A Comparison between Finite Element Model (FEM) Simulation and an Integrated Artificial Neural Network (ANN)-Particle Swarm Optimization (PSO) Approach to Forecast Performances of Micro Electro Discharge Machining (Micro-EDM) Drilling.

Artificial Neural Network (ANN), together with a Particle Swarm Optimization (PSO) and Finite Element Model (FEM), was used to forecast the process performances for the Micro Electrical Discharge Machining (micro-EDM) drilling process. The integrated ANN-PSO methodology has a double direction functionality, responding to different industrial needs. It allows to optimize the process parameters as a function of the required performances and, at the same time, it allows to forecast the process performances fixing the process parameters.

Cost Index Model for the Process Performance Optimization of Micro-EDM Drilling on Tungsten Carbide.

The present work deals with the execution of through micro-holes on tungsten carbide plates using a micro-electrical discharge machining (micro-EDM) machine. The experiments were carried out by varying peak current, voltage and frequency in order to achieve suitable technology windows. Tubular electrodes, made of two different materials (tungsten carbide and brass), were used.