基于响应曲面法压铸用Al2O3-40%TiO2涂层制备工艺研究

Preparation and Process Optimization of AlO-40%TiO Coating for Aluminum Alloy Die Casting

为改善铝合金压铸用钢制工器具的耐铝液腐蚀性能,提高压铸循环次数并降低成本。采用等离子喷涂工艺,在H13钢制工器具表面制备了Al2O3-40%TiO2涂层。基于响应曲面法(RSM)优化等离子喷涂工艺参数,利用Box-Behnken(BBD)设计分析了电压(U)、电流(I)和喷涂距离(d)等主要因素对涂层显微硬度(H)和孔隙率(P)的影响规律,建立了电压(U)、电流(I)和喷涂距离(d)与显微硬度(H)和孔隙率(P)之间的数学模型,构筑了涂层显微硬度(H)和孔隙率(P)的响应曲面。建立的数学模型可靠,在本工艺条件下,经优化后涂层的显微硬度(H)和孔隙率(P)的曲线方程可分别表述为:H=5 733.59+330.51×U-38.33×I-97.57×d-0.31×U×I+1.43×U×d-2.41×U2+0.053×I2, P=-32.8-0.32×U+0.11×I+0.27×d-0.004×U×d+0.005 6×U2-0.000 11×I2。优化的数学模型可应用于Al2O3-40%TiO2涂层的喷涂工艺参数优化和性能预测。模型预测的涂层最佳显微硬度(H)和孔隙率(P)的工艺参数为:U=70 V, I=475 A, d=120 mm,涂层显微硬度(H)和孔隙率(P)分别为965.46 HV0.3和0.87%,实际测定的涂层显微硬度(H)和孔隙率(P)分别为978.35 HV0.3和0.82%,二者结果相近。响应曲面法(RSM)所具有的预测优势,可弥补常规实验优化的不足,运用到先进涂层技术的工艺设计和数据分析中,用于喷涂工艺的优化,有利于高性能涂层的研制。

In order to improve the corrosion resistance of steel tools used in aluminum alloy die casting, increase the number of die casting cycles and reduce costs, the Al2O3-40% TiO2 coatings were prepared on the H13 substrate by means of plasma spraying. Based on the response surface method(RSM), the plasma spraying process parameters were optimized, and the BoxBehnken(BBD) was used to analyze the main factors, such as voltage(U), current(I) and spraying distance(d), on the coating micro hardness(H) and porosity(P), the mathematical models between voltage( U), current( I), spraying distance( d) and porosity( P) were established. The response surface of micro hardness(H) and porosity(P) were constructed. The established mathematical models are reliable. Under this process condition, the curve equations of the optimized coating micro hardness( H) and porosity( P) can be expressed as: H =5733.59+330.51×U-38.33×I-97.57×d-0.31×U×I+1.43×U×d-2.41×U2+0.053×I2, P=-32.8-0.32×U+0.11×I+0.27×d-0.004×U×d+0.005 6×U2-0.000 11×I2. The optimized mathematical models can be applied to the optimization of spraying parameters and performance prediction of Al2O3-40%TiO2 coating. The optimum process parameters predicted by the model were U=70 V, I=475 A,d=120 mm, respectively. Under this condition, the predicted micro hardness(H) and porosity( P) of the coating were 965.46 HV0.3 and 0.87%, respectively. The actual measured micro hardness(H) and porosity(P) of the coating were 978.35 HV0.3 and 0.82%, respectively. The results of the predicted and measured values are similar. The prediction advantage of response surface method(RSM) can make up for the shortcomings of conventional experiment optimization.It can be used in the process design and data analysis of advanced coating technology to optimize the spraying process, which is conducive to the development of high-performance coatings.