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).
|Number of pages||11|
|Journal||The International Journal of Advanced Manufacturing Technology|
|Early online date||30 Aug 2006|
|Publication status||Published - Nov 2007|
- Intelligent grinding
- Artificial intelligence methods
- Grinding kinematics