A systematic quality-integrated diagnostic method for complex product assembly using multi-task spatial–temporal transfer learning.

The assembly process is generally considered one of the primary factors influencing the quality of complex products. Currently, most existing quality-integrated diagnostic methods for products tend to deteriorate over different processes and degrade over time. To address this issue, this paper introduces a systematic quality-integrated diagnostic method for complex product assembly processes. First, the influence factors and error sources in the complex assembly process are analyzed using the 5M1E and FAHP methodologies. Next, similarity-based multi-task clustering and dismantling using RGMM is applied to divide the assembly tasks. Finally, MMD-MSE is employed to develop a quality prediction model for the complex assembly process using a spatial–temporal transfer learning approach. Experiments were conducted on an array antenna assembly task, comparing the proposed method with conventional methods. The results show that the accuracy and PrUP of the proposed model are 97.6% and 95.2%, respectively, for the quality-integrated diagnostic of complex assembly processes, with a fluctuation in accuracy of less than 6%. The diagnostic results effectively meet expert evaluations and provide a stable, reliable, and practical solution for addressing quality fluctuations in complex production assembly processes. [ABSTRACT FROM AUTHOR]