A method to predict possibility of arcing in EDM of TiB2p reinforced ferrous matrix composite

• Published in: International journal of advanced manufacturing technology 2016-10, Vol.86 (9-12), p.2837-2849
• Main Authors: Pandey, A. B., Brahmankar, P. K.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; CAE) and Design; Ceramic tools; Computer-Aided Engineering (CAD; Cutting force; Cutting wear; Design of experiments; EDM electrodes; Electric discharge machining; Engineering; Industrial and Production Engineering; Machine shops; Mathematical models; Mechanical Engineering; Media Management; Metal matrix composites; Original Article; Parameter identification; Power supplies; Process parameters; Short circuits; Tool wear; Workpieces
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-016-8414-x

Electrical Discharge Machining (EDM) is very popular for machining conductive metal matrix composites (MMCs) because the hardness rendered by the ceramic reinforcements to these composites causes very high tool wear and cutting forces in conventional machining processes. EDM requires selection of a number of parameters for desirable results. Inappropriate parameter selection can lead to high overcuts, tool wear, excessive roughness, and arcing during machining and adversely affect machining quality. Arcing leads to short circuit gap conditions resulting in large energy discharges and uncontrolled machining. Arcing is a detrimental phenomenon in EDM which causes spoiling of workpiece and tool electrode and tends to damage the power supply of EDM machine. Parameter combinations that lead to arcing during machining have to be identified and avoided for every tool, work material, and dielectric combination. Proper selection of parameter combinations to avoid arcing is essential in EDM. In the work, experiments were conducted using L27 design of experiment to determine the parameter settings which cause arcing in EDM machining of TiB 2 p reinforced ferrous matrix composite. Important EDM process parameters were selected in roughing, intermediate, and finishing range so as to study the occurrence of arcing. Using the experimental data, an artificial neural network (ANN) model was developed as a tool to predict the possibility of arcing for selected parameter combinations. This model can help avoid the parameter combinations which can lead to arcing during actual machining using EDM. The ANN model was validated by conducting validation experiments to ensure that it can work accurately as a predicting tool to know beforehand whether the selected parameters will lead to arcing during actual machining using EDM. Validation results show that the ANN model developed can predict arcing possibility accurately when the depth of machining is included as input variable for the model.