Abstract
Aiming at the quality control problems in the discrete manufacturing process of large and superlarge equipment, which cannot meet the urgent needs of production, a quality control method based on big data and pattern recognition is proposed. A large amount of data is collected through the test equipment developed in the discrete manufacturing process; a database of typical working conditions and an information tracking system relying on the cloud platform were formed. The working conditions were divided by the principal component analysis (PCA) and improved K-means algorithm. The Markov prediction model predicts the working conditions, recognizes the pattern with typical working conditions, regulates the processing parameters, and achieves quality control. Taking the quality control of the hydraulic cylinder manufacturing process above 5 m as an example for experimental verification, the experiments indicated that working conditions can be automatically identified and classified through pattern recognition technology. The process capability index Cpk increased from 0.6 to 1, which proved the effectiveness of quality control and the improvement of processing capabilities.
Highlights
Large and superlarge equipment is an indispensable product for important manufacturing industries such as construction machinery, aerospace, and steel
It is of great significance to improve the manufacturing quality control of large and superlarge equipment
Wu [5] proposed a quality control method for complex product assembly process based on digital twin technology
Summary
Large and superlarge equipment is an indispensable product for important manufacturing industries such as construction machinery, aerospace, and steel. Large and superlarge equipment is a typical discrete manufacturing process with multiple varieties and small batches. It is of great significance to improve the manufacturing quality control of large and superlarge equipment. Jiang XY [2] used similar manufacturing theory, statistical process control, neural network, and other theories and technologies to establish an intelligent process quality control system. Zheng [4] proposed a Bayesian network and big data analysis integration method for manufacturing process quality analysis and control. Wu [5] proposed a quality control method for complex product assembly process based on digital twin technology. It is of great significance to improve processing accuracy and production efficiency and save processing time and production costs
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