Modeling of Product Quality Based on Certain Product Measurements (2022-US-EPO-1085)Kaitlin Shorkey, Statistical Engineer, Corning Inc
This presentation highlights modeling approaches used to understand and predict product quality based on product measurements. The model development process involves four steps: data collection, model development, testing, and implementation. During data collection, parts are collected from the production line and product measurements are completed. These parts continue through the process and are subject to a quality test. These product measurements and quality test results are combined into the dataset and used for modeling. The quality test is the response and product measurements are predictors. In model development, the data is examined thoroughly to ensure it is as clean as possible. Variable clustering and stepwise regression are applied to remove highly correlated input variables and select the important variables. The final step is to apply generalized regression using log-normal distribution and the adaptive lasso estimation method. The model must have an accuracy of greater than a certain metric that is acceptable. If the model meets this criterion, it’s moved into the testing phase. This phase involves using the model under engineering control to determine how well the model predicts the product quality. Once satisfied, the model is implemented into production. The operations team receives instant feedback on how the part will perform and can adjust and tune the process in real-time. With this information, we can also deem the product acceptable or not. If rejected, the product is disposed of and doesn’t continue through the process. These predictive models identify unacceptable parts and process upsets in the upstream processes.
Data ExplorationStatistics and Modeling
