Skip to main content
Log in

Intelligent grinding assistant (IGA(©)) - system development part I intelligent grinding database

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The selection of machining parameters is undertaken throughout the world on a daily basis. It remains an important activity that significantly impacts production cost. Alarmingly however, nearly all of this machining information is not recorded and there is a reliance on operators for its retention. To help address this problem, the present trend is to develop software systems able to record machining cycle data. However, this approach retains substantial data that is not optimal and a significant quantity of non-useful data is created. Selectivity provides a solution to the problem of data overload. This paper describes the structure, content, and relations employed in an intelligent grinding database developed to provide only selective and/or optimal data to the operator. The intelligent database was constructed in MS Access with Visual Basic support code. The database was developed as an integral feature of an intelligent grinding assistant (IGA©). The IGA© was implemented and evaluated on a cooperating partner’s CNC machine tool. The structure of the database is described in detail. An off-line feature to select grinding conditions for a workpiece material or workpiece dimension new to the database is also described. The offline feature was based on case-based reasoning (CBR) and rule-based reasoning (RBR).

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. Rowe WB, Li Y, Inasaki I (1994) Applications of artificial intelligence in grinding. Ann CIRP 43/2:521–531

    Article  Google Scholar 

  2. Li Y, Rowe WB, Mills B (1999) Study and selection of grinding conditions, part 1: grinding conditions and selection strategy. Proc Instn Mech Engrs, Part B 213:119–129

    Google Scholar 

  3. Rowe WB, Li Y, Mills B, Allanson DR (1996) Application of intelligent CNC in grinding. Compute Ind 31:45–60

    Article  Google Scholar 

  4. Chen Y (1998) A generic intelligent control system for grinding. PhD Thesis. Liverpool John Moores University, UK

  5. Li Y (1996) Intelligent selection of grinding conditions. PhD Thesis. Liverpool John Moores University, UK

  6. Machining data handbook. Machinability data centre (1980) 3rd edn., vol 2. Metcut Research Associates, Inc., USA

  7. Grinding data book (1992) Universal Grinding Wheel Co. Ltd., UK

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. Cai.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cai, R., Rowe, W.B., Moruzzi, J.L. et al. Intelligent grinding assistant (IGA(©)) - system development part I intelligent grinding database. Int J Adv Manuf Technol 35, 75–85 (2007). https://doi.org/10.1007/s00170-006-0702-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-006-0702-4

Keywords

Navigation