Abstract
In the cable insulation crosslinking production process, the process quality is determined by multiple processes and multiple related process variables, and there are complex autocorrelation and cross-correlation among these process variables. In this paper, different process variables are studied. By implementing statistical process control on a univariate, single value control chart (I) and moving range control chart () are drawn, and process capability CP is analyzed to find variables with weak process capability for improvement. At the same time, based on the background of the continuous development of the Internet of Things and big data technology, this paper evaluates the complex autocorrelation and cross-correlation between the overall process variables, adopts the multivariate statistical process control technology, and combines the multivariate HOTELLING T2 control chart and MEWMA control chart to reflect the statistical characteristics of multiple variables, thus avoiding misjudgment or omission of process quality monitoring model. By studying the setting of cable production process parameters and quality standards, combined with the application of complete multivariate statistical process control (MSPC) around the evaluation results, process, and technical requirements, this research can provide effective guidance for the cable insulation crosslinking production process
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