Semantic-Based Assembly Precision Optimization Strategy Considering Assembly Process Capacity

Assembly precision optimization is an important means to ensure product accuracy, including two aspects: on the one hand, the relevant deviations of out-of-tolerance key characteristics are reduced to the design tolerance range; on the other hand, the deviation fluctuation range of key characteristics with a large process capability index (Cp) can be extended to achieve the balance between accuracy, process capacity, and production cost. By virtue of the accumulated experience, a fast solution can be provided for the out-of-tolerance problem. Therefore, a semantic-based assembly precision optimization method considering process capacity is proposed in this paper. By constructing an ontology model between Cp and optimization strategy, a reasonable assembly precision optimization strategy can be pushed based on product accuracy analysis results. Firstly, an assembly precision optimization semantic model is established by association between analysis results, out-of-tolerance key characteristics, assembly process, and tolerance adjustment defined with Web Ontology Language (OWL) assertions. Furtherly, according to different Cp corresponding to different assembly success rates, Semantics Web Rule Language (SWRL) rules based on Cp are constructed to the push optimization strategy. Finally, the effectiveness of the model is illustrated by an aircraft inner flap.

An assembly precision prediction method for customized mechanical products based on GAN-FTL

Reliable prediction of assembly precision is important for quality control of customized mechanical products characterized by individual customization, small batch size, and multiple varieties, resulting in insufficient samples for predicting assembly performance. A customized mechanical product assembly precision prediction method based on generative adversarial networks and feature transfer learning (GAN-FTL) is proposed in this paper. A GAN is built based on high quality data (source domain) to generate auxiliary samples with high fidelity and large sample size. A support vector machine is used to generate pseudo-tags for auxiliary samples. Features of source domain, target domain and auxiliary samples from different distributions are transferred to the same distribution to achieve multi-source fusion of measured and simulated data using FTL. Data after FTL is used to train the assembly precision prediction model. The elevator guide rail assembly is taken as the case study. T70/B and T90/B guide rail assembly are selected as the source and target domains, respectively. FTL was performed between the source and target domains, with different sample sets for comparison and compared with five different methods. Experimental results show that the prediction accuracy of the target domain is improved when the auxiliary sample size is 300, 400, and 500, and the accuracy improvement of the five methods are 15.37%, 12.17%, 9.68%, 6.29%, and 4.31%, respectively, which verified the effectiveness and usability of the proposed assembly precision prediction method based on GAN-FTL.

Variation Analysis for Composite Parts With Considering Local Delamination Defects

Laminate composite components have been widely used in engineering fields, such as aerospace and aircraft fields, automotive industries, marine and civil engineering. Due to inherent properties of laminated composites, the effects of local delamination defects will result in a decrease of assembly dimensional precision and mechanical property, and a variation analysis considering the effect of delamination defects become increasingly indispensable. In order to understand the influence relationship between delamination defects and manufacturing deviations, a methodology based on the method of influence coefficients is proposed to analyze the influence relationship between delamination defects and manufacturing deviations. Then, local delamination modes are developed based on the interface deformable bi-layer beam model with consideration of transverse shear and delamination tip deformations. This model provides an accurate solution with the delaminated composite structures. Finally, the effect of local delamination length ratio (a/h) has been further studied for the assembly precision analysis based on the flexible joint model, and the comparison of simulation result shows that the delamination has a signification influence on the structural performance and the quality of the product.