Process characterization and optimization based on censored data from highly fractionated experiments

Censored data resulting from life-test of durable products, coupled with complicated structures of screening experiments, makes process characterization very difficult. Existing methods can be inadequate for modeling such data because important effects and factor levels might be identified wrongly. This article presents an expectation-modeling-maximization (EMM) algorithm, where censored data are imputed as pseudo-complete samples and a forward regression is used to compare all main effects and 2-factor interactions for process characterization. Then, the best combination of controllable variables is determined in order to optimize predictions from the final model. A sensitivity study of the selected models, with changes of imputation and parameter estimation methods, shows the importance of using appropriate models and estimation methods in EMM. The author's analysis of the Specht (1985) heat-exchanger life-test data indicates that E, EG, EH in the wall data and A, K, D, DJ in the corner data are the dominating factors. However, in finding the best process recipe, one might use a model with a few additional terms, which leads to more accurate predictions for better process optimization.<
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