The current work implements machine learning techniques such as artificial neural network (ANN), support vector machine (SVM), and genetic algorithm (GA) to model and optimize the surface roughness during wire electrical discharge machining (WEDM) of Inconel 718. For this, surface roughness values were obtained from real-time WEDM experiments conducted under the different levels of control factors such as pulse on time, pulse off time, peak current, servo voltage, and wire feed rate. The optimum ANN model architecture was identified as 5-10-10-1 and SVM parameters were tuned with the help of the grid search technique. The ANN and SVM models’ predictions were compared with response surface methodology (RSM) predictions and performance was evaluated based on correlation coefficient (R-value) between experimental and model predictions. The SVM predictions were accurate among all the models studied, as determined from the R-value of 0.99998 with experimental results and the least mean absolute percentage error (MAPE) of 0.0347%. Further, the GA approach was implemented using the developed RSM equation as the fitness function and led to 61.31% improvement in the surface roughness. The proposed SVM and GA approach would help quick and high accurate prediction and optimization of surface roughness during WEDM of Inconel 718.

Machine learning and statistical approach in modeling and optimization of surface roughness in wire electrical discharge machining

The machine learning methodology is gaining immense exposure as a potential methodology for solving and modelling the machining behaviour of advanced materials. The present paper deals with the application of machining learning approach in analyzing and predicting the effect of reinforced silicon carbide (SiC) particle size on the erosion behaviour of silicon carbide reinforced polymer composites. L27 orthogonal array was designed based on Taguchi’s methodology to execute the experiments. Support vector machine (SVM) and multi-linear regression (MLR) approach were coupled with Taguchi’s methodology to validate obtained optimized response characteristics. These machine learning-based SVM and MLR models are adopted to analyze the absurdity among obtained experimental results and predicted response. Out of 27 experimental runs based on experimental design, 19 experimental runs were selected for training models whereas 08 models were selected for the testing phase. Impingement angle, workpiece reinforcement, standoff distance and slurry pressure were used as input process parameters, whereas material loss was observed as response characteristics. The kernel functions, i.e. Pearson VII based universal kernel (PUK) and radial based function (RBF) kernel were used with machine learning models to obtain the best performing machine learning approach in predicting erosion behaviour of polymer composites.

Machine Learning-Based Erosion Behavior of Silicon Carbide Reinforced Polymer Composites

Manufacturing more efficient low pressure turbines has become a topic of primary importance for aerospace companies. Specifically, wire electrical discharge machining of disc turbine fir-tree slots has attracted increasing interest in recent years. However, important issues must be still addressed for optimum application of the WEDM process for fir-tree slot production. The current work presents a novel approach for tolerance monitoring based on unsupervised machine learning methods using distribution of ionization time as a variable. The need for time-consuming experiments to set-up threshold values of the monitoring signal is avoided by using K-means and hierarchical clustering. The developments have been tested in the WEDM of a generic fir-tree slot under industrial conditions. Results show that 100% of the zones classified into Clusters 1 and 2 are related to short-circuit situations. Further, 100% of the zones classified in Clusters 3 and 5 lie within the tolerance band of ±15 μm. Finally, the 9 regions classified in Cluster 4 correspond to situations in which the wire is moving too far away from the part surface. These results are strongly in accord with tolerance distribution as measured by a coordinate measuring machine.

https://www.mdpi.com/1424-8220/18/10/3359/pdf

Unsupervised Machine Learning for Advanced Tolerance Monitoring of Wire Electrical Discharge Machining of Disc Turbine Fir-Tree Slots

Spark erosion is a convenient, flexible, and low-cost method to quickly produce fine powders of metals, alloys, and semiconductors in size ranging from nearly a hundred micrometers to submicrometer by repetitive spark discharges. Due to the complexity of the powder-forming journey, normally accompanied by high temperature, high pressure, decomposition, diffusion and rapid quenching caused by discharge plasma, the mechanisms of powder formation and possible contaminant infiltration are still controversial, posing a significant challenge to control particle size and chemical composition of the powder produced by this method. In this study, Fe-based amorphous powders in different particle-size distributions with high sphericity were fabricated by spark erosion under different discharge-energy conditions. The maximum particle size of the resultant powders can be correlated with discharge parameters, crater depth, and crater radius, respectively. A multi-ring-breakup model is proposed to reveal the particle-size distribution of the powder formed from the electrode melt under a single-pulse discharge. Furthermore, a dielectric-element infiltration model is provided to quantitatively evaluate the infiltration mass ratio of the contaminant elements, stemming from the decomposed products of dielectric liquid, in the resultant powder with different particle sizes. The models verified through the experimental data are significant for the development of high-performance fine Fe-based amorphous powder with controlled particle size and chemical composition. 

Formation mechanism of Fe-based amorphous powders produced by spark erosion

Digital twins for electro-physical, chemical, and photonic processes

Online Workpiece Height Estimation for Reciprocated Traveling Wire EDM Based on Support Vector Machine

Study on energy distribution of discharge plasma and its effect on crater formation in EDM

Characterization of plasma discharge channel in EDM using photography and spectroscopy

A parallel PIC-MCC simulation of microsecond discharge modeling in EDM

Ana-Hash Table Sorting Algorithm for Curve Objects in WEDM CAM

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