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Optimization of vacuum counter-pressure casting process for an aluminum alloy casing using numerical simulation and defect recognition techniques

机译:使用数值模拟和缺陷识别技术优化铝合金壳体真空反压铸造工艺

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摘要

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.
机译:真空反压力铸造(VCPC)工艺是生产具有复杂薄壁特性的组分的有效成型过程。在该研究中,使用具有五个工艺参数的代理基优化方法来减少复杂的薄壁铝合金壳体的VCPC过程中的收缩孔隙率缺陷。通过使用VCPC过程的数值模拟来实现对收缩孔隙率缺陷的预测。通常,由于计算和时间约束,基于铸模直接执行优化可能不是实际的。为了缓解该问题,采用计算机实验构建的响应表面模型(RSM)来精确地近似于从铸造模拟产生的缺陷图像中的缺陷面积之间的功能关系。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,以识别和量化图像的缺陷。随后使用基于函数的优化方法配制优化问题,并且使用内置的RSM估计目标函数。最终结果表明,可以获得一组具有最小缺陷的最佳过程参数。

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