Research on the Analysis Method of Engine Assembly Quality Based on Data Driven

In the context of the country’s vigorous development of the industrial Internet, the role of big data has become increasingly prominent. Through the mining and analysis of the value of industrial big data, the efficiency and quality of industrial production will be effectively improved. However, the current research lacks direct quantitative research on the correlation between bolt tightening position data and engine quality. Based on the data of a certain production line of Weichai, the SimpleKMeans clustering and Apriori correlation analysis model for the assembly process data and product quality of a certain type of diesel engine is constructed based on the Weka tool. The mining results of the association rule between the bolt tightening data of the eight stations and the cylinder head vibration peak data show that the cylinder head main bolts, cylinder head auxiliary bolts, camshaft gear bolts and connecting rod bolts are closely related to the cylinder head vibration data, and the confidence levels are respectively 0.78, 0.70, 0.63, 0.52. Therefore, when the engine vibration is abnormal, the tightening data of the cylinder head main bolts, cylinder head auxiliary bolts, camshaft gear bolts and connecting rod bolts are detected first to effectively improve the quality and efficiency of the assembly process.