. JP Y, Wang C (2013) Method for discriminating geometric feasibility in assembly planning based on extended and turning interference matrix. Int J Adv Manuf Technol 67(5–8):1867–1882

    Google Scholar 

  27. Perrard C, Bonjour E (2013) Unification of the a priori inconsistencies checking among assembly constraints in assembly sequence planning. Int J Adv Manuf Technol 69(1–4):669–685

    Article  Google Scholar 

  28. JP Y, LD X, Bi ZM, Wang C (2014) Extended interference matrices for exploded view of assembly planning. IEEE Trans Autom Sci Eng 11(1):279–286

    Article  Google Scholar 

  29. JP Y, Wang C (2013) A max-min ant colony system for assembly sequence planning. Int J Adv Manuf Technol 67(5–8):2819–2835

    Google Scholar 

  30. Llies HT (2009) Continuous collision and interference detection for 3D geometric models. J Comput Info Sci Eng 9(2):1–7

    Google Scholar 

  31. Tching L, Dumont G, Perret J (2010) Interactive simulation of CAD models assemblies using virtual constraint guidance. Int J Interact Des Manuf 4(2):95–102

    Article  Google Scholar 

  32. Shi JC, Liu JH, Ning RX, Hou WW (2013) A collisions evaluation method in virtual environment for collaborative assembly. J Netw Comput Appl 36(6):1523–1530

    Article  Google Scholar 

  33. Gao SM, Zhao W, Lin HW, Yang FQ, Chen X (2010) Feature suppression based CAD mesh model simplification. Comput -Aided Design 42(12):1178–1188

    Article  Google Scholar 

  34. Jiménez JJ, Segura RJ (2008) Collision detection between complex polyhedra. Comput Graph 32(4):402–411

    Article  Google Scholar 

  35. Weller R (2013) New geometric data structures for collision detection and haptics. Springer, Zurich

    Book  Google Scholar 

  36. Peng GL, Hou X, Wu C, Jin TG, Zhang XT (2010) Fast collision detection approach to facilitate interactive modular fixture assembly design in a virtual environment. Int J Adv Manuf Technol 46(1–4):315–328

    Article  Google Scholar 

  37. Bergen G (1997) Efficient collision detection of complex deformable models using AABB trees. J Graph Tools 2(4):1–13

    Article  MATH  Google Scholar 

  38. Gottcshalk S, Lin MC, Manocha D (1996) OBB-tree: a hierarchical structure for rapid interference detection. Proc ACM SIGGRAPH Conf Computer Graph, New Orleans, pp. 171–180

    Google Scholar 

  39. Bradshaw G, O’Sullivan C (2004) Adaptative medial-axis approximation for sphere-tree construction. ACM Trans Graph 23(1):1–26

    Article  Google Scholar 

  40. Jiménez P, Thomas F, Torras C (2001) 3D collision: a survey. Comput Graph 25(2):269–285

    Article  Google Scholar 

  41. Feng YT, Li CF, Owen DRJ (2006) SMB:collision detection based on temporal coherence. Comput Methods Appl Mech Eng 195(19–22):2252–2269

    MATH  Google Scholar 

  42. Kieffer J, Litvin FL (1991) Swept volume determination and interference detection for moving 3-D solids. J Mech Des 113(4):456–463

    Article  Google Scholar 

  43. Sulaiman HA, Othman MA, Saat MSM, Darsono AMB, Bade A, Abdullah MH (2014) Vector-based technique for distance computation in narrow phase collision detection. 1st International Symposium on Technology Management and Emerging Technologies (ISTMET). Bandung, Indonesia, pp 506–510

  44. Baciu G, Wong WSK (2003) Image-based techniques in a hybrid collision detector. IEEE Trans Vis Comput Graph 9(2):254–271

    Article  Google Scholar 

  45. Zhang WL, RX Q, MR X, Luo XC (2016) Method for automatic generation of assembly interference matrix of complex products. Journal of Mechanical Engineering 52(1):139–148 in Chinese

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wenlei Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, W., Ma, M., Li, H. et al. Generating interference matrices for automatic assembly sequence planning. Int J Adv Manuf Technol 90, 1187–1201 (2017). https://doi.org/10.1007/s00170-016-9410-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-016-9410-x

Keywords

Navigation