A generic and innovative approach for integrated simulation and optimisation of end milling using solid modelling and neural network

Abstract

Accurate and integrated modelling and optimisation of 2½ - axis end milling using standard tools and novel approaches is presented here. The resulting system provides a practical bridge between current state-of-the-art CAD/CAM and optimised CNC production. First, a novel and generic approach for extracting the in-cut geometry is implemented using ACIS® open architecture solid modeller. Next, an ANN (artificial neural network) model is designed and implemented for force prediction. Finally, a new technique for optimising the cutting parameters is applied and verified. The technique is based on reverse mapping of the ANN model for cutting force estimation. As such, the machining process is optimised directly using the learned neural network model by adjusting the net inputs to optimal values that minimise a specified objective function subject to cutting constraints. The optimisation results are used to update the initial CL (cutter location) data file to produce an optimum NC (numerically controlled) code. The proposed approach is demonstrated and verified through a case study to show its validity, practicality, and applicability.

Keywords:

• end milling
• geometric modelling
• neural network
• force prediction
• optimisation