Intelligent Dimensional Big Data Closed-Loop Quality Solution

Abstract

The quality data in manufacturing plant is lack of deep mining, relying more on artificial experience and existing quality analysis tools to do data processing. While virtual analysis is more and more used in the stage of engineering development, which is limited by assumptions and cannot be directly used for on-site production guidance and quality analysis. It has been a long time since the gap between theoretical analysis and solution of practical issues existed. Data barrier and the difficulty to form a closed loop between the theoretical and practical analysis have seriously restricted speeding up the product development and launch period. In this paper, a creative quality management solution based on dimensional engineering methodology is put forward, which forms a digital closed loop of upstream and downstream data flows. It can realize the digital interaction of VA theoretical model, measurement points and on-site measured data in an independent dimensional analysis model. Artificial intelligence algorithm is also used to train the model and generate the specific impact factors and sensitivity contribution of on-site quality problems, which can support the data analysis, risk prediction more quickly and accurately.