Abstract

Vacuum counter-pressure casting (VCPC) process is an effective forming process of producing components with complex thin-walled characteristics. In this study, a surrogate-based optimization method with five process parameters is used to reduce the shrinkage porosity defect during the VCPC process of a complicated thin-walled aluminum alloy casing. The prediction of the shrinkage porosity defect is implemented by using the numerical simulation of the VCPC process. In general, performing the optimization directly based on the casting simulation may not be practical due to the computational and time constraints. To mitigate this problem, the response surface model (RSM) constructed via computer experiments is employed to accurately approximate the functional relationship between the process parameters and the response variable which is defined as the area of defects in the defect image generated from the casting simulation. To measure the area of defects in the image, a histogram-based threshold segmentation approach is proposed to recognize and quantify the defects from the image. The optimization problem is subsequently formulated using a desirability function-based optimization method, and the objective function is estimated using the built RSMs. The final results indicate that a set of optimal process parameters with minimum defects can be obtained.

中文翻译：

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利用数值模拟和缺陷识别技术优化铝合金壳体的真空反压铸造工艺

摘要

真空反压铸造（VCPC）工艺是生产具有复杂薄壁特性的零件的有效成型工艺。在这项研究中，使用具有五个工艺参数的基于替代的优化方法来减少复杂薄壁铝合金外壳VCPC过程中的收缩孔隙率缺陷。收缩孔隙率缺陷的预测是通过使用VCPC过程的数值模拟来实现的。通常，由于计算和时间限制，直接基于铸造模拟执行优化可能不切实际。为了减轻这个问题，通过计算机实验构建的响应表面模型（RSM）可以精确地估算过程参数与响应变量之间的函数关系，响应变量定义为铸造模拟生成的缺陷图像中的缺陷区域。为了测量图像中的缺陷区域，提出了一种基于直方图的阈值分割方法来识别和量化图像中的缺陷。随后，使用基于期望函数的优化方法制定优化问题，并使用内置的RSM估算目标函数。最终结果表明可以获得一组具有最小缺陷的最佳工艺参数。提出了一种基于直方图的阈值分割方法来识别和量化图像中的缺陷。随后，使用基于期望函数的优化方法制定优化问题，并使用内置的RSM估算目标函数。最终结果表明可以获得一组具有最小缺陷的最佳工艺参数。提出了一种基于直方图的阈值分割方法来识别和量化图像中的缺陷。随后，使用基于期望函数的优化方法制定优化问题，并使用内置的RSM估算目标函数。最终结果表明可以获得一组具有最小缺陷的最佳工艺参数。