Study On Key Techniques Of The Assembly Quality Assurance Of A Turbofan Engine

Assembly is not only the last process of a turbofan engine manufacturing, but also is one of the most important aspects of the manufacture. Assembly directly affects the final quality and performance of a turbofan engine. Therefore, it’s of great significance to study how to ensure the quality of turbofan engine assembly. In this paper, the key technologies of ensuring assembly quality of a turbofan engine are studied in according with the research projects of Air Force equipment Department.Firstly, some useful methods of assembly process modeling are analyzed and the hybrid model of hierarchical model and the relational mode for turbofan engine assembly process is proposed in this paper. Then, based on the assembly model, a quality house of key assembly quality assurance technology and a quality house of assembly process quality assurance are established using Quality Function Deployment principle and quality house tools are applied to divide importance degree of assembly assurance technologies and assembly processes. For the need to focus on the control of the assembly process, this paper presents the use of Z control chart instead of the traditional Shewhart control chart to control process quality, and makes process capability analysis which can be assessed through process capability index according to statistical data of the key processes. Z control charts suits for turbofan engines that are produced in small quantities of non-line process control and this method requires for a small number of samples, has high prediction accuracy on stability of the assembly quality, high control flexibility and a wide range. Finally, this paper adopts the adaptive genetic algorithm for intelligent matching of engine parts, and calculates matching processes which use the highest precision and the minimal quality loss respectively to optimize the objective applying to the adaptive genetic algorithm and basic genetic algorithm. Through comparison of the calculation, we can see that matching calculation by the use of adaptive genetic algorithm has higher accuracy, faster convergence, and it overcomes the "mature" defects of traditional genetic algorithm.