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 backpropagation 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 neighbors, 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.

Automated summary

This paper presents a framework of data-driven intelligence system for the MF-EDM machining process of SiCp/Al composites. The implemented system consists of modules for data modelling, predicting, optimization and monitoring. The results show that the BPNN model, used in conjunction with the MOMS optimization algorithm, achieves the highest accuracy compared to other models.