Abstract:

In continuous production scenarios of process manufacturing, product homogenization directly determines the stability and satisfaction of user experience. Its core value is not only reflected in the consistency of product performance, but also of great significance for improving production efficiency and controlling costs. However, various process parameters in process manufacturing are mutually coupled with complex correlations, which leads to high prediction delay and low accuracy in existing models. To address the above problems, a multi-stage, multi-step prediction model for process-manufacturing quality indicators based on grey relational analysis (GRA) and a Transformer-MC-CF was proposed. Firstly, to mitigate multivariate parameter redundancy in process manufacturing, GRA was used to quantify correlation strengths between input parameters of each process and quality indicators, to identify key influencing parameters, and to eliminate redundant variables with low correlation. This approach reduced the model’s computational complexity and avoided interference from irrelevant information during feature learning. Secondly, a multi-stage, multi-step prediction model based on the Transformer was constructed. To address insufficient local feature fusion caused by mutual coupling of multiple factors, a multi-channel receptive field block (MC-RFB) was designed. By combining multi-scale convolution and dilated convolution, the block simultaneously captured local detailed features and long-term temporal dependencies. Considering the sequential coherence of process manufacturing, a correlation feature fusion (CF) module was proposed; it adopted an adaptive-weighting strategy to fuse latent features of upstream and downstream processes, thereby effectively exploring the indirect inter-process influence relationships. Experimental results showed that the application of GRA effectively improved the prediction performance of the model. Compared with traditional models, the proposed Transformer-MC-CF model outperformed others by more than 4.6% in process-manufacturing quality prediction scenarios, and the average fitting degree for multi-process, multi-step prediction reached 97.5%. This model provided technical support for improving prediction and regulation capabilities of process-manufacturing quality indicators and for achieving homogenized production.

Key words: process manufacturing, quality prediction, grey relational analysis, convolutional neural network, Transformer