Modelling of Machining Time and its Uncertainty in Micro Electrical Discharge Machining Process Using RSM

• Main Authors: Hasan, Mohammad Mainul, Saleh, Tanveer, Sophian, Ali
• Format: Proceeding Paper
• Language: English; English
• Published: IEEE Xplore 2024
• Subjects: T Technology (General); T175 Industrial research. Research and development; TJ Mechanical engineering and machinery
• Online Access: http://irep.iium.edu.my/115950/1/IEEE_Mainul.pdf; http://irep.iium.edu.my/115950/7/Cover_Page%20_Conceptualization%20of%20a%20Hybrid%20Machine%20%281%29-combined-compressed-1.pdf; http://irep.iium.edu.my/115950/; https://ieeexplore.ieee.org/document/10652357

Abstract—In modern manufacturing processes, precision and efficiency are paramount, particularly in a stochastic method such as Micro Electrical Discharge Machining (μEDM). With its ability to fabricate intricate geometries in hard and difficult-to-machine materials, μEDM has become indispensable in various industries. However, optimising machining time while ensuring reliability and accuracy remains a significant challenge that often relies on the intuition and experience of machinists. In response, this paper presents a comprehensive study on the modelling of machining time (MT) and also its associated uncertainty characterised by the standard deviation (SD) in the μEDM process using Response Surface Methodology (RSM). The study utilises experimental data generated through the I-optimal design of experiment technique (DOE) to develop quadratic equations for the MT Mean and SD MT models. The input factors for the models include capacitance, voltage, feed rate, tool speed, tool diameter, workpiece thickness, and workpiece materials defined by thermal conductivity, melting point, and electrical resistivity. The adequacy of both models was assessed based on ANOVA and fit statistics, demonstrating their effectiveness. Additionally, the mean percentage accuracies of the MT Mean and SD MT models are evaluated as 74.5% and 58% for the train set and 86.8% and 82.1% for the test set, respectively. By leveraging RSM techniques, this research aims to provide insights into the complex relationships between machining parameters and the resulting machining time and uncertainty, facilitating enhanced process μEDM control and performance optimisation.