Auto-body assembly process fault diagnosis based on a dynamic variation modeling approach

Purpose – This paper aims to provide a new dynamic modeling approach for root cause detection of the auto-body assembly variation. Design/methodology/approach – The dynamic characteristics, such as fixture element wear and quality of incoming parts, are considered in assembly variation modeling with the dynamic Bayesian network. Based on the network structure mapping, the parameter learning of different types of nodes is conducted by integrating process knowledge and Monte Carlo simulation. The inference was that both the measurement data and maintenance actions are evidence for the improvement of diagnosis accuracy. Findings – The proposed assembly variation model which has incorporated dynamic manufacturing features could be used to detect multiple process faults effectively. Originality/value – A dynamic variation modeling method is proposed. This method could be used to provide more accurate diagnosis results and preventive maintenance guidelines for the assembly process.

The Assembly Variation Modeling for the Rear Casing Wax Part Based on Polar Coordinate

Rear casing is a key part of the aeroplane engine. Its dimensional precision is significant to the quality of the aeroplane engine. In the rear casing manufacturing process, the assembly variation of its corresponding wax dramatically affects the final dimensions. In this paper, a polar-coordinate based model is proposed to calculate the assembly variation of ring-shaped rear casing wax part. It avoids the variation caused by the coupling relationship between Cartesian coordinate systems and locating position. We also compare the polar-coordinate based model with the ordinary one in practical application. The results show that the polar-coordinate based model can simplify the calculating process and improve the computational accuracy for the assembly variation analysis of the ring-shaped part.

Research on Assembly Variation Modeling of Aircraft Weakly-Rigid Structures

A large number of weakly rigid parts are used in the progress of aircraft assembly, while it’s deviation will affect the quality of the product finally. The assembly variation model of aircraft weakly rigid parts based on influence coefficient method are constructed for controlling assembly accuracy effectively. Based on the linear elastic hypothesis of small deformation, the model carry out the mechanical analysis of aircraft weakly rigid parts in the typical process of positioning, clamping, jointing and spring-back. Influence coefficient matrix of the key features affected by parts manufacturing variation and fixture variation is calculated through the Finite Element method and Monte Carlo simulation, then the assembly variation model is constructed. Finally, based on the above model, an engineering example of assembly variation prediction of aircraft panels was given.

Variation Analysis of Sheet Metal Assemblies with Fixture Configuration

The dimensional quality of auto-body relates to the whole external appearance and wind noise, the effect of closing the door and even driving smoothness of vehicles. The deviation propagation can be analyzed through the method of influence coefficient (MIC). However, fixture deviation usually impacts on the assembly variation than part variation. A variation analysis modeling with fixture configuration is presented to improve the current variation analysis efficiency of sheet metal parts. The variation change process of part, fixture and assembly was analyzed by researching the locating and assembly process of sheet metal parts. The linear relationships among variations of parts, fixtures and assemblies are established with two locating principles of “N-2-1” and “3-2-1”. Moreover, in accordance with the different release modes of the fixture locating points after the assembly, the assembly variation modeling is established in the two modes of over-constrained release and full release. Finally, a case of sheet metal assembly is illustrated to show the effectiveness of the assembly variation analysis modeling.