Statistical Adjustment for Short-Run Manufacturing: Parametric Optimization, Robustness Analysis, and Ensemble Control Using Gibbs Sampling

短期制造的统计调整:参数优化、鲁棒性分析和使用吉布斯抽样的集成控制

基本信息

项目摘要

A problem located at the foundations of the Statistical Process Control (SPC) field is how to adjust a manufacturing process that is suspected to be operating in a malfunctioning mode. Rapidly adjusting a manufacturing process when the setup operation is defective is particularly important. The object of this research is to develop optimal sequential adjustment methods for the setup and within-run control problems. The setup adjustment problem was first analyzed by F. Grubbs who derived a simple sequential scheme. A Bayesian formulation for process adjustment will be developed based on Kalman filters. The formulation unifies several adjustment rules including Grubbs' scheme, Stochastic Approximation, and classical control methods such as Linear Quadratic Gaussian (LQG) control. The proposed work will follow two main research thrusts: a) parametric optimization and robustness assessment of existing adjustment rules; b) development of a new ensemble-optimal adjustment rule. It is proposed to utilize Markov Chain Monte Carlo techniques, and in particular, Gibbs Sampling, applied to the problem of estimating the mean of a sequentially-adjusted process that experiences errors in the setups according to some stable distribution. The main outcome of this research will be a new set of process adjustment tools that will provide efficient setup and within-run control in short-run manufacturing processes. This formulation has the major advantage that after a few runs or lots are produced, it allows to start adjusting a new lot prior to obtaining the first measurement in the lot. This is clearly an advantage for the type of flexible, short-run manufacturing systems this research is expected to benefit. Use of Penn State's FAME manufacturing laboratory will provide a realistic testbed for the techniques developed in this project. Collaboration with industrial researchers (Eli Lilly and SAS Institute Inc.) will provide expertise in the real-life application of the techniques developed in this research and guidance about software implementation. To allow technology transfer, software tools will be written and will be freely distributed at Penn State's Applied Statistics laboratory web site.
统计过程控制(SPC)领域的一个基础问题是如何调整疑似在故障模式下运行的制造过程。当设置操作有缺陷时快速调整制造工艺尤为重要。本研究的目的是为设置和运行内控制问题开发最佳顺序调整方法。设置调整问题首先由 F. Grubbs 分析,他导出了一个简单的顺序方案。将基于卡尔曼滤波器开发用于过程调整的贝叶斯公式。该公式统一了多种调整规则,包括格鲁布斯方案、随机逼近和线性二次高斯 (LQG) 控制等经典控制方法。拟议的工作将遵循两个主要研究重点:a)现有调整规则的参数优化和鲁棒性评估; b) 开发新的集合最优调整规则。建议利用马尔可夫链蒙特卡罗技术,特别是吉布斯采样,应用于估计顺序调整过程的平均值的问题,该过程在设置中根据某些稳定分布遇到错误。这项研究的主要成果将是一套新的工艺调整工具,它将在短期制造工艺中提供高效的设置和运行内控制。该配方的主要优点是,在生产了几次运行或批次后,它允许在获得该批次中的第一个测量值之前开始调整新批次。对于本研究预计受益的灵活、短期制造系统类型来说,这显然是一个优势。宾夕法尼亚州立大学 FAME 制造实验室的使用将为该项目开发的技术提供一个现实的测试平台。与工业研究人员(Eli Lilly 和 SAS Institute Inc.)的合作将为本研究中开发的技术的实际应用提供专业知识,并提供有关软件实施的指导。为了实现技术转让,将编写软件工具并将其在宾夕法尼亚州立大学应用统计实验室网站上免费分发。

项目成果

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Enrique Del Castillo其他文献

D-optimal design of artifacts used in-machine software error compensation
使用机内软件误差补偿的工件的 D 优化设计

Enrique Del Castillo的其他文献

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{{ truncateString('Enrique Del Castillo', 18)}}的其他基金

Deep Intrinsic Learning for On-line Process Control of Manufacturing Manifold Data
用于制造流形数据在线过程控制的深度内在学习
  • 批准号:
    2121625
  • 财政年份:
    2022
  • 资助金额:
    $ 19.35万
  • 项目类别:
    Standard Grant
High Dimensional Statistical Inference in Flexible Response Surface Models for Product Formulation
产品配方灵活响应面模型中的高维统计推断
  • 批准号:
    1634878
  • 财政年份:
    2016
  • 资助金额:
    $ 19.35万
  • 项目类别:
    Standard Grant
Collaborative Research: Active Statistical Learning: Ensembles, Manifolds, and Optimal Experimental Design
协作研究:主动统计学习:集成、流形和最优实验设计
  • 批准号:
    1537987
  • 财政年份:
    2015
  • 资助金额:
    $ 19.35万
  • 项目类别:
    Standard Grant
On-line Profile-to-Profile Process Adjustment for Robust Parameter Design Scenarios
针对稳健参数设计方案的在线剖面到剖面工艺调整
  • 批准号:
    0825786
  • 财政年份:
    2008
  • 资助金额:
    $ 19.35万
  • 项目类别:
    Standard Grant
Optimization Techniques in Response Surface Methodology for Quality Improvement
用于质量改进的响应面方法中的优化技术
  • 批准号:
    9988563
  • 财政年份:
    2000
  • 资助金额:
    $ 19.35万
  • 项目类别:
    Standard Grant
CAREER: Multivariate Quality Control of Semiconductor Manufacturing Processes via Adaptive Optimizing Controllers
职业:通过自适应优化控制器对半导体制造工艺进行多元质量控制
  • 批准号:
    9996031
  • 财政年份:
    1998
  • 资助金额:
    $ 19.35万
  • 项目类别:
    Standard Grant
CAREER: Multivariate Quality Control of Semiconductor Manufacturing Processes via Adaptive Optimizing Controllers
职业:通过自适应优化控制器对半导体制造工艺进行多元质量控制
  • 批准号:
    9623669
  • 财政年份:
    1996
  • 资助金额:
    $ 19.35万
  • 项目类别:
    Standard Grant
U.S. - Germany Cooperative Research: Integration of Statistical and Automatic Control Techniques for Economic Quality Control
美德合作研究:统计与自动控制技术的整合用于经济质量控制
  • 批准号:
    9513444
  • 财政年份:
    1996
  • 资助金额:
    $ 19.35万
  • 项目类别:
    Standard Grant

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