Integration of geometric variation and part deformation into variation propagation of 3-D assemblies

Guo, Junkang; Li, Baotong; Liu, Zhigang; Hong, Jun; Wu, Xinglong

doi:10.1080/00207543.2016.1158881

Cited by 47 publications

(28 citation statements)

References 38 publications

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“…In the repeated experiments, the bolt jointed structures were disassembled and reassembled three times. In this way, the wave propagation path might be changed due to assembly variations [ 36 ]. The other main source of deviation is that there is a scatter in the torque-preload relationship [ 1 ], and the preloads were not the same when the bolt torques were the same in the repeated experiments.…”

Section: Resultsmentioning

confidence: 99%

Preload Monitoring of Bolted L-Shaped Lap Joints Using Virtual Time Reversal Method

et al. 2018

Sensors

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L-shaped bolt lap joints are commonly used in aerospace and civil structures. However, bolt joints are frequently subjected to loosening, and this has a significant effect on the safety and reliability of these structures. Therefore, bolt preload monitoring is very important, especially at the early stage of loosening. In this paper, a virtual time reversal guided wave method is presented to monitor preload of bolted L-shaped lap joints accurately and simply. In this method, a referenced reemitting signal (RRS) is extracted from the bolted structure in fully tightened condition. Then the RRS is utilized as the excitation signal for the bolted structure in loosening states, and the normalized peak amplitude of refocused wave packet is used as the tightness index (TIA). The proposed method is experimentally validated by L-shaped bolt joints with single and multiple bolts. Moreover, the selections of guided wave frequency and tightness index are also discussed. The results demonstrate that the relationship between TIA and bolt preload is linear. The detection sensitivity is improved significantly compared with time reversal (TR) method, particularly when bolt loosening is at its embryo stage. The results also show that TR method is an effective method for detection of the number of loosening bolts.

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Section: Resultsmentioning

confidence: 99%

Preload Monitoring of Bolted L-Shaped Lap Joints Using Virtual Time Reversal Method

et al. 2018

Sensors

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“…The random errors (roughness and waviness) are similar to geographic terrains and the average roughness has no practical meaning in relating surfaces to its functional effectiveness [31]. Further, the surface waviness under a large applied force (such as clamping and cutting forces) has a negligible contribution to the resulting surface variation at a distance greater than 20 mm away from the source, in accordance to Saint-Venant's Principle [32]. Hence, a manufacturing simulation during the prediction stage can focus only on systematic errors when assembling fixture and part models, without significantly influencing the setup accuracy.…”

Section: Machine Tool Setupmentioning

confidence: 99%

Octree-Based Generation and Variation Analysis of Skin Model Shapes

Yacob

Semere

Nordgren

2018

JMMP

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The concept of Skin Model Shape has been introduced as a method for a close representation of manufactured parts using a discrete geometry representation scheme. However, discretized surfaces make irregular polyhedra, which are computationally demanding to model and process using the traditional implicit surface and boundary representation techniques. Moreover, there are still some research challenges related to the geometrical variation modelling of manufactured products; specifically, methods for geometrical data processing, the mapping of manufacturing variation sources to a geometric model, and the improvement of variation visualization techniques. To provide steps towards addressing these challenges this work uses Octree, a 3D space partitioning technique, as an aid for geometrical data processing, variation visualization, variation modelling and propagation, and tolerance analysis. Further, Skin Model Shapes are generated either by manufacturing a simulation using a non-ideal toolpath on solid models of Skin Model Shapes that are assembled to non-ideal fixtures or from measurement data. Octrees are then used in a variation envelope extraction from the simulated or measurement data, which becomes a basis for further simulation and tolerance analysis. To illustrate the method, an industrial two-stage truck component manufacturing line was studied. Simulation results show that the predicted Skin Model Shapes closely match to the measurement data from the manufacturing line, which could also be used to map to manufacturing error sources. This approach contributes towards the application of Octrees in many Skin Model Shape related operations and processes. of 21However, the computational cost scales up with mesh density and the number of sampled points per part. The meshes and the reconstructed point clouds form irregular polyhedra, whose representation, operation, and manipulation, based on implicit surfaces and boundary representation techniques, is computationally slow and memory intensive [5][6][7]. Since the prime aim of utilizing SMSs is to get a detailed digital representation of parts, computational efficacy of SMS modelling and operations is crucial. As an alternative, an approach based on a 3D space partitioning technique, using Octrees, has been proven to significantly improve computation time and memory in manipulation and processing of irregular polyhedra [8][9][10]. This work utilizes the computational efficacy of Octrees, in one hand, and their capability to localize regions of form errors, in the other hand, in the generation and variation analysis of SMSs.Moreover, despite many contributions in SMS generation methods and associated operations, there are still some challenges that need to be addressed; specifically, the mapping of manufacturing variation sources to geometric models, the development of geometrical data processing methods applicable in different stages of variation modelling, and the improvement of variation visualization techniques [11]. To address these challenges, in the context...

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“…Existing assembly error evaluation methods mainly include the tolerance analysis method [ 1 , 2 , 3 , 4 , 5 ] and finite element analysis [ 6 , 7 , 8 , 9 , 10 ]. In precision assembly, surface topography errors in the parts are unavoidable and cause assembly error.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Method to Evaluate Contact-Pressure Distribution for Assembly-Accuracy Stability Prediction

Chen

Jin

Shang

et al. 2019

Entropy

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Assembly accuracy and accuracy stability prediction are significant research directions for improving the reliability and efficiency of precision assembly. In this study, an improved method for assembly accuracy stability prediction, based on the contact-pressure distribution entropy, is presented. By using the contact-pressure distribution as the evaluation parameter instead of the strain-energy distribution, the improved method can not only predict the assembly accuracy of precision assembly more efficiently, but also predict the stability of the assembly accuracy with variations in the ambient temperature. The contact pressure has a clearer mechanical significance than strain energy density in the assembly process, which can be used to distinguish the actual contact area from the contact surface. Hence, the improved method is more efficient and accurate than the original. This study utilizes the same case used in the original method and an additional case from the actual production process to verify the improved method. The correctness and validity of the improved method are proved by these case studies.

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Integration of geometric variation and part deformation into variation propagation of 3-D assemblies

Cited by 47 publications

References 38 publications

Preload Monitoring of Bolted L-Shaped Lap Joints Using Virtual Time Reversal Method

Preload Monitoring of Bolted L-Shaped Lap Joints Using Virtual Time Reversal Method

Octree-Based Generation and Variation Analysis of Skin Model Shapes

Entropy-Based Method to Evaluate Contact-Pressure Distribution for Assembly-Accuracy Stability Prediction

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