Improving Monitoring and Diagnosis for Process Control using Independent Component Analysis

Statistical Process Control (SPC) is the general field concerned with monitoring the operation and performance of systems. SPC consists of a collection of techniques for characterizing the operation of a system using a probability distribution consistent with the system\u27s inputs and outputs. Classical SPC monitors a single variable to characterize the operation of a single machine tool or process step using tools such as Shewart charts. The traditional approach works well for simple small to medium size processes. For more complex processes a number of multivariate SPC techniques have been developed in recent decades. These advanced methods suffer from several disadvantages compared to univariate techniques: they tend to be statistically less powerful, and they tend to complicate process diagnosis when a disturbance is detected. This research introduces a general method for simplifying multivariate process monitoring in such a manner as to allow the use of traditional SPC tools while facilitating process diagnosis. Latent variable representations of complex processes are developed which directly relate disturbances with process steps or segments. The method models disturbances in the process rather than the process itself. The basic tool used is Independent Component Analysis (ICA). The methodology is illustrated on the problem of monitoring Electrical Test (E-Test) data from a semiconductor manufacturing process. Development and production data from a working semiconductor plant are used to estimate a factor model that is then used to develop univariate control charts for particular types of process disturbances. Detection and false alarm rates for data with known disturbances are given. The charts correctly detect and classify all the disturbance cases with a very low false alarm rate. A secondary contribution is the introduction of a method for performing an ICA like analysis using possibilistic data instead of probabilistic data. This technique extends the general ICA framework to apply to a broader range of uncertainty types. Further development of this technique could lead to the capability to use extremely sparse data to estimate ICA process models

Machine Learning Applications for Thermal Manufacturing Processes

This thesis introduces a novel approach for the extraction of physically meaningful thermal component time series during the manufacturing of casting parts. I treat their extraction as Blind Source Separation (BSS) problem by exploiting process-related prior knowledge. The proposed method arranges temperature time series into a data matrix, which is then decomposed by Non-negative Matrix Factorization (NMF). The latter is guided by a knowledge-based strategy, which initializes the NMF component matrix with time curves designed according to basic physical processes. It is shown how to extract components linked to physical phenomena that typically occur during production and cannot be monitored directly. The proposed methods are applied to real world data, collected in a foundry during the series production of casting parts for the automobile industry