Artificial neural network-based prediction assessment of wire electric discharge machining parameters for smart manufacturing

Abstract Artificial intelligence (AI), robotics, cybersecurity, the Industrial Internet of Things, and blockchain are some technologies solutions that combined to produce “smart manufacturing,” which is used optimize manufacturing processes by creating and/or accepting data. In manufacturing, spark erosion technique such as wire electric discharge machining (WEDM) a process machines different hard-to-cut alloys. It regarded solution for cutting intricate parts materials resistant conventional techniques or required design. present study, holes radii, i.e. 1, 3, 5 mm, have been cut on Nickelvac-HX. Tapering in WEDM delicate avoid disadvantages break, bend, friction, guide wear, insufficient flushing. Taper angles viz. 0°, 15°, 30° were obtained from unique fixture get at angles. The study also shows influence taper part geometry area holes. Next, artificial neural network (ANN) implemented parametric result prediction. findings good agreement with experimental data, supporting viability ANN approach evaluation process. this research provide reference potential AI-based assessment smart design tool many manufacturing-related fields.