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Haptics can significantly enhance the user's sense of immersion and interactivity. Especially in an assembly task, haptic feedback can help designers to have a better understanding of virtual objects and to increase task efficiency. The purpose of this paper is to investigate the design and implementation of a haptic‐based virtual assembly system (HVAS).

A multi‐thread system structure was designed, an automatic data integration interface was developed to transfer geometry, topology, assembly and physics information from a computer‐aided design system to virtual reality application, and a hierarchical constraint‐based data model and scene graph structure was designed to construct the virtual assembly environment. Unlike traditional virtual assembly systems based on collision detection or geometry constraint only, a physics‐based modeling approach combining with haptic feedback and geometry constraint was undertaken to realize and guide the realistic assembly process. When two parts collide into each other, the force and torque can be computed and provide feedback, and a spring‐mass model is used to prevent penetration and simulate dynamic behaviour. When two parts are close enough to each other and the assembly simulation state is activated, a geometry constraint can be captured, an attractive force can be generated to guide the user to assemble the part along the correct position, and the repulsive force can also be generated to realize the mating process as natural and realistic as in real life.

The implementation details and application examples demonstrate that haptic‐based virtual assembly is a valuable tool for assembly design and process planning.

The paper presents an HVAS.

In this paper, the authors aim to propose a method for learning robotic assembly sequences, where precedence constraints and object relative size and location constraints can be learned by demonstration and autonomous robot exploration.

To successfully plan the operations involved in assembly tasks, the planner needs to know the constraints of the desired task. In this paper, the authors propose a methodology for learning such constraints by demonstration and autonomous exploration. The learning of precedence constraints and object relative size and location constraints, which are needed to construct a planner for automated assembly, were investigated. In the developed system, the learning of symbolic constraints is integrated with low-level control algorithms, which is essential to enable active robot learning.

The authors demonstrated that the proposed reasoning algorithms can be used to learn previously unknown assembly constraints that are needed to implement a planner for automated assembly. Cranfield benchmark, which is a standardized benchmark for testing algorithms for robot assembly, was used to evaluate the proposed approaches. The authors evaluated the learning performance both in simulation and on a real robot.

The authors' approach reduces the amount of programming that is needed to set up new assembly cells and consequently the overall set up time when new products are introduced into the workcell.

In this paper, the authors propose a new approach for learning assembly constraints based on programming by demonstration and active robot exploration to reduce the computational complexity of the underlying search problems. The authors developed algorithms for success/failure detection of assembly operations based on the comparison of expected signals (forces and torques, positions and orientations of the assembly parts) with the actual signals sensed by a robot. In this manner, all precedence and object size and location constraints can be learned, thereby providing the necessary input for the optimal planning of the entire assembly process.

The virtual design environment offers users an opportunity to interact with a virtual prototyping rather than physical models and to build a fixture configuration in a realistic way. But the virtual reality (VR) environment tends to be inaccurate because humans have difficulty in performing precise positioning tasks. Therefore, it is necessary to implement precise object manipulation methods for assembly and disassembly activities, so that users can perform modular fixture configuration design efficiently in VE. The purpose of this paper is to develop a VR‐based modular fixture assembly design system, which supports the design and assembly of modular fixture configuration in a virtual environment.

Geometric constraint‐based method is utilized to represent and treat the assembly relationship between modular fixture elements. The paper presents a hybrid method of rule‐based reasoning and fuzzy comprehensive judgment to capture the user's operation intent and recognize geometric constraint. Through degrees of freedom based analysis, a mathematical matrix is presented for representing and reducing allowable motion of fixture elements, and a constraint‐based motion navigation approach is proposed to ensure that the manipulation of a fixture component not violate that the existing constraints.

The paper finds that the proposed techniques are applicable to the convenient manipulation and accurate positioning of fixture elements in a virtual environment.

Component manipulation plays a key role in interactive virtual assembly design. The proposed approach in this paper enables interactive assembly design of modular fixture in virtual environment.

This paper presents a geometric constraint‐based approach that realizes automatic assembly relationship recognition, constraint solving and motion navigation for interactive modular fixture assembly design in a virtual environment.

Modeling and analysis of dimensional variation propagation is a crucial support technology for variation reduction, product/process design evaluation and recognition of variation source. However, owing to the multi-deviation (i.e. part deviations and fixture deviations) and multi-interaction (i.e. part-to-part interaction, part-to-fixture interaction and station-to-station interaction) in assembly processes, it is difficult for designers to describe or understand the variation propagation (or accumulation) mechanism clearly. The purpose of this paper is to propose a variation propagation modeling and analysis (VPMA) method based on multiple constraints aiming at a single station.

Initially, part-to-part constraints (PPCs) and part-to-fixture constraints (PFCs) are applied for the multi-interaction of assembly, and multiple constraints graph (MCG) model is proposed for expressing PPCs, PFCs, parts, as well as the variation propagation relation among them. Then, locating points (LPs) are adopted for representing the deviations in constraints, and formulas for calculating the deviations of LPs are derived. On that basis, a linearized relation between LPs’ deviations and part’s locating deviations is derived. Finally, a wing box is presented to validate the proposed method, and the results indicate the methodology’s feasibility.

MCG is an effective tool for dimensional VPMA, which is shown as an example of this paper.

Functions of geometric constraints in dimensional variation propagation are revealed, and MCG is proposed to formulize dimensional variation propagation.

The purpose of this paper is to propose an assembly coordination modelling approach based on measured data for assembly quality control of multi-constrained objects in aircraft assembly. This approach aims to establish a high-precision digital mirror of physical assembly system in the virtual environment, with which the assembly process in the virtual environment can be performed synchronously with that in the physical world.

This paper presents a realistic geometrical representation model based on measured point cloud, as well as the multiple constraints modelling methods for local and global constraints with the proposed representation model. For the assembly target optimization, a novel optimization method based on the evaluation of multi-dimensional tolerance zone is proposed, where the particle swarm optimization and simulated annealing algorithm are combined to calculate the optimal solutions.

As shown in the validation results, the minimum easiness value for easiness model in global optimization is 3.01, while the best value for weighting model by adjusting weights for more than 10 times is 1.94. The results verify that the proposed coordination modelling approach is effective to the assembly of multi-constrained objects, and the optimization model has an obvious advantage over the traditional weighting method.

This paper provides a new idea for the fine control of assembly quality of non-ideal components by introducing the measured data into the on-line assembly process. Besides, a novel optimization method based on the evaluation of multi-dimensional tolerance zone is proposed, which overcomes the problem of traditional weighting model wherein the weightings are difficult to determine.

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.

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 semantic level to geometric level is presented by dividing assembly operation into a set of simulation actions and providing some rules for this division. Then, a geometric constraint-based calculation method is proposed to obtain the essential parameters of each simulation action. Finally, cases are studied to demonstrate the effectiveness of the method.

Results show that laborious work would be reduced, and the redundant human participation would be avoided in assembly simulation.

It has the potential and possibility to change the current pattern of assembly simulation.

A novel assembly simulation method based on semantics and geometric constraint is presented to make assembly simulation more convenient and faster.

Existing models of product assembly scheme design often ignore the constraint relations among design thinking. In order to grasp the functions of each part and the constraint relations among them in product assembly system macroscopically, further design and variation of product assembly system should be made according to design thinking. This paper seeks to address these issues.

Through analyzing the similarity between biological organization system and product system and taking biology knowledge for reference, product assembly system was expressed as product function gene, product constraint gene, product function protein, product constraint protein and product cell and so on in this paper. The product gene model composed of product function gene groups and constraint genes was established and a modeling method based on it was proposed.

The author applied this method to model the 5‐DOF manipulator of complex diamond manufacturing special equipment with good results which proved the effectiveness of this modeling method.

By identifying constraint relations and design thinking in the gene model, the system makes the modification process which is conducted by the designers automatically identified and varied to achieve computer‐aided design and assembly.

The implied assembly constraints of a computer-aided design (CAD) model (e.g. hierarchical constraints, geometric constraints and topological constraints) represent an important basis for product assembly sequence intelligent planning. Assembly prior knowledge contains factual assembly knowledge and experience assembly knowledge, which are important factors for assembly sequence intelligent planning. This paper aims to improve monotonous assembly sequence planning for a rigid product, intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge is proposed.

A spatio-temporal semantic assembly information model is established. The internal data of the CAD model are accessed to extract spatio-temporal semantic assembly information. The knowledge system for assembly sequence intelligent planning is built using an ontology model. The assembly sequence for the sub-assembly and assembly is generated via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge. The optimal assembly sequence is achieved via a fuzzy comprehensive evaluation.

The proposed spatio-temporal semantic information model and knowledge system can simultaneously express CAD model knowledge and prior knowledge for intelligent planning of product assembly sequences. Attribute retrieval and rule reasoning of spatio-temporal semantic knowledge can be used to generate product assembly sequences.

The assembly sequence intelligent planning example of linear motor highlights the validity of intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge.

The spatio-temporal semantic information model and knowledge system are built to simultaneously express CAD model knowledge and assembly prior knowledge. The generation algorithm via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge is given for intelligent planning of product assembly sequences in this paper. The proposed method is efficient because of the small search space.

The purpose of this paper is to give a comprehensive survey on the physics-based virtual assembly (PBVA) technology in a novel perspective, to analyze current drawbacks and propose several promising future directions.

To provide a deep insight of PBVA, a discussion of the developing context of PBVA and a comparison against constraint-based virtual assembly (CBVA) is put forward. The core elements and general structure are analyzed based on typical PBVA systems. Some common key issues as well as common drawbacks are discussed, based on which the research trend and several promising future directions are proposed.

Special attention is paid to new research progresses and new ideas concerning recent development as well as new typical systems of the technology. Advantages of PBVA over CBVA are investigated. Based on the analysis of typical PBVA systems and the evolution of PBVA, the core elements of the technology and the general structure of its implementation are identified. Then, current PBVA systems are summarized and classified. After that, key issues in the technology and current drawbacks are explored in detail. Finally, promising future directions are given, including both the further perfecting of the technology and the combination with other technologies.

The PBVA technology is put into a detailed review and analysis in a novel way, providing a better insight of both the theory and the implementation of the technology.

The purpose of this paper is to develop a modeling and interactive operating method for virtual assembly (VA) to support assembly process generation based on interactive operation.

This paper puts forward an assembly semantic modeling method for interactive assembly and process generation after the analysis on requirements of operation process generation. Based on this semantic model, methods for semantic generation, semantic processing and assembly motion extraction from interactive operation are presented. Partial process generation of auto engine is proposed to verify the approaches in this paper.

The application shows that assembly semantic modeling and operating methods can support process generation based on VA operations.

The approaches presented in this paper improve the efficiency of assembly process, making assembly process intuitive and natural.