Data Fusion Based On-Line Product Quality Evaluation for the Manufacturing Process of Ternary Cathode Material

Abstract

In the manufacturing process of ternary cathode material, batching, mixing, loading and sintering procedures play decisive roles on product quality, and data generated by these procedures are collected by the manufacturing process of ternary cathode material. However, data of the process are heterogeneous and have different properties, and sampling of key quality variables is difficult. On this basis, on-line evaluation on product quality is hard. In this paper, a data fusion based online product quality evaluation method for the ternary cathode material is proposed. Data is collected from an enterprise manufactures ternary cathode material. Then, to establish the model between the extracted data features and the key quality variables such as particle size and surface-free lithium content, a semi-supervised double-weighted probabilistic principal component regression is proposed. After that, a distance cost index is brought to cluster and grade the two quality variables, which are predicted by the semi-supervised model. Therefore, the on-line evaluation of product performance is achieved by setting rule table in accordance with production experience. Finally, based on data collected from an enterprise manufactures ternary cathode material, the proposed method is verified to be effective and accurate.