Multiple Fault Diagnosis for Sheet Metal Fixtures Using Designated Component Analysis

This paper presents a new approach to multiple fault diagnosis for sheet metal fixtures using designated component analysis (DCA). DCAfirst defines a set of patterns based on product/process information, then finds the significance of these patterns from the measurement data and maps them to a particular set of faults. Existing diagnostics methods has been mainly developed for rigid-body-based 3-2-1 locating scheme. Here an N-2-1 locating scheme is considered since sheet metal parts are compliant. The proposed methodology integrates on-line measurement data, part geometry, fixture layout and sensor layout in detecting simultaneous multiplefixture faults. A diagnosability discussion for the different type of faults is presented. Finally, an application of the proposed method is presented through a computer simulation. @DOI: 10.1115/1.1643076 # Fixtures are used to locate and hold workpiece in manufacturing. In general, fixture elements can be classified by their functionality into locators and clamps. Locators establish the datum reference frame and provide kinematic restraint. Clamps provide additional restraint by holding the part in position under the application of external forces during the manufacturing process. A 3-2-1 locating scheme is used to uniquely locate a rigid body, constraining the six degrees of freedom of the part. According to this principle, three locators are placed in the primary plane, two in the secondary plane and one in the tertiary plane. However, for compliant sheet metal parts, Cai et al. @1# showed that an N-2-1 principle is more adequate and is widely used in industry. The N-2-1 fixture principle establishes that to locate and support compliant sheet metal parts, it is necessary to provide more than 3 locators in the primary plane due to part flexibility. In general, fixture failure directly affects part location and assembly dimensional quality. Ceglarek and Shi @2# found that during the launch of a new vehicle, fixture faults represent around 70% of all dimensional faults. Consequently, adequate fixture failure diagnosis can positively impact dimensional quality. Due to existence of new measurement systems, such as optical coordinate measurement machine ~OCMM !, a large amount of dimensional data can be obtained from manufacturing processes. Therefore, new opportunities for process diagnosis are available. Several authors have studied fixture diagnosis in the last few years. In general, past research in fixture diagnosis is based on three major approaches: principal component analysis, correlation clustering and least square regression. In 1992, Hu and Wu @3# introduced the principal component analysis ~PCA ! to identify sources of dimensional variation in automotive body assembly. They used PCA to extract variation patterns from dimensional data. Later, Ceglarek and Shi @4# proposed a fixture fault diagnosis method combining PCA with pattern recognition. They developed variation patterns for each hypothetical fault based on the fixture and measurement sensor layout. Then, they used principal component analysis to extract variation modes from production data and map the modes with the hypothetical variation patterns. This method focused on single assembly fixture failure. Ding et al. @5# developed a diagnosis method based on a state space dimensional variation model for multistage manufacturing processes using PCA. In addition, Ceglarek and Shi @6# include considerations of measurement noise in fixture failure diagnosis. Correlation clustering is able to detect multiple dimensional faults by matching a model behavior with the measured behavior. Shiu et al. @7# developed a multi-station assembly modeling for diagnostics in automotive body assembly process. The model is based on critical characteristics such as the locating mechanism ~fixture to part interactions ! and the joining conditions ~part to part interactions !. The variation patterns are expressed by a correlation matrix. Then, the correlation matrix from measurement data is compared with the different simulated correlated matrices associated with the faults. Multivariate process diagnosis has also been studied using a least squares approach. The approach consists of relating the measurement variation patterns to potential causes. The least squares method is used to identify the significance of each of these potential causes @8–12 #. Apley and Shi @9# used least squares algorithm to identify the significance or severity of multiple fixture faults. Fault severity was measured as the variance of the decomposition of the data for each variation pattern. The variation patterns where determined as the effect a fixture element fault over the measurement points.