This invention discloses a
big data-based method for
quality control in the production of supercritical foamed materials, belonging to the field of
manufacturing quality control technology. This
big data-based method acquires time-
series data and secondary mapping data on
barrel temperature deviation, melt pressure fluctuation, and
screw speed fluctuation during continuous
extrusion foaming. Based on these two types of data, it determines the correspondence between the
carbon dioxide dissolution rate distribution and the
residence time distribution, and generates
concentration gradient field characterization data. After fusion, it obtains fused
feature data, inputs it into a degradation trend identification model, outputs the degradation trend of
dissolution uniformity inside the
barrel, and determines the risk of finished product quality defects. This invention simultaneously collects
process time-
series data and secondary mapping data reflecting the internal state of the melt, generates
concentration gradient field characterization data, and fuses and models it to identify the degradation trend and
risk status of
dissolution uniformity. It locates the
data source deviation before defects form, providing a traceable decision-making basis for
production quality control.