In the manufacturing industries, the quality of the products is ensured through reliability assessment using standards and user requirements. However, this assessment is traditionally expensive and time-consuming. Transforming the current practice with integrating machine learning approaches will bring in competitive benefits to the industries. This talk will evaluate the opportunity of this integration and gain insights from the predictive analytics. Case studies will be shared using electronics products, which data from early testing weeks are fed as predictors for the product functionality in the future weeks. This concept can be utilized to predict the test outcome of either pass/fail or the time to failure. It has the capability to optimize the test resources and facilitate early decision-making using prediction results, thus enabling faster time-to-market.
Learning Objectives:
Evaluate the prediction model to transform the current practices of time-consuming and costly reliability tests.
Demonstrate the concept and leverage opportunities to utilize machine learning approaches in assessing product reliability and optimize the qualification duration.
