Developing digital twin design for enhanced productivity of an automated anodizing industry and process prediction using hybrid deep neural network

Abstract

Automation is beneficial when implemented in challenging environments requiring less human effort or reducing human effort. Employee safety is a concern to increase productivity, delivery and achieve the required quality. The present research investigates the possibility of installing an automation procedure in an anodizing industry called GOLDEN ANODIZER, which is situated in Coimbatore, India. During the preliminary analysis at the specified industry premises, there was a reduction in productivity, quality and customer delivery due to manual operations, including some health issues were identified. And hence the present investigation analyses the possibility of installation of an automation system for the anodizing process. For such reason, the digital twin of an automation system is first designed, developed and tested using Siemens NX software. The anodizing factors such as Anodizing medium temperature (K), Acid Concentration (wt%), applied voltage (V) and Responses Surface Finish (Ra), Film Thickness (tf) and Time Duration (T) were optimized for improved productivity and quality. Prediction results and RMSE analysis show that the proposed hybrid PSO-LFA algorithm has outperformed all modern algorithms. The proposed algorithm optimized the anodizing parameters and suggested 3650.7 s as a new cycle time. Also, the improved cycle time can boost the plant outcome by 182% and reduce the manpower by 46% through the proposed Automation system.