Experimental analysis and machine learning with IoT monitor in two-way abrasive flow machine polishing on P20 mold components

The purpose of this paper was to reveal the effects of pressure and time on the surface roughness (Ra) and compare the experimental design (Factorial Regression) and machine learning (ML) of steel mold workpieces. Methodology began with turning, and fine sandpaper P180 to P1200 and measured with an initial value of Ra compared with the final value of Ra at the end of the two-way prototype AFM process. The process parameters are as follows; abrasive particle size (alumina; Al2O3) 5.0 μm in Silicone Oil (concentration 50% by weight) at pressures (p) of 10 bar and 20 bar, processing time (t) 5, 15, and 25 min, specimens P20 Mold steel. The experimental results show that under these conditions. The average surface roughness of the specimens differed from the initial value by Ra 0.034 to 0.021 μm, with delta values ranging from 0.011 μm to 0.005 μm. The results showed a smoother profile between before and after polishing. The approach to the topic is DOE and ML, and the theoretical or subject scope of the paper is Statistical and AI. The original value of the paper is applied to the ESP32 Arduino to control and display critical parameters. A General Factorial Regression statistical value of 76.39% is acceptable. A pressure factor of 20 bars and a time of 25 minutes gives the best effect on surface roughness. ML assisted in predicting the Surface Roughness for optimization based on the experiment.