An optimization of on-line monitoring of simple linear and polynomial quality functions

This research aims to introduce a number of contributions for enhancing the statistical performance of some of Phase II linear and polynomial profile monitoring techniques. For linear profiles the idea of variable sampling size (VSS) and variable sampling interval (VSI) have been extended from multivariate control charts to the profile monitoring framework to enhance the power of the traditional T^2 chart in detecting shifts in linear quality models. Finding the optimal settings of the proposed schemes has been formulated as an optimization problem solved by using a Genetic Approach (GA). Here the average time to signal (ATS) and the average run length (ARL) are regarded as the objective functions, and ATS and ARL approximations, based on Markov Chain Principals, are extended and modified to capture the special structure of the profile monitoring. Furthermore,the performances of the proposed control schemes are compared with their fixed sampling counterparts for different shift levels in the parameters. The extensive comparison studies reveal the potentials of the proposed schemes in enhancing the performance of T^2 control chart when a process yields a simple linear profile. For polynomial profiles, where the linear regression model is not sufficient, the relationship between the parameters of the original and orthogonal polynomial quality profiles is considered and utilized to enhance the power of the orthogonal polynomial method (EWMA4). The problem of finding the optimal set of explanatory variable minimizing the average run length is described by a mathematical model and solved using the Genetic Approach. In the case that the shift in the second or the third parameter is the only shift of interest, the simulation results show a significant reduction in the mean of the run length distribution of the EWMA4 technique

Feedback control of gas metal arc braze-welding using thermal signals

textIn serial manufacturing processes, localized energy sources (e.g. plasma cutters, arc welders or water jets) induce material geometry transformations that yield a desired product. Simple parameter control of these energy sources does not necessarily ensure an optimal or successful part because of disturbances in the manufacturing process (material and temperature variations, etc). Currently, control in manufacturing is based on statistical process control where large databases for the manufacturing of a fixed process are available and have been compiled over several manufacturing runs. In the absence of a statistical database, and with the increased need for improved monitoring and throughput, there is need for active process control in manufacturing. In this work, Gas Metal Arc Braze-Welding (GMABW) will serve as a test-bed for the implementation of model predictive control (MPC) for a serial manufacturing process. This dissertation investigates the integration of real time modeling of the temperature field with control algorithms to control the evolving temperature field in the ix braze-welded base metal. Fundamental problems involving MPC that are addressed are modeling techniques to calculate temperature fields with reduced computational requirements and control algorithms that utilize the thermal models directly to inform the controller. The dissertation first outlines and compares analytical and computational thermal models and comparison with experimental data are obtained. A thermal model based on a metamodeling approach is used as the plant model for a classical control system and control parameters are found. Various techniques for dealing with signal noise encountered during experimentation are investigated. A proportional controller is implemented in the experimental setup that applies feedback control of the braze –welding process using thermal signals. A novel approach to MPC is explored by using a metamodel as the plant model for the braze-welding process and having the temperature trajectory dictated by the metamodel in the steady state region of the weld. Lastly, future work and extensions of this research are outlined.Mechanical Engineerin

Integrated model-based run-to-run uniformity control for epitaxial silicon deposition.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Also available online at the MIT Theses Online homepage Includes bibliographical references (p. 241-247).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Semiconductor fabrication facilities require an increasingly expensive and integrated set of processes. The bounds on efficiency and repeatability for each process step continue to tighten under the pressure of economic forces and product performance requirements. This thesis addresses these issues and describes the concept of an "Equipment Cell," which integrates sensors and data processing software around an individual piece of semiconductor equipment. Distributed object technology based on open standards is specified and utilized for software modules that analyze and improve semiconductor equipment processing capabilities. A testbed system for integrated, model-based, run-to-run control of epitaxial silicon (epi) film deposition is developed, incorporating a cluster tool with a single-wafer epi deposition chamber, an in-line epi film thickness measurement tool, and off-line thickness and resistivity measurement systems. Automated single-input-single-output, run-to-run control of epi thickness is first demonstrated. An advanced, multi-objective controller is then developed (using distributed object technology) to provide simultaneous epi thickness control on a run-to-run basis using the in-line sensor, as well as combined thickness and resistivity uniformity control on a lot-to-lot basis using off-line thickness and resistivity sensors.(cont.) Control strategies are introduced for performing combined run-to-run and lot-to-lot control, based on the availability of measurements. Also discussed are issues involved with using multiple site measurements of multiple film characteristics, as well as the use of time-based inputs and rate-based models. Such techniques are widely applicable for many semiconductor processing steps.by Aaron Elwood Gower-Hall.Ph.D

A study of advanced control charts for complex time-between-events data

Ph.DDOCTOR OF PHILOSOPH

Improvement of the demand forecasting methods for vehicle parts at an international automotive company.

This study aims to improve the forecasting accuracy for the monthly material flows of an area forwarding based inbound logistics network for an international automotive company. Due to human errors, short-term changes in material requirements or data bases desynchronization the Material Requirement Planning (MRP) cannot be directly derived from the Master Production Schedule (MPS). Therefore, the inbound logistics flows are forecast. The current research extends the forecasting methods¿ scope already applied by the company namely, Naïve, ARIMA, Neural Networks, Exponential Smoothing and Ensemble Forecast (an average of the first four methods) by allowing the implementation of three new algorithms: The Prophet Algorithm, the Vector Autoregressive (Multivariate Time Series) and Automated Simple Moving Average, and two new data cleaning methods: Automated Outlier Detection and Linear Interpolation. All the methods are structured in a software using the programming language R. The results show that as of April 2018, 80.1% of all material flows have a Mean Absolute Percentage Error (MAPE) of less than or equal to 20%, in comparison with the 58.6% of all material flows which had the same behavior in the original software in February 2018. Furthermore, the three new algorithms represent now 29% of all forecasts. All the analysis realized in this research were made with actual data from the company, and the upgraded software was approved by the logistics analysts to make all future material flow forecasts.PregradoINGENIERO(A) EN INDUSTRIA

Contributions to statistical methods of process monitoring and adjustment

Ph.DDOCTOR OF PHILOSOPH

Carta de controle EWMA aplicada em uma empresa do setor alimentício: EWMA control chart applied in a company of the food sector

A satisfação dos clientes sobre a qualidade dos produtos e serviços interfere na posição de mercado da empresa, podendo até gerar vantagens competitivas para sua sobrevivência e ascensão. O objetivo deste artigo é discutir, por meio de um estudo de caso, a utilização da carta de controle EWMA (Exponentially Weighted Moving Average), mostrando situações reais em uma indústria do setor alimentício, onde a variável Peso (g) foi estudada por meio de cartas de controle EWMA. As cartas foram refeitas, comunicando ao setor responsável e retirando os pontos fora de controle, até chegar a um processo estatisticamente estável. Conclui-se que apesar da obtenção de uma carta sob controle estatístico, ainda existem causas especiais influenciando a variabilidade do processo. Para o controle da variabilidade é necessário continuar analisando amostras periódicas e inserir programas de qualidade a fim de estabelecer comprometimento para com a melhoria contínua

A DATA ANALYTICAL FRAMEWORK FOR IMPROVING REAL-TIME, DECISION SUPPORT SYSTEMS IN HEALTHCARE

In this dissertation we develop a framework that combines data mining, statistics and operations research methods for improving real-time decision support systems in healthcare. Our approach consists of three main concepts: data gathering and preprocessing, modeling, and deployment. We introduce the notion of offline and semi-offline modeling to differentiate between models that are based on known baseline behavior and those based on a baseline with missing information. We apply and illustrate the framework in the context of two important healthcare contexts: biosurveillance and kidney allocation. In the biosurveillance context, we address the problem of early detection of disease outbreaks. We discuss integer programming-based univariate monitoring and statistical and operations research-based multivariate monitoring approaches. We assess method performance on authentic biosurveillance data. In the kidney allocation context, we present a two-phase model that combines an integer programming-based learning phase and a data-analytical based real-time phase. We examine and evaluate our method on the current Organ Procurement and Transplantation Network (OPTN) waiting list. In both contexts, we show that our framework produces significant improvements over existing methods