Research on the construction and application of knowledge graph for process quality optimization

Process quality optimization is a necessary means for enterprises to improve product quality. However, the current process quality optimization of enterprises is highly dependent on experienced senior technicians. If the experience of these people cannot be effectively converted into knowledge, the value of massive industrial data cannot be effectively obtained. It has become the main bottleneck faced by the intelligent production of enterprises. To this end, this paper proposes a process quality optimization method based on knowledge graph, which uses process quality optimization rules and artificial experience knowledge to construct a process quality optimization knowledge graph including process knowledge, product defect knowledge, and process optimization knowledge. Associate process defects and defect handling cases in the form of event clusters, combine artificial intelligence and graph algorithms, and assist process quality optimization decision-making in various ways such as knowledge search, knowledge question and answer, and knowledge recommendation.