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. The functionality is strictly related to the input and/or output fixed in the model. The FEM model was based on the capacity of modeling the removal process through the mesh element deletion, simulating electrical discharges through a proper heat-flux. This paper compares these prevision models, relating the expected results with the experimental data. In general, the results show that the integrated ANN-PSO methodology is more accurate in the performance previsions. Furthermore, the ANN-PSO model is faster and easier to apply, but it requires a large amount of historical data for the ANN training. On the contrary, the FEM is more complex to set up, since many physical and thermal characteristics of the materials are necessary, and a great deal of time is required for a single simulation.

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. The investigation focuses on the influence of variable process parameters on the process performances and their optimization. The performance indicators taken into account were Material Removal Rate (MRR) and Tool Wear Ratio (TWR). A general model based on a cost index was defined for the process performances optimization and the optimal conditions were identified through the minimization of the objective function.