Skip to main content
Log in

A customizable process planning approach for rotational parts based on multi-level machining features and ontology

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

Abstract

Turning is the one of the most commonly available and least expensive machining operations. Confronted with the increased number of part variants and the requirement for fast system responsiveness in dynamic environments, traditional methods for building a computer-aided process planning (CAPP) system for turning are infeasible due to the fixed feature library and hard-coded heuristic rules. In this paper, a customizable approach for automatic process planning of rotational parts is proposed to boost the productivity of enterprises by realizing multi-level machining feature recognition and knowledge-based machining activity/resource selection. First, from the decomposed cells of the turning area, basic turning features are successively recognized based on a novel cell machinability analysis. The high-level custom features are define based on the directed acyclic graph and recognized from the basic feature precedence graph using the subgraph matching algorithm. To facilitate the knowledge-based process planning, a knowledge base is then established utilizing ontology, in which the taxonomies, properties, and causal relationships among the core concepts, namely, machining feature, machining operation, cutting tool, and machine tool, are formally defined. Finally, a five-step procedure is proposed to automatically infer manufacturing activity/resource for multi-level features through rule-based reasoning. The effectiveness and extensibility of the proposed approach are validated through two case studies on complex rotational parts.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

Similar content being viewed by others

References

  1. Deja M, Siemiatkowski MS (2018) Machining process sequencing and machine assignment in generative feature-based CAPP for mill-turn parts. J Manuf Syst 48:49–62

    Article  Google Scholar 

  2. Yusof Y, Latif K (2014) Survey on computer-aided process planning. Int J Adv Manuf Technol 75(1-4):77–89

    Article  Google Scholar 

  3. Ma H, Zhou X, Liu W, Li J, Niu Q, Kong C (2018) A feature-based approach towards integration and automation of CAD/CAPP/CAM for EDM electrodes. Int J Adv Manuf Technol 98(9-12):2943–2965

    Article  Google Scholar 

  4. Li R-KW (1988) A part-feature recognition system for rotational parts. Int J Prod Res 26(9):1451–1475

    Article  Google Scholar 

  5. Wu W, Huang Z, Liu Q, Liu L (2018) A combinatorial optimisation approach for recognising interacting machining features in mill turn parts. Int J Prod Res 56(11):3757–3780

    Article  Google Scholar 

  6. Al-wswasi M, Ivanov A (2019) A novel and smart interactive feature recognition system for rotational parts using a STEP file. Int J Adv Manuf Technol 104(1-4):261–284

    Article  Google Scholar 

  7. Liu L, Huang Z, Liu W, WenboWu (2018) Extracting the turning volume and features for a mill/turn part with multiple extreme faces. Int J Adv Manuf Technol 94 (1-4):257-280

  8. Huang Z, Yip-Hoi D (2002) High-level feature recognition using feature relationship graphs. Comput Aided Des 34(8):561–582

    Article  MATH  Google Scholar 

  9. Ming Z, Zeng C, Wang G, Hao J, Yan Y (2018) Ontology-based module selection in the design of reconfigurable machine tools. J Intell Manuf 31:301–317. https://doi.org/10.1007/s10845-018-1446-3

    Article  Google Scholar 

  10. Verma AK, Rajotia S (2010) A review of machining feature recognition methodologies. Int J Comput Integr Manuf 23(4):353–368

    Article  Google Scholar 

  11. Babic B, Nesic N, Miljkovic Z (2008) A review of automated feature recognition with rule-based pattern recognition. Comput Ind 59(4):321–337

    Article  Google Scholar 

  12. Joshi S, Chang TC (1988) Graph-based heuristics for recognition of machined features from a 3D solid model. Comput Aided Des 20(2):58–66

    Article  MATH  Google Scholar 

  13. Gao S, Shah JJ (1998) Automatic recognition of interacting machining features based on minimal condition subgraph. Comput Aided Des 30(9):727–739

    Article  MATH  Google Scholar 

  14. Madurai SS, Lin L (1992) Rule-based automatic part feature extraction and recognition from CAD data. Comput Ind Eng 22(1):49–62

    Article  Google Scholar 

  15. Wang Q, Yu X (2014) Ontology based automatic feature recognition framework. Comput Ind 65(7):1041–1052

    Article  Google Scholar 

  16. Yip-Hoi D, Dutta D, Huang Z (2003) A customizable machining feature extraction methodology for turned components. J Manuf Syst 22(2):82–98

    Article  Google Scholar 

  17. Li S, Shah JJ (2007) Recognition of user-defined turning features for mill/turn parts. J Comput Inf Sci Eng 7(3):225–235

    Article  Google Scholar 

  18. Xia Q, Etienne A, J-y D, Siadat A (2018) Reconfigurable machining process planning for part variety in new manufacturing paradigms: Definitions, models and framework. Comput Ind Eng 115:206–219

    Article  Google Scholar 

  19. Xu X, Wang L, Newman ST (2011) Computer-aided process planning – a critical review of recent developments and future trends. Int J Comput Integr Manuf 24(1):1–31

    Article  Google Scholar 

  20. Leo Kumar SP (2018) Knowledge-based expert system in manufacturing planning: state-of-the-art review. Int J Prod Res 57(15-16):4766–4790

    Article  Google Scholar 

  21. Oral A, Cakir MC (2004) Automated cutting tool selection and cutting tool sequence optimisation for rotational parts. Robot Comput Integr Manuf 20(2):127–141

    Article  Google Scholar 

  22. Law HW, Tam HY, Chan AHS, Hui IK (2001) Object-oriented knowledge-based computer-aided process planning system for bare circuit boards manufacturing. Comput Ind 45(2):137–153

    Article  Google Scholar 

  23. Helgoson M, Kalhori V (2012) A conceptual model for knowledge integration in process planning. Procedia CIRP 3:573–578

    Article  Google Scholar 

  24. Feng SC, Song EY (2003) A manufacturing process information model for design and process planning integration. J Manuf Syst 22(1):1–15

    Article  MathSciNet  Google Scholar 

  25. Panetto H, Dassisti M, Tursi A (2012) ONTO-PDM: Product-driven ONTOlogy for Product Data Management interoperability within manufacturing process environment. Adv Eng Inform 26(2):334–348

    Article  Google Scholar 

  26. Zhang Y, Luo X, Zhang H, Sutherland JW (2014) A knowledge representation for unit manufacturing processes. Int J Adv Manuf Technol 73(5-8):1011–1031

    Article  Google Scholar 

  27. Kang M, Kim G, Lee T, Jung CH, Eum K, Park MW, Kim JK (2016) Selection and sequencing of machining processes for prismatic parts using process ontology model. Int J Precis Eng Manuf 17(3):387–394

    Article  Google Scholar 

  28. Šormaz D, Sarkar A (2019) SIMPM – Upper-level ontology for manufacturing process plan network generation. Robot Comput Integr Manuf 55:183–198

    Article  Google Scholar 

  29. Musen MA, Team P (2015) The protege project: a look back and a look forward. AI matters 1(4):4–12

    Article  Google Scholar 

  30. Zhang Y, Luo X, Zhao Y, H-c Z (2015) An ontology-based knowledge framework for engineering material selection. Adv Eng Inform 29(4):985–1000

    Article  Google Scholar 

  31. Sirin E, Parsia B, Grau BC, Kalyanpur A, Katz Y (2007) Pellet: a practical owl-dl reasoner. Web Semant Sci Serv Agents World Wide Web 5(2):51–53

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xionghui Zhou.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, H., Zhou, X., Liu, W. et al. A customizable process planning approach for rotational parts based on multi-level machining features and ontology. Int J Adv Manuf Technol 108, 647–669 (2020). https://doi.org/10.1007/s00170-020-05437-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-05437-0

Keywords

Navigation