Product quality control method based on process network model and machine learning algorithm

Abstract

The invention discloses a product quality control method based on a process network model and a machine learning algorithm. According to the method, product quality control is realized in a mode of fusing a product process complex network and a machine learning algorithm, namely, a quality transmission complex network is established based on a product process mechanism, a machine learning XGBoost model is established based on the quality transmission network and sample data and trained, and an SHAP algorithm model is established to analyze an XGBoost training result. Key process parameters influencing quality are quantitatively mined, and the linkage effect among the process parameters is accurately calculated. The method has the advantages that a product process mechanism and a big data analysis method are effectively combined, so that the problems of high dimension, strong nonlinearity and non-uniform sample distribution of industrial actual production data, which cannot be solved by a common statistical analysis method, are effectively solved, and the defect that a traditional product quality control method carries out qualitative description or only carries out product quality control for a single independent factor is overcome; and the method can comprehensively and quantitatively analyze the influence of complex process factors in the whole process of product processing on the final product quality and accurately calculate the linkage influence among the process factors to form a quality identification model based on process mechanism and data dual drive, and is large in processing data volume, high in speed and accurate in evaluation result.

Description

technical field [0001] The invention is a product quality control method based on a process network model and a machine learning algorithm, and the method relates to product quality control and processing technology optimization. Background technique [0002] Traditional quality management is based on concepts such as quality inspection, statistical process control (SPC), and defective rate. It often relies on manual measurement tools and mathematical statistical analysis. It is greatly influenced by experience and the analysis object factors are relatively limited. It cannot target the entire process of the product. Various parameters are comprehensively analyzed, and the linkage effect between parameters cannot be found, and it cannot adapt to complex product production processes. The manufacturing process of many products is complex, and process mechanism parameters such as environment, materials, and processes are closely related to product quality results. At the same t...

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Problems solved by technology

[0016] The above product quality control methods are mostly based on statistical analysis methods, and are often analyzed and controlled for a single quality index or process factor. For products with multiple processes or complex process processes, it is impossible to comprehensively analyze the reasons that affect product quality from the overall perspective of the entire manufacturing process. , it is impossible to explore the linkage influence between process factors, which is very important and necessary for product quality control

At the same time, the data processing capacity of statistical analysis methods is generally small, and the problems of high dimensionality, strong nonlinearity, and uneven sample distribution of real product industrial big data cannot be solved

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