Numerical Analysis of Sagging Based on Rheological Properties of a Paint Film and Proposal for a Novel Index to Evaluate the Amount of Sag

In the present study, we used computational fluid dynamics to analyze the sag caused by spray painting, considering the change in paint shape due to flow. We focused on the paint adhering to the target surface because this behavior has not been previously examined. The particle method was adopted for the calculation because it enabled a stable analysis of the paint droplets and the complex uneven surface of the paint film. A high-speed camera and image analysis were used to capture the spray painting and identify the parameter values. Using the developed model, we analyzed the change in the film thickness distribution for painting on a flat plate in the vertical direction. It was confirmed that the numerical and experimental data correlated for two conditions of the target distance. In addition, we proposed a new index, Degree of Sagging (DSG), to evaluate the amount of sag based on the physical properties of the paint rheology and the geometry of the target. In the painting tests on flat plates with different angles, a strong positive correlation of 0.95 was observed between the sum of the calculated DSG values and the measured paint flow distance due to sag. In the painting tests on L-shaped surfaces, the predicted sag appearance by DSG agreed with the measured results at 14 of the 15 measurement points under all conditions. Overall, a computational model was developed for field implementation that could predict painting thickness distribution and sag occurrence. Note to Practitioners—The present work is motivated by the problem that teaching painting robots in a painting site is particularly time consuming. To address this problem, off-line programming methods have been proposed to computationally predict paint quality and film thickness and determine the painting route. Conventional approaches have approximated the paint deposition model with a function, which determines the paint thickness distribution, or simulated this model with high accuracy by considering electrostatic forces and wind velocity fields using computational fluid dynamics (CFD) modeling methods. However, two major problems exist in the field implementation of these studies: The first problem is the inability to predict defects, including sagging, that may occur after painting. In the conventional approach, the behavior of the paint after it adheres to the target is outside the scope of the calculation. The second problem is the high computational resource cost of CFD simulation, which renders its application to robotic painting challenging. In painting sites, the painting thickness must be accurately predicted and routed in a short period of time. In this paper, we present a method for analyzing film thickness distributions by considering time series changes of flow and propose a function-based model for predicting sagging based on the simulation results. The model for sag prediction was shown to be computationally inexpensive enough to be implemented in the field. The results of several painting experiments with a real robot confirmed the validity of the predictions made by this method. Our method could be implemented in the near future as a prediction system for off-line evaluation of painting routes, which could significantly reduce teaching cost. Since the target in this paper was a flat plate, it is necessary to conduct evaluation tests using various target shapes and painting conditions. Also, integration with the painting route optimization method has not yet been achieved, which is a topic that will be addressed in the future.