Model transfer across additive manufacturing processes via mean effect equivalence of lurking variables
Model transfer across additive manufacturing processes via mean effect equivalence of lurking variables
复制标题
通过潜伏变量的平均效应等价在增材制造过程中进行模型转移
DOI：
10.1214/18-aoas1158
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Qiang Huang
中科院分区：
文献类型：
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作者：
Arman Sabbaghi;Qiang Huang
形状偏差模型是增材制造 (AM) 系统质量控制的重要组成部分。然而，特定模型在系统中广泛的过程中的应用范围有限，这些过程的特点是过程变量（包括潜伏变量）的不同设置。我们开发了一种新的效果等效框架和贝叶斯方法，可以在有限的实验运行的情况下实现增材制造系统中跨进程的偏差模型传输。模型迁移是通过根据观察到的因素推断潜伏变量的等效效果来执行的，该观察到的因素的影响已在先前学习的过程中进行了建模。对立体光刻的研究表明我们的框架能够扩大偏差模型的范围和对增材制造系统的全面理解。
Shape deviation models constitute an important component in quality control for additive manufacturing (AM) systems. However, specified models have a limited scope of application across the vast spectrum of processes in a system that are characterized by different settings of process variables, including lurking variables. We develop a new effect equivalence framework and Bayesian method that enables deviation model transfer across processes in an AM system with limited experimental runs. Model transfer is performed via inference on the equivalent effects of lurking variables in terms of an observed factor whose effect has been modeled under a previously learned process. Studies on stereolithography illustrate the ability of our framework to broaden both the scope of deviation models and the comprehensive understanding of AM systems.