Generating prediction model of fan width by optimizing paint application process for Electrostatic Rotary Bell atomizer

Fan Width plays a critical role in the paint spray application process, so much so that it can be represented as the backbone of the process. In this process, a high voltage Rotary atomizer along with shaping air discharge paint particles to create a spray pattern which defines the basic fan width. Many painting industries practice a "trial and error" method to determine the fan width of their coating process which can be inconsistent and lead to poor quality results. A predictive model for this dynamic fan width pattern, based on the application inputs, can make a significant efficiency improvement in the spray application technique to deliver a uniform paint distribution across the painted object. This research presents a fan width prediction model for a specific rotary bell atomizer and a waterborne automotive OEM paint color. A full factorial Design of Experiement has been performed to analyze factors that influence the dynamic spray pattern. From this a linear regression model has been derived to calculate the fan width which varies within 2.5 cm to maintain manufacturing tolerance. The model has been verified statistically and experimentally, so that it can eliminate the trial-and-error method to save time and cost.

扇面宽度在喷漆应用过程中起着至关重要的作用，以至于它可被视为该过程的关键。在此过程中，高压旋转雾化器与整形空气一起喷出油漆颗粒，形成一种喷雾模式，该模式决定了基本的扇面宽度。许多喷漆行业采用“试错”方法来确定其涂装过程的扇面宽度，这种方法可能不一致，并导致质量不佳的结果。基于应用输入的这种动态扇面宽度模式的预测模型，可以显著提高喷涂应用技术的效率，使油漆在被喷涂物体上均匀分布。本研究针对一种特定的旋杯雾化器和一种水性汽车原厂漆颜色提出了一种扇面宽度预测模型。进行了全因子实验设计，以分析影响动态喷雾模式的因素。由此推导出一个线性回归模型来计算扇面宽度，其在2.5厘米范围内变化以保持制造公差。该模型已经过统计和实验验证，因此它可以消除试错方法，从而节省时间和成本。