Research on assembly quality adaptive control system for complex mechanical products assembly process under uncertainty

• An assembly quality adaptive control system is proposed, developed and applied.
• The system is utilized to reach monitoring, data capture, tracing, and analysis.
• Assembly quality control threshold optimization is built based on BP neural network.
• Assembly performance prediction is built based on BP neural network.
• The assembly precision, stability and efficiency are improved by applying the system.

To improve the products’ assembly precision, stability and efficiency, an assembly quality adaptive control system (A_QACS) is proposed for complex mechanical products under uncertainty. Firstly, the characteristics of the complex mechanical products assembly process are analyzed, and the definition and overall framework of assembly quality adaptive control (AQAC) are elaborated. Then, the operation logic of AQAC is researched based on the definition of six units. And the structure of A_QACS system is presented for complex mechanical products. Under the structure, the key technologies of the A_QACS system are described respectively, including assembly resource identification, the key source of monitoring, the data acquisition technology and assembly quality adaptive optimization technology. The monitoring of assembly process, real-time data acquisition, assembly quality adaptive control as well as closed-loop intelligent control integrated with comprehensive quality information tracing and knowledge accumulation are realized. It provides support for timely optimizing the work instructions and control strategies for operators and equipment. Finally, an example of the selection of main bearing and the assembly of crankshaft is given. The system is demonstrated feasibility and effectiveness by analyzing variance, assembly qualification rate and rework rate. And the proposed system provides a practical and efficient technology for assembly process quality control.