Data-Driven Analysis In Magnetic Field-Assisted Electrical Discharge Machining of High Volume SiCp/Al

This paper presents a framework of data-driven intelligence system which can be applied on magnetic field-assisted electrical discharge machining (MF-EDM) machining process for SiC particulate reinforced Al-based metal matrix composites (SiCp/Al) with different high-volume fractions. The implemented system consists of data modelling, predicating, optimization and monitoring modules. A multi-objective moths search (MOMS) optimization algorithm with back-propagation neural network (BPNN) model and multi-hierarchy non-dominated strategy is proposed for tuning optimal processing performance. Data are collected from machining different fraction volumes of SiCp/Al composites by MF-EDM, with peak current, magnetic, pulse width and pulse interval time as input, and material removal rate, electrode wear rate, surface roughness as output. The BPNN model shows the best accuracy compared to K-nearest neighbours, least square support vector machine and Kriging model. To demonstrate the effectiveness of the MOMS optimization algorithm, a set of results is selected as paradigm, which dominates 95.83% original experiments. A verification experiment is also done for an optimized parameter with 65% fraction and 0.2T magnetic. Both result data and three-dimensional surface topography comparison show that the verification experiment result dominates the original experiment of similar input designs.

Optimizing Main Process Parameters When Conducting Powder-Mixed Electrical Discharge Machining of Hardened 90CrSi

In the current study, an optimization process of powder-mixed electrical discharge machining (PMEDM) process when machining cylindrically shaped parts made of hardened 90CrSi steel is reported. In this study, SiC powder was mixed into the Diel MS 7000 dielectric solution. Additionally, graphite was chosen as the electrode material. The multi-objective functions were minimizing the surface roughness (SR) and electrode wear rate (EWR) and maximizing the material removal rate (MRR). The used input parameters of the optimization process included the powder concentration, the pulse-on time, the pulse-off time, the pulse current, and the servo voltage. A combination between the Taguchi method and the grey relation analysis (GRA) method with the support of Minitab R19 software was used to design the experiment and analyze the results. It was found that the optimal set of process parameters that can satisfy the above responses are Cp of 0.5 g/L, Ton of 8 µs, Toff of 8 µs, IP of 5 A, and SV of 4 V.

A NOVEL METHOD FOR SELECTING THE OPTIMAL EDM PROCESS FOR HASTELLOY B2 USING THE MODIFIED-ADDITIVE RATIO ASSESSMENT METHOD (M-ARAS) BASED ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)

Electrode wear and metal removal exhibited nonlinear behavior in the Electrical Discharge Machining (EDM) of Hastelloy B2 plate. Hence, mathematical modeling was used to solve this problem. The hole size, pulse duration, duty cycle, and current were selected as inputs. Squareness and taper angle were considered as responses. Therefore, the Modified-Additive Ratio Assessment Method (M-ARAS) based Adaptive Neuro Fuzzy Inference System (ANFIS) method was used to find the optimum EDM process parameters. The overall analysis showed that the M-ARAS-based ANFIS algorithm provided a good fit for optimization of the process parameters and could be used for further multi-objective optimization problems.

An Experimental Investigation on Micro Drilling of Cold Work Steel X153CrMoV12 By EDM

In this study, it was aimed to analyze the effects of machining parameters on the process quality by drilling holes in heat treated cold work tool steel with a hardness of 60-62 HRC using the electrical discharge machining (EDM) method and Ø2 mm diameter brass electrodes. In this context, drilling was performed using three different current values ​​(5, 6, 7 A), three different voltage values ​​(1, 2, 3 V), three different discharge pulse frequency Ton (23, 26, 29 µs) as well as Toff (3, 5 µs) respectively, and the effects of these machining parameters on the machining time, material removal rate (MRR), electrode wear rate (EWR), surface roughness (SR) and hardness of around the white layer were analyzed using micro, macro and analytical measurements, especially with Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Analysis (EDX). As a result of the analysis, ıt was observed that current, voltage, Ton and Toff had an effect on machining time, MRR, EWR, SR and hardness, but current was the most effective parameter, and also worn electrode as well as workpiece residues affected the process quality. Increasing the machining current increased sparking between the workpiece and the electrode, resulting in increased point melting and evaporation, resulting in increased average surface roughness, metal removal rate, and electrode wear rate. As a result of the high metal removal rate, the machining time was greatly reduced and the thermal effect time was reduced, which led to a decrease in the hardness variation on the machined surfaces.

Suggested Research Trends in the Area of Micro-EDM—Study of Some Parameters Affecting Micro-EDM

This paper provides an overall view of the current research in micro-electrical discharge machining (micro-EDM or µEDM) and looks into the present understanding of the material removing mechanism and the common approach for electrode material selection and its limitations. Based on experimental data, the authors present an analysis of different materials’ properties which have an influence on the electrodes' wear ratio and energy distribution during the spark. The experiments performed in micro-EDM conditions reveal that properties such as electron work function and electrical resistivity strongly correlate with the discharge energy ratio. The electrode wear ratio, on the other hand, is strongly influenced by the atomic bonding energy and was found to be related to the tensile modulus. The proposed correlation functions characterized the data with a high determination coefficient exceeding 99%.