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Electrostatic spray deposition (ESD) of 'self organizing' TiO2-epoxy powder paints: Experimental analysis and numerical modeling

机译:“自组织” TiO2-环氧粉末涂料的静电喷涂(ESD):实验分析和数值模型

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This paper deals with an electrostatic spray deposition (ESD) process of 'self organizing' TiO2-epoxy powder paints on metal substrates. The first part of the paper focuses on analyzing the influence of both electrical and aerodynamic operational parameters on ESD process. Design of experiments (DOE) was used to plan experimental trials. ANOVA tables were calculated and used as support instruments to interpret the experimental findings and to understand physical phenomena involved in the deposition process as well as the role of TiO2. As a result, several process maps were produced which report consistent trends in coating thickness versus operational parameters. Best deposition conditions could therefore be deduced. Initial attempts to produce 'self-organizing' TiO2-based gradient coatings are also discussed. The second part of the paper focuses on assessing the influence of the target locations in the deposition booth on coating thickness of the TiO2-epoxy films. Design of experiments (DOE) was once again used to plan experimental trials and ANOVA tables were also calculated. Experimental findings are interpreted and the role of TiO2 is also investigated. This leads to the best target locations being deduced. Finally, neural network solutions able to predict coating thickness according to settings of operational parameters were developed and validated by comparing them with experimental data and a first approximation regression model. (c) 2006 Elsevier B.V. All rights reserved.
机译:本文研究了在金属基材上“自组织” TiO2-环氧粉末涂料的静电喷涂(ESD)工艺。本文的第一部分着重于分析电气和空气动力学操作参数对ESD过程的影响。实验设计(DOE)用于计划实验试验。计算了方差分析表并将其用作支持工具,以解释实验结果并了解与沉积过程有关的物理现象以及TiO2的作用。结果,产生了几个过程图,这些过程图报告了涂层厚度与操作参数的一致趋势。因此可以推断出最佳沉积条件。还讨论了生产“自组织” TiO2梯度涂料的初步尝试。本文的第二部分着重于评估沉积室中目标位置对TiO2-环氧膜涂层厚度的影响。实验设计(DOE)再次用于计划实验试验,还计算了ANOVA表。解释了实验结果,还研究了TiO2的作用。这导致推断出最佳目标位置。最后,通过将神经网络解决方案与实验数据和第一近似回归模型进行比较,开发并验证了能够根据操作参数设置预测涂层厚度的神经网络解决方案。 (c)2006 Elsevier B.V.保留所有权利。

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