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A knowledge-based online fault detection method of the assembly process considering the relative poses of components

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Abstract

The real-time process fault detection in the multi-station assembly process is always a challenging problem for auto body manufactures. Traditionally, the fault diagnosis approaches for variation source identification are divided into two categories, i.e. the pattern matching methods and model-based estimation ones based on the collected data set. The measurements provide effective process monitoring, but the real-time process fault diagnosis in the assembly process is still difficult with the traditional diagnosis techniques, and always depends on the engineering experience in practice. Based on the assembly process knowledge, including multi-station assembly hierarchy, fixture scheme, measurement characteristics and tolerances etc. in the multi-station, a knowledge-based diagnostic methodology and procedures are proposed with the measurements of each body in white for part/component defections and faulty assembly station identification. For the station involved with defective parts/components, the sub-coordinate system of the part/component is established reflecting its position and pose in the space, and then the relative pose matrix to the “normally build” pose is calculated based on the deviations of sub-coordinates of the parts in this station. Finally, the assembly process malfunctions are determined by a proposed rule-based strategy with the relative pose matrix in real time. A simple 3 stations assembly process with 5 sheet metal parts was analyzed and compared with the traditional diagnostic method to verify the effectiveness and stability of the proposed method.

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Abbreviations

S k :

The k-th assembly station

C i, j :

The j-th part or sub-assembly in the i-th layer

P i,j,m (a, b, c):

The deviation of the j-th part in the i-th layer in the form of m, and (a, b, c) represent the directions of the positioning elements control

M ijp :

The p-th measurement characteristic of the j-th part/component that is firstly assembled in the i-th layer

(x A, y A, z A):

The coordinates of the reference point A and the suffix of the x, y, z represents the corresponding reference point

TR :

The tolerance of the RPFD

\(n_{ij}^{{\prime }}\) :

The number of unqualified key product characteristics

n ij :

The whole number of measurement characteristics on the j-th component of the i-th layer

r ij :

The failure rate on the component

r ij0 :

The threshold of the rij

\(d_{ijp}^{c}\) :

The p-th RPFD of the j-th part in the i-th layer

(x 0, y 0, z 0):

The measurement point near the four-way pin

(u, v, w):

The origin of the sub coordinate system

u/v/w :

The u/v/w axis of the sub coordinate system

C ikj :

The j-th part in the k-th station of the i-th layer

N ik :

Whole part/component number in the k-th station of the i-th layer

\(\Delta x/\Delta y/\Delta z\) :

The differences in three translations along the coordinate axis

\(\Delta \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{x} /\Delta \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{y} /\Delta \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{z}\) :

The difference in three rotations around the coordinate axis

\(D_{j - l}^{ik 0}\) :

The nominal difference vector between the two parts when they are assembled in the “normal build” state

\(D_{j - l}^{ik}\) :

The difference vector between the two parts

\(\varepsilon_{j - l}^{ik}\) :

The threshold for the 2-norm of the difference vectors

\(\varepsilon_{j - l}^{ikT}\) :

The threshold for the 2-norm of the translational difference vectors

\(\varepsilon_{j - l}^{ikR}\) :

The threshold for the 2-norm of the rotational difference vectors

V j−l :

The evaluation results of any two parts or components in the multi-station

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Acknowledgements

This project is supported by National Natural Science Foundation of China (Grant No. 51875362) and partly supported by SAIC General Motors Corporation.

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Authors and Affiliations

  1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Yinhua Liu, Rui Sun & Shiming Zhang

  2. School of Mechanical Engineering, Guangxi University of Science and Technology, Liuzhou, 545006, China

    Yuwei Lu

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  1. Yinhua Liu
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  4. Shiming Zhang
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Correspondence to Yuwei Lu.

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Liu, Y., Sun, R., Lu, Y. et al. A knowledge-based online fault detection method of the assembly process considering the relative poses of components. Int. J. Precis. Eng. Manuf. 20, 1705–1720 (2019). https://doi.org/10.1007/s12541-019-00218-6

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