Holistic modeling and analysis of multistage manufacturing processes with sparse effective inputs and mixed profile outputs
Holistic modeling and analysis of multistage manufacturing processes with sparse effective inputs and mixed profile outputs
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DOI：
10.1080/24725854.2020.1786197
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发表时间：
2020-08
影响因子：
2.6
通讯作者：
Andi Wang;Jianjun Shi
中科院分区：
文献类型：
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作者：
Andi Wang;Jianjun Shi
摘要在多阶段制造过程中，多个类型的传感器被部署来收集每个制造阶段之后的中间产品质量测量。本研究旨在模拟混合档案的这些品质输出与稀疏有效流程输入之间的关系。我们提出了一个基于四个过程特征的分析框架：(I)每一次输入只影响同一阶段和后续阶段的输出；(Ii)所有阶段的输出都是光滑的函数曲线或图像；(Iii)只有少量的输入影响输出；以及(Iv)输入引起输出的几种变化模式。我们提出了一个优化问题，该问题同时估计了过程输入对整个制造过程的输出的影响。针对这一问题，提出了一种ADMM一致性算法。该算法具有高度并行化的特点，能够处理从多个阶段获得的大量混合类型数据。仿真实验验证了该算法在估计、选择有效输入、识别各阶段变化模式等方面的能力。
Abstract In a Multistage Manufacturing Process (MMP), multiple types of sensors are deployed to collect intermediate product quality measurements after each stage of manufacturing. This study aims at modeling the relationship between these quality outputs of mixed profiles and sparse effective process inputs. We propose an analytical framework based on four process characteristics: (i) every input only affects the outputs of the same and the later stages; (ii) the outputs from all stages are smooth functional curves or images; (iii) only a small number of inputs influence the outputs; and (iv) the inputs cause a few variation patterns on the outputs. We formulate an optimization problem that simultaneously estimates the effects of process inputs on the outputs across the entire MMP. An ADMM consensus algorithm is developed to solve this problem. This algorithm is highly parallelizable and can handle a large amount of data of mixed types obtained from multiple stages. The ability of this algorithm in estimations, selecting effective inputs, and identifying the variation patterns of each stage is validated with simulation experiments.