Uncertainty quantification of machining simulations using an in situ emulator
Uncertainty quantification of machining simulations using an in situ emulator
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DOI：
10.1080/00224065.2018.1474689
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发表时间：
2018-07
影响因子：
2.5
通讯作者：
Evren Gul;V. R. Joseph;Huan Yan;S. Melkote
中科院分区：
文献类型：
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作者：
Evren Gul;V. R. Joseph;Huan Yan;S. Melkote
摘要 了解仿真输出的不确定性对于仔细决策加工过程非常重要。然而，当模拟计算量很大时，基于蒙特卡罗的方法不能用于评估不确定性。另一种方法是构建一个易于评估的模拟器来近似计算机模型并在模拟器上运行蒙特卡洛模拟。尽管这种方法非常有前途，但当计算机模型高度非线性且感兴趣区域很大时，它就会变得低效。大多数加工模拟都是这种类型，因为输出受到多个定量因素的影响，例如工件材料属性、切削刀具参数和工艺参数，这些因素的影响可能会根据其他定性因素（例如材料类型、刀具设计和刀具路径）而变化。由于定性因素的级别数量可以从数十到数千不等，因此构建精确的模拟器并不是一件容易的事。本文提出了一种称为原位仿真器的新方法来克服这个问题。这个想法是为用户指定的定性因素水平和由定量因素的输入不确定性分布定义的局部区域构建一个模拟器。高效的实验设计和统计建模技术用于构建原位模拟器。通过模拟两个整体立铣削工艺来说明该方法。
ABSTRACT Understanding the uncertainty in simulation outputs is important for careful decision-making regarding a machining process. However, Monte Carlo–based methods cannot be used for evaluating the uncertainty when the simulations are computationally expensive. An alternative approach is to build an easy-to-evaluate emulator to approximate the computer model and run the Monte Carlo simulations on the emulator. Although this approach is very promising, it becomes inefficient when the computer model is highly nonlinear and the region of interest is large. Most machining simulations are of this kind because the output is affected by several quantitative factors—such as the workpiece material properties, cutting tool parameters, and process parameters whose effects can change depending on other qualitative factors such as the type of materials, tool designs, and tool paths. Because the number of levels of the qualitative factors can range from tens to thousands, building an accurate emulator is not an easy task. This article proposes a new approach, called an in situ emulator, to overcome this problem. The idea is to build an emulator for the user-specified levels of the qualitative factors and inside the local region defined by the input uncertainty distribution of the quantitative factors. Efficient experimental design and statistical modeling techniques are used for constructing the in situ emulator. The approach is illustrated using the simulations of two solid end milling processes.