Ensemble analysis of parameters optimizations of copper electroplated NiTinol Plate through Matlab and robotic swarm particle

Ensemble analysis of parameters optimization using Artificial Intelligence (AI) techniques of full factorial Swarm Differential Algorithm (SDA) and further with Matlab was applied in experimental procedure conducted in copper electroplated NiTinol Plate by evaluating its surface roughness of NiTinol plate. This approach applied optimization module using Artificial Intelligence model of swarm particle optimization which enhanced the accuracy and reliability of the parameters implied control. The analysis was done based on parameter analysis of temperature, thickness of Copper electroplated NiTinol plate, current density. After Data prepossessing, support vector Regression (SVR) was analyzed for pragmatic. Further, datasets were splatted into trained AI Model using subsequently its each dataset and then validating sets by evaluating individual performances. Stacking was used for training meta-model for combined predictions of individual models. Optimized parameters using Swarm differential algorithm in MATLAB was deployed. The results of the ensemble analysis were used to further validate the parameters of temperature and current density of electroplated NiTi plates. Optimized parameters model was further used for predicting and controlling the surface roughness in electroplated NiTinol plate which hence enhanced its surface finish. The analysis of Nitinol electroplating using swarm intelligence technique optimized coating thickness and surface roughness thus enhanced solder ability providing a sacrificial layer of micro fabrication,corrosion resistant biocompatible surface layer. The approach utilized a simulated 10×10 grid of measurements to identify under plated regions and rough surface areas, followed by a Particle Swarm Optimization (PSO) and MATLAB based parameters optimization strategy.