Assembly precision analysis is one of the fundamental technologies used for controlling the assembly quality of products. Existing assembly precision analysis methods focus on identifying the assembly deviation caused by manufacturing errors of parts. They place less emphasis on the influencing factors in the assembly process, which significantly affect the reliability of the analytical results. Additionally, the lack of assembly knowledge for part models leads to a low efficiency of the assembly simulation. To address these problems, this paper presents an assembly precision analysis method based on a general part digital twin model (PDTM). The proposed PDTM integrates multi-source heterogeneous geometric models and maps assembly information from assembly semantics to geometry elements, allowing automatic assembly positioning of parts and improving the efficiency of assembly simulation. In addition to the manufacturing errors, the assembly-positioning error and mating-surface deformation are considered to quantify the impact on the key characteristics of the assembled product. Based on the real mating status simulation for the mating surfaces, the uncertainty of assembly positioning in an actual assembly is simulated by combining the small displacement torsor (SDT) theory and the Monte Carlo method. Furthermore, the mating-surface deformation can be superposed to the result of the assembly-gap calculation, improving the reliability of the analytical results. Finally, a prototype system and a case study involving a load-sensitive multi-way valve assembly process are introduced to demonstrate the applicability of the proposed method.

An assembly precision analysis method based on a general part digital twin model

This paper develops a planner to find an optimal assembly sequence to assemble several objects. The input to the planner is the mesh models of the objects, the relative poses between the objects in the assembly, and the final pose of the assembly. The output is an optimal assembly sequence, namely (1) in which order should one assemble the objects, (2) from which directions should the objects be dropped, and (3) candidate grasps of each object. The proposed planner finds the optimal solution by automatically permuting, evaluating, and searching the possible assembly sequences considering stability, graspability, and assemblability qualities. It is expected to guide robots to do assembly using translational motion. The output provides initial and goal configurations to motion planning algorithms. It is ready to be used by robots and is demonstrated using several simulations and real-world executions.

Assembly Sequence Planning for Motion Planning

Adaptive machining with responsive fixtures is an effective way to release and eliminate deformation during the machining process of large-scale structural parts. While a proper locating principle is a key challenge for the responsive fixture based machining due to its two contradictory requirements of maintaining the machining datum and deformation releasing sufficiently. For traditional machining methods, locators are placed as far as possible from each other to maintain workpiece stability, which would significantly limit the release of workpiece deformation. In order to address the issue mentioned above, different from traditional locating method, a novel 6+X locating principle is proposed in this paper, where the workpiece is divided into a fixed region and a floating region to facilitate the new locating principle. The fixed region is optimized by considering dynamic mass centers with a Genetic Algorithm to maintain workpiece stability, and the 6° of freedom of the workpiece are restrained by the fixed region to keep machining datum. The floating region is supported by X floating units so as to keep the workpiece stable during machining process, while the floating units can adjust the workpiece posture in accordance with the machining deformation during the machining intervals. Algorithm experiments are applied to verify the convergence of the proposed optimization model. Finally, a case study with real machining experiment is carried out to verify the proposed method.

6+X locating principle based on dynamic mass centers of structural parts machined by responsive fixtures

To an aircraft, the accuracy of aerodynamic configuration has a direct influence on flight performance. To improve the assembly accuracy of aircraft wing components, two kinds of positioning method and the corresponding assembly precision are studied, and a low-cost flexible assembly tooling system for different wing components is proposed. First, the article analyzes the technological characteristics of airplane wing skin and determines the assembly requirement. Second, positioning method based on contour boards and coordination holes and their assembly precision are researched. Third, to verify the positioning method and the algorithm of assembly precision calculation based on coordination holes, a locating unit with three motion axes is designed and manufactured. Fourth, experimental verification is done and the corresponding results are analyzed. Experiment results showed the assembly precision based on coordination holes has an improvement of 24% contrasting with the precision based on contour boards...

Positioning method and assembly precision for aircraft wing skin

The layout and assembly of flexible cables play important roles in the design and development of complex electromechanical products. The rationality of cable layout design and the reliability of cable assembly greatly affect product quality. In this paper, we review the methods of cable layout design, cable assembly process planning, and cable assembly simulation. We first review research on flexible cable layout design (both interactive and automatic). Then, research on the cable assembly process planning, including cable assembly path and manipulation planning, is reviewed. Finally, cable assembly simulation is introduced, which includes general cable information, cable collision detection data, and cable assembly process modeling. Current problems and future research directions are summarized at the end of the paper.

A review of cable layout design and assembly simulation in virtual environments

In manufacturing engineering, locating error analysis for workpieces is a common issue in predicting the quality of machined features, computer-aided fixture design and tolerance allocation. This a...

Locating error analysis for workpieces with general fixture layouts and parameterized tolerances

Purpose – The purpose of this paper is to provide a novel assembly simulation method to reduce the repetitive and tedious assembly simulation work. Currently, assembly simulation is always carried out by human–computer interaction, which is a time-consuming and tedious work. The most important reason for this problem is that the assembly simulation is a mapping between human intent and movements of models; at the same time, assembly information is transferred from semantic level to geometric level. However, some essential assembly information is lost during the transfer, and it must be accomplished through manual definition. To address the issue, a novel assembly simulation method is proposed in this paper based on semantics and geometric constraint. Design/methodology/approach – First, an assembly operation semantic model is put forward to integrate and manage the semantic information of assembly, and some rules for modeling are generalized. Second, method for transferring assembly information from seman...

A novel assembly simulation method based on semantics and geometric constraint

The beetle antennae search (BAS) algorithm is an outstanding representative of swarm intelligence algorithms. However, the BAS algorithm still suffers from the deficiency of not being able to handle high-dimensional variables. A quantum-based beetle swarm optimization algorithm (QBSO) is proposed herein to address this deficiency. In order to maintain population diversity and improve the avoidance of falling into local optimal solutions, a novel quantum representation-based position updating strategy is designed. The current best solution is regarded as a linear superposition of two probabilistic states: positive and deceptive. An increase in or reset of the probability of the positive state is performed through a quantum rotation gate to maintain the local and global search ability. Finally, a variable search step strategy is adopted to speed up the ability of the convergence. The QBSO algorithm is verified against several swarm intelligence optimization algorithms, and the results show that the QBSO algorithm still has satisfactory performance at a very small population size.

/pdf/a-quantum-based-beetle-swarm-optimization-algorithm-for-2xkjjb6y.pdf

A Quantum-Based Beetle Swarm Optimization Algorithm for Numerical Optimization

Document-level event extraction (DEE) aims at extracting event records from given documents. Existing DEE methods handle troublesome challenges by using multiple encoders and casting the task into a multi-step paradigm. However, most of the previous approaches ignore a missing feature by using mean pooling or max pooling operations in different encoding stages and have not explicitly modeled the interdependency features between input tokens, and thus the long-distance problem cannot be solved effectively. In this study, we propose Document-level Event Extraction Model Incorporating Dependency Paths (DEEDP), which introduces a novel multi-granularity encoder framework to tackle the aforementioned problems. Specifically, we first designed a Transformer-based encoder, Transformer-M, by adding a Syntactic Feature Attention mechanism to the Transformer, which can capture more interdependency information between input tokens and help enhance the semantics for sentence-level representations of entities. We then stacked Transformer-M and Transformer to integrate sentence-level and document-level features; we thus obtained semantic enhanced document-aware representations for each entity and model long-distance dependencies between arguments. Experimental results on the benchmarks MUC-4 and ChFinAnn demonstrate that DEEDP achieves superior performance over the baselines, proving the effectiveness of our proposed methods.

/pdf/deedp-document-level-event-extraction-model-incorporating-2bu93ddh.pdf

DEEDP: Document-Level Event Extraction Model Incorporating Dependency Paths

Community detection is of great significance to complex network. In response to community detection in complex network which has unknown number of communities, this paper proposed a new community detection approach with integration based on Beetle Swarm Optimization (BSO) and been named BSOI (Beetle Swarm Optimization with Integration). In the initialization of community division, community flag adapting node is adopted, which means the community flag of every node is initialized by its neighbor node. BSO is applied to optimize the community division. Then, we merge communities by the maximum module density increment. Experiments show that it can get a stable and effective result of community division quickly on both artificial and real-world social networks.

Community detection based on Beetle Swarm Optimization

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