Statistical Pattern Recognition for Manufacturing Quality Control

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Statistical process control (SPC) plays an important role in controlling a product's quality. SPC gives a signal**when the process mean or the product variability moves away from the permissible range. Such zone tests, also**known as run tests, are widely accepted classical techniques for recognition of the abnormal process behaviour.**However, operating within the specification limits does not necessarily signify a stable process. Run tests have**proven effective in indicating out-of-control situations, their effectiveness in interpreting process data,**however, is disputed.**The prime goal of this proposal is to have such custom inspection applications trained to recognize samples of**perfect and defective items. This is also the strategic objective of the Sightline company for increasing its**market competitiveness. Once the computational model reaches a sufficient level of accuracy, it would be**deemed ready to start inspecting and be deployed in a production environment. Upon receiving new data, the**model would use statistical pattern recognition alongside machine learning algorithms to detect any variance in**the product, such as scratches, contaminants, or improper dimensions.**If the product passes or a defect is detected, the operator would be notified immediately. As the model receives**high quality inspection data over time, it can be used for predictive analysis which would allow one to**understand when, where, how, and why defects are entering the process, thereby providing ample opportunity**to take preemptive and proactive measures. The ultimate objective of this project is to develop machine**learning models which can automatically learn complex structures and trends in large image and sensor**datasets collected from manufacturing processes. Using these learned features, we can identify known defects**in newly manufactured products and, potentially, discover new ones.