Skip to main content
SpringerLink
Log in

A parameterized automatic programming solution for composite grinding based on digital image processing

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

Abstract

In this paper, a parameterized automatic programming solution whose advantage depends on the automatic feature recognition of digital image is proposed and applied to the development of automatic programming software for composite grinding. This solution can overcome the difficulty of recognizing intersecting feature of complicated rotational part. And a 7-layer CNN classifier is utilized to decide whether the part has the internal features or not, which makes the proposed feature recognition method more intelligent than retrieving the unknown objects blindly. The emphasis of the research is the geometric data extraction algorithm which is the synthesis of border following algorithm, corner detection algorithm and a variety of morphological processing. Under the condition of 12000 × 12000 pixel dimension and 200-dpi resolution of input image, the relative errors between the extracted and actual values of various geometric data are all less than 0.05% for the rotational parts of maximum diameter 500 mm and maximum length of 1500 mm. And all the extracted values of geometric data rounded to integers can fully meet the requirements of NC programming. The automatic programming software based on the proposed solution has excellent portability and practicability, which is independent of any CAD tools or data exchange standards. After the programs generated by the automatic programming software are validated in simulation software NCSIMUL, the software is integrated into HNC-848 CNC system and applied in the prototype of H377 composite grinding center.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Seeram SR, Ali MA, Karimulla S (2016) Automatic recognition of internal features of axisymmetric parts from 2-D images. Indian J Sci Technol 9(44). https://doi.org/10.17485/ijst/2016/v9i44/90267

  2. Rao SS, Satyanarayana B, Sarcar MMM (2012) Automated generation of NC part programs for turned parts based on 2-D drawing image files. Int J Prod Res 50(12):3470–3485. https://doi.org/10.1080/00207543.2011.588620

    Article  Google Scholar 

  3. Yi W (2017) Composite processing and composite grinding technology——Interview with Mr MU Donghui, the President of the Equipment Technology Institute of Beijing Jingcheng Machinery Electric Holding Co., Ltd. Manuf Technol Machine Tool 2:23–24. https://doi.org/10.3969/j.issn.1005-2402.2017.02.006

    Article  Google Scholar 

  4. Sortino M, Belfio S, Totis G, Gaspero LD, Nali M (2015) An investigation on swarm intelligence methods for the optimization of complex part programs in CNC turning. Int J Adv Manuf Technol 80(1-4):657–672. https://doi.org/10.1007/s00170-015-7011-8

    Article  Google Scholar 

  5. Cai RL, Li XD, Qian SS (2016) Research status and development trend of CNC system technology at home and abroad. Mech Sci Technol Aerosp Eng 4:493–500. https://doi.org/10.13433/j.cnki.1003-8728.2016.0401

    Article  Google Scholar 

  6. Zhang X, Nassehi A, Newman ST (2014) Feature recognition from CNC part programs for milling operations. Int J Adv Manuf Technol 70(1-4):397–412. https://doi.org/10.1007/s00170-013-5275-4

    Article  Google Scholar 

  7. Wswasi MA, Ivanov A, Makatsoris H (2018) A survey on smart automated computer-aided process planning (ACAPP) techniques. Int J Adv Manuf Technol 97:809–832. https://doi.org/10.1007/s00170-018-1966-1

    Article  Google Scholar 

  8. Arivazhagan A, Mehta NK, Jain PK (2008) Development of a feature recognition module for tapered and curved base features. Int J Adv Manuf Technol 39(3-4):319–332. https://doi.org/10.1007/s00170-007-1212-8

    Article  Google Scholar 

  9. Zhu J, Kato M, Tanaka T, Yoshioka H, Saito Y (2015) Graph based automatic process planning system for multi-tasking machine. J Adv Mech Des Syst 9(3):1–15. https://doi.org/10.1299/jamdsm.2015jamdsm0034

    Article  Google Scholar 

  10. Zubair AF, Mansor MSA (2018) Automatic feature recognition of regular features for symmetrical and non-symmetrical cylinder part using volume decomposition method. Eng Comput-Germany 34(4):843–863. https://doi.org/10.1007/s00366-018-0576-8

    Article  Google Scholar 

  11. Klancnik S, Brezocnik M, Balic J (2016) Intelligent CAD/CAM system for programming of CNC machine tools. Int J Simul Model 15(1):109–120. https://doi.org/10.2507/IJSIMM15(1)9.330

    Article  Google Scholar 

  12. Hao YT, Chi YM (2011) Research on ANN-based feature recognition and manufacturing behavior sequence. IEEE MACE 7568–7574. https://doi.org/10.1109/MACE.2011.5988802

  13. Sunil VB, Pande SS (2009) Automatic recognition of machining features using artificial neural networks. Int J Adv Manuf Technol 41(9–10):932–947. https://doi.org/10.1007/s00170-008-1536-z

    Article  Google Scholar 

  14. Amaitik SM (2017) Automatic Generation of CNC Codes Based on Machining Features. 1st Conference of Industrial Technology, At Misuraya, Libya, 1

  15. Rauch M, Laguionie R, Hascoet JY, Suh SH (2012) An advanced STEP-NC controller for intelligent machining processes. Robot Cim-Int Manuf 28(3):375–384. https://doi.org/10.1016/j.rcim.2011.11.001

    Article  Google Scholar 

  16. Nezis K, Vosniakos GC (1997) Recognizing 212D shape features using a neural network and heuristics. Comput Aided Des 29(7):523–539. https://doi.org/10.1016/S0010-4485(97)00003-1

    Article  Google Scholar 

  17. Hao P (2010) Development and current research of NC combined grinding technology. Aeronaut Manuf Technol 10:54–57. https://doi.org/10.3969/j.issn.1671-833X.2010.10.007

    Article  Google Scholar 

  18. Harris C, Stephens M (1988) A combined corner and edge detector. In: Proceedings of the 4th ALVEY Vision Conference, pp. 147–151. https://doi.org/10.5244/C.2.23

  19. Laptev I, Lindeberg T (2003) Space–time interest points. In: Proceedings of the International Conference on Computer Vision (ICCV’03) 1:432–439. https://doi.org/10.1109/ICCV.2003.1238378

  20. Suzuki S, Be K (1985) Topological structural analysis of digitized binary images by border following. Comp Vision Graph Image Process 30(1):32–46. https://doi.org/10.1016/0734-189X(85)90016-7

    Article  MATH  Google Scholar 

  21. Ramos L, Barreira N, Mosquera A, Verdeal HP, Pimentel EY (2013) Break-up analysis of the tear film based on time, location, size and shape of the rupture Area. In: Kamel M, Campilho A (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin. https://doi.org/10.1007/978-3-642-39094-4_79

    Chapter  Google Scholar 

  22. Douglas DH, Peucker TK (1973) Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartogr Int J Geogr Inf Geovis 10(2):112–122. https://doi.org/10.3138/FM57-6770-U75U-7727

    Article  Google Scholar 

  23. O’Shea K, Nash R (2015) An Introduction to Convolutional Neural Networks. Comput Sci ArXiv 1511:08458

    Google Scholar 

  24. Li J, Wu YR, Shen NY, Zhang JW, Chen EL, Sun J, Deng ZQ, Zhang YC (2019) A fully automatic computer-aided diagnosis system for hepatocellular carcinoma using convolutional neural networks. Biocybern Biomed Eng 40:40–248. https://doi.org/10.1016/j.bbe.2019.05.008

    Article  Google Scholar 

  25. He KM, Zhang XY, Ren SQ, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In: Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile. pp 1026–1034. https://doi.org/10.1109/ICCV.2015.123

  26. Lecun Y, Bottou L (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  27. Djassemi M (2000) An efficient CNC programming approach based on group technology. J Manuf Syst 19(3):213–217. https://doi.org/10.1016/s0278-6125(00)80013-8

    Article  Google Scholar 

  28. Rafa G (2017) Parametric programming of CNC machine tools. Matec Web Conf 94:07004. https://doi.org/10.1051/matecconf/20179407004

    Article  Google Scholar 

  29. SPRING Technologies (2010) User-Friendly NCSIMUL 8.8 Version of SPRING Technologies. Aeronaut Manuf Technol 19:93. https://doi.org/10.3969/j.issn.1671-833X.2010.19.016

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Science and Technology Major Project of China [2016ZX04004003].

Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Room 431, Building 9, No. 333, Nanchen Road, Baoshan District, Shanghai, 200444, China

    Nanyan Shen, Chen Zhao, Jing Li, Yingjie Xu, Yang Wu & Zongqian Deng

Authors
  1. Nanyan Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yingjie Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zongqian Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Nanyan Shen: conceptualization, writing—original draft, writing—review & editing, supervision, project administration. Chen Zhao: methodology, software, validation, formal analysis, investigation, data curation, writing—original draft, writing—review & editing, visualization. Jing Li: conceptualization, resources, supervision, project administration, funding acquisition. Yingjie Xu: software, data curation, investigation. Yang Wu: software, data curation, visualization. Zongqian Deng: methodology.

Corresponding author

Correspondence to Jing Li.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Shen, N., Zhao, C., Li, J. et al. A parameterized automatic programming solution for composite grinding based on digital image processing. Int J Adv Manuf Technol 110, 2727–2742 (2020). https://doi.org/10.1007/s00170-020-05984-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-05984-6

Keywords

Search

Navigation