A QCs-linkage model based framework of quality characteristic variation analysis and control was proposed. Firstly the definition of quality characteristic variation propagation was clarified and the concept of linkage was introduced, based on which, QCs-linkage network and QCs-linkage matrix were constructed. Then the framework of variation propagation analysis and control consisting of 4 phases of quality characteristic variation identification, variation source-tracing, variation mitigation and variation prevention was given. Some typical variation propagation effects were concluded and the variation source-tracing process was studied, in which the algorithms of variation propagation probabilities in different linkage combination patters were discussed. In the following, the variation mitigation process and mitigation methods were discussed in detail and the variation mitigation principle based on minimum mitigation risk was given. A case of internal combustion engine study presented in this paper indicated that the framework was effective for quality control and improvement in product development.

Product quality is an important part of enterprise competitiveness. Product processing is the key process of quality formation. In smart factories, the improvement of data acquisition and processing capability provides a basis for data-based quality control. In order to reduce the occurrence of product quality problems, we abstracted the product processing process as a data processing unit, abstracted the process of changing the product quality state as a process of the processing quality characteristics data, divided the measured value of quality characteristics into three states according to the fluctuation of the measured value of product quality characteristics, and then the classification model of process equipment parameters was established. The experimental results show that the error rate of the real-time dynamic prediction of quality characteristics based on equipment parameters was acceptable, and its prediction could be used as a reference in real production. The research could be applied in product quality prediction, production process simulation, digital twin and other fields.