Online Model-Based Input Shaping for Precision Application Processes

Abstract Precision application within the coatings industry is a highly sought-after process with substantial gains in efficiency and performance. Its high degree of customizability allows for tailored solutions that meet specific requirements, further enhancing the overall quality and effectiveness of the coatings. Despite these advantages, precision application is a complex process influenced by numerous factors, and achieving the desired outcome is not always guaranteed. Typically, the printing performance is assessed after the process, with parameters being fine-tuned incrementally through trial and error. However, this routine of post-process evaluation can undermine its efficiency gains due to the repetitive cycles involved. This inefficiency highlights a clear gap in the industry for more advanced sensing and control systems that can construct an optimal input shape in real time. This work explores the experimental implementation of a control framework that synthesizes online model identification and model-based input shaping to achieve desired dimensions in uniform one- and two-dimensional printed patterns. Jetting frequency was selected as the primary input, allowing for the use of physics-based models to predict the process dynamics. Experimental validation of the proposed approach shows relatively consistent convergence of line width and film volume to reference values within 5 iterations. Additionally, minimal a priori information about the ink-substrate properties is required due to the incorporation of online model identification.