An assembly gap control method based on posture alignment of wing panels in aircraft assembly

The gaps between two mating surfaces should be strictly controlled in precision manufacturing. Oversizing of gaps will decrease the dimensional accuracy and may reduce the fatigue life of a mechanical product. In order to reduce the gaps and keep them within tolerance, the relative posture (orientation and position) of two components should be optimized in the assembly process. This paper presents an optimal posture evaluation model to control the assembly gaps in aircraft wing assembly.Based on the step alignment strategy, i.e. preliminary alignment and refined alignment, the concept of a small posture transformation (SPT) is introduced. In the preliminary alignment, an initial posture is estimated by a set of auxiliary locating points (ALPs), with which the components can be quickly aligned near each other. In the refined alignment, the assembly gaps are calculated and the formulation of the gaps with component posture is derived by the SPT. A comprehensive weighted minimization model with gap tolerance constraints is established for redistributing the gaps in multi-regions. Powell-Hestenes-Rockafellar (PHR) optimization, Singular Value Decomposition (SVD) and KD-tree searching are introduced for the solution of the optimal posture for localization. Using the SPT, the trigonometric posture transformation is linearized, which benefits the iterative solution process. Through the constrained model, overall gaps are minimized and excess gaps are controlled within tolerance. Practical implications – This method has been tested with simulated model data and real product data, the results of which have shown efficient coordination of mating components.This paper proposed an optimal posture evaluation method for minimizing the gaps between mating surfaces through component adjustments. This will promote the assembly automation and variation control in aircraft wing assembly

DSSim-ontology mapping with uncertainty

This paper introduces an ontology mapping system that is used with a multi agent ontology mapping framework in the context of question answering. Our mapping algorithm incorporates the Dempster Shafer theory of evidence into the mapping process in order to improve the correctness of the mapping. Our main objective was to assess how applying the belief function can improve correctness of the ontology mapping through combining the similarities which were originally created by both syntactic and semantic similarity algorithms. We carried out experiments with the data sets of the Ontology Alignment Evaluation Initiative 2006 which served as a test bed to assess both the strong and weak points of our system. The experiments confirm that our algorithm performs well with both concept and property names

Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding

We construct a multilingual common semantic space based on distributional semantics, where words from multiple languages are projected into a shared space to enable knowledge and resource transfer across languages. Beyond word alignment, we introduce multiple cluster-level alignments and enforce the word clusters to be consistently distributed across multiple languages. We exploit three signals for clustering: (1) neighbor words in the monolingual word embedding space; (2) character-level information; and (3) linguistic properties (e.g., apposition, locative suffix) derived from linguistic structure knowledge bases available for thousands of languages. We introduce a new cluster-consistent correlational neural network to construct the common semantic space by aligning words as well as clusters. Intrinsic evaluation on monolingual and multilingual QVEC tasks shows our approach achieves significantly higher correlation with linguistic features than state-of-the-art multi-lingual embedding learning methods do. Using low-resource language name tagging as a case study for extrinsic evaluation, our approach achieves up to 24.5\% absolute F-score gain over the state of the art.Comment: 10 page