Assembly Features: definition, classification, and usefulness in sequence planning

Assembly sequence planning is a typical of combinatorial optimization problem which is difficult to be tackled when the number of parts of assembly becomes large. To reduce the searching space of assembly sequence planning of complex products, assembly sequences merging based on assembly unit partitioning is suggested. Assembly unit partitioning is presented to decompose the complex products into a group of assembly units containing a reduced number of parts or components, and the assembly design constraints and the assembly process constraints are comprehensively taken into account. The global optimal assembly sequences can be acquired through three steps. Firstly, the assembly units and decision graph of assembly unit are generated utilizing fuzzy analytical hierarchy process approach. Secondly, the optimal or near-optimal subsequences of assembly units can be obtained with current efficient methods of assembly sequence planning. Thirdly, under the assembly interference of assembly relations (geometrical constraints) of the whole products and the assembly precedence concluded by subsequences of assembly units, the assembly sequence merging is implemented to generate the global assembly sequences, and the optimal sequence is obtained through assembly sequences evaluation. The assembly constraints considered at the two previous steps is represented by the evaluation function. The effectiveness of the method is verified by an illustrative example and the results show that the searching space of assembly sequence merging of complex products is reduced remarkably and the optimal assembly sequence of the whole produces is obtained.

The invention relates to a fixture assembly sequence planning method based on an ant colony algorithm. The fixture assembly sequence planning method comprises the following steps: firstly carrying out product assembly modeling based on a directed graph, thus establishing a foundation for solving assembly sequence planning by using the ant colony algorithm later; constructing the ant colony algorithm oriented to the fixture assembly sequence planning according to the fundamental principles of the ant colony algorithm, utilizing the advantages of the ant colony algorithm to optimize the assembly sequence of fixtures, using redirection times and tool change times as evaluation indexes, and searching out the optimum assembly sequence in numerous feasible assembly sequences; using Matlab programming to realize the optimization procedure of the ant colony algorithm on the fixture assembly sequences to obtain the optimum assembly sequence. The fixture assembly sequence planning method can improve the design efficiency of enterprises, reduces the production cost, shortens the production period, and enhances the market competitiveness.

A tree-model for assembly sequences planning is proposed after analyzing the characteristic of assembly sequence evaluation, and the generation algorithm of the tree-model is proposed. Based on the tree-model of assembly sequence, the two-phase method for synthetically evaluating and optimizing assembly sequence layer by layer is expounded. Weights of all evaluation indexes are calculated by using the entropy method. Then, by using the fuzzy comprehensive evaluation, the assembly sequence of all data sub-nodes under A or AP in every layer of the assembly sequence tree-model is optimized. Finally, the optimal assembly sequence is generated. Using this method, the calculation extension of the assembly sequence is reduced, and the evaluation efficiency is heightened. The theory is validated by an example of gear reducer.

This paper describes a system for the automatic recognition of assembly features and the generation of assembly/disassembly sequences. The paper starts by reviewing the nature and use of assembly features. One of the conclusions drawn from this survey is that the majority of assembly features involve sets of spatially adjacent faces. Two principle types of adjacency relationships are identified and an algorithm is presented for identifying assembly features which arise from "spatial" and "contact" face adjacency relationships (known as s-adjacency and c-adjacency respectively).

Due to the complexities of calculations based on different representations of constituent parts in an assembly, the combined improvement of efficiency and precision for the riveting has seldom been studied by the sequence planning or dimensional engineering. This article develops an assembly sequence optimization method that simultaneously minimizes the path length and overall dimensional error for the solid riveting. The sequence is denoted by the order of classifications for the nodes around the rivet holes in the finite element model for the assembly. Ant colony algorithm is applied on the optimization by integrating two calculations, that is, the length calculation of riveting path and the calculation of the dimensional error related to rivet upsetting directions and assembly sequence. Results for three assemblies are presented which show a good degree of optimization in performance for different scales of assemblies with different numbers of rivets that share the same solid riveting process.

For the distribution of consumer and industrial goods, every company is obliged to provide assembly instructions. For the consumer goods market, for example, the German Civil Code defines that defective assembly instructions must be declared as a material defect. However, the creation of comprehensible and defect-free assembly instructions is still a very time-consuming manual process, which must be determined in an extremely time-consuming procedure. Nonetheless, assembly instructions are more than just obligatory documents. They are also required in places where they are not prescribed. For example, assembly instructions are needed in production to pass on assembly knowledge to the assembly operators. Here, it often turns out that this knowledge is either not available or can only be used to a limited extent.The two key elements of an assembly instruction are the assembly sequence and the visual illustrations. Currently, the assembly sequence is determined manually by the designers based on their personal experience, whereas illustrations are generated with costly software tools which are not even able to check the feasibility of the planned instruction. This work presents a novel approach to generate assembly instructions directly and automatically from CAD models of the designers. For this purpose, the commercial CAD software SolidWorks was extended by a macro Tool. All necessary data to generate an assembly instruction are extracted from the CAD model. The extracted data are assembly features, stability and geometric restrictions, subassemblies and assembly directions. Based on these data, the assembly operations are evaluated with a fitness function which includes the attributes like tool changing costs or distances of assembly paths. The whole assembly sequence optimization process was modeled as a Travelling Salesman Problem. After the ideal assembly sequence was found by the macro Tool, this tool also generated matching visualizations of the assembly operations based on the CAD model. The approach was validated by three different models, an assembly benchmark, a single-cylinder engine and a gear box.

Assembly sequence planning (ASP) is a combinatorial optimization problem with highly non-linear geometric constraints. Most proposed methodologies are based on graph theory and involve complex geometric and physical analysis. As a result, even for a simple structure, it is difficult or impossible to take all important criteria into consideration. In order to bring assembly sequence planning closer to real-world application, this paper proposes a genetic planner for efficiently finding global-optimal assembly sequences. To optimize our genetic-algorithm-based approach, we propose a hierarchical genetic structure and an evaluation mechanism for dynamically adapting control parameters in our hierarchical structure. Unlike conventional genetic algorithms, which use static genetic operator probability settings (GOPS), our hierarchical genetic planner searches for optimal assembly sequences in a low-level GA and manipulates low-level GOPS using a high level GA. Conventional “GA within GA” approaches perform, for every high-level generation, a full low-level GA run, whereas our multi-level GA uses a high-level GA to isochronously update low-level GA control parameters during each low-level GA run. Experimental results show that our multi-level genetic assembly sequence planner solves combinatorial ASP problems quickly, reliably, and accurately.

Manufacturing industries are looking for efficient assembly planners that can swiftly develop a practically feasible assembly sequence while keeping costs and time to a minimum. Most assembly sequence planners rely on part relations in the virtual environment. Nowadays, tools and robotic grippers perform most of the assembly tasks. Ignoring the critical aspect renders solutions practically infeasible. Additionally, it is vital to test the feasibility of positioning and assembling components while employing robotic grippers and tools prior to their implementation. This paper presents a novel concept named by considering both part and tool geometry to propose “tool integrated assembly interference matrices” (TIAIMs) and a “tool integrated axis-aligned bounding box” (TIAABB) to generate practically feasible assembly sequence plans. Furthermore, the part-concatenation technique is used to determine the best assembly sequence plans for an actual mechanical component. The results show that the proposed approach effectively and efficiently deals with real-life industrial problems.

Multi-sequence modelling is proposed to optimise assembly sequences for compliant body assemblies such as automotive and aircraft bodies. Multi-sequence modelling is composed of assembly modelling, assembly sequences, tolerance analysis, and assembly operations. Assembly modelling describes the geometric modelling through the liaison graph, which is based on assembly sequence optimisation. Assembly sequences provide information for assembly sequence optimisation. Three-dimensional tolerance analysis is employed to evaluate assembly operations, which is different from previous conceptual tolerance analysis. A genetic algorithm is presented to optimise assembly operations among components. Results show that different sequences lead to a different tolerance of key product characteristics because assembly operations among components are not identical. This paper provides the flowchart of optimising assembly sequences according to the tolerance of key product characteristics.

Assembly features encode geometric and functional relationships between parts in an assembly. They can be used in assembly design by features, kinematic and structural analyses, assembly planning and assemblability evaluation. Explicit definition of assembly features and archival in libraries can be used to assist in design and analysis of mechanical assemblies. This paper builds on our past work in neutral definition of generic assembly features to provide a mechanism for user-defined custom assembly features using an interactive system, called the assembly feature tutor. The user teaches the system interactively by examples and the tutor outputs an assembly definition file that is written in EXPRESS like language. Central to the definition are pairs of mating part faces and includes algebraic, geometric and parametric relations. Twist and wrench matrices can be extracted from face pair relations. The definition is implementation independent and provides a uniform way to define assembly features....

As space structures trend toward larger scales and modularization, their on-orbit assembly presents a significant challenge. Assembly sequence planning is crucial in minimizing vibration and enhancing the efficiency of on-orbit construction. This paper explores the multirobot assembly sequence planning algorithm for a large space telescope. A dynamic model is firstly developed to analyze the vibration of segmented mirror pieces and the main satellite during assembly, taking into account the flexibility of connections at interfaces. The dynamic model is able to provide criteria for time efficiency and satellite attitude disturbances. Considering constraints during the assembly process, a modified multiobjective ant colony optimization algorithm capable of planning multirobot assembly sequences is implemented. The simulation results demonstrate that the proposed method not only facilitates parallel planning of multirobot assembly sequences but also leads to a reduction in assembly time and attitude disturbances of the main satellite. The results of assembly sequence planning provide dependable guidance for the subsequent planning levels, such as task planning and trajectory planning, of the on-orbit assembly task for large space telescopes.

If a product is made of more than one component, then it needs to be assembled. Complexity of assembling a product is largely subject to the implication of product design as well as the number of assembly components that may also affect attainable assembly sequences. In addition, it is widely understood that efficiency of assembling a product can be partially achieved by reducing assembly times (therefore costs) and this is notably important for small and medium-sized manufacturing companies. Thus, it is useful for seeking an optimal assembly sequence of a product at the early product design stage. This paper presents a study by applying the genetic algorithm (GA) for solving an assembly sequence optimisation problem of a car engine pump valve providing a quick solution in obtaining an optimal or near-optimal assembly sequence of the product.

Sequence planning generation is an important problem in assembly line design. A good assembly sequence can help to reduce the cost and time of the manufacturing process. This paper focuses on assembly sequence planning (ASP) known as a hard combinatorial optimization problem. Although the ASP problem has been tackled via even more sophisticated optimization techniques, these techniques are often inefficient for proposing feasible assembly sequences that satisfy the assembly planners’ preferences. This paper presents an approach that makes easier to check the validity of operations in assembly process. It is based on a model of the assembly planners’ preferences by means of strategic constraints. It helps to check a priori the consistency of the assembly constraints (strategic and operative constraints) given by the assembly system designers before and while running an assembly plan generation algorithm. This approach reduces the solution space significantly. A case study is presented to demonstrate the relevance of the proposed approach.

Nowadays in order to perform assembly process planning by using CAPP (Computer Aided Process Planning), researches to generate the assembly sequence have come under constant interest by many researchers and they are devoted to optimize the assembly sequence according to the novel approach such as Genetic Algorithm and the newly biology inspired principle such as ant colony optimizing method. In this paper, the developed assembly sequence generating system analyzes the relationship between assembled parts based on surface contacting information. Moreover, this system generates the new type of parts liaison graph for the assembly sequences via analyzed information such as common area between parts, related ratio and number of the connected parts. Finally, the optimized assembly sequence is generated logically through the parts liaison graph.KeywordsAssembly SequenceLiaison GraphRelated RatioCommon Area

Assembly planning calls for the subtle consideration of certain limitation factors such as geometric features and tools so as to work out a specific assembly sequence. From the assembly sequence, all parts will be turned into a product. It is evident that the degree of complexity of the assembly problem will increase when the number of constraints is larger. Using Genetic Algorithms (GAs) to solve the assembly sequence features speed and flexibility can fit the requirements of various domains. In the case of larger constraint assembly problems, however, GAs will generate a large number of infeasible solutions in the evolution procedure, thus reducing the efficiency of the solution-searching process. Traditionally, using GAs is a random and blind-searching procedure in which it is not always the case that the offspring obtained through the evolutionary mechanism will meet the requirements of all limitations. In this study, therefore, Guided-GAs are proposed wherein the proper initial population and the alternation of crossover and mutation mechanisms are covered to overcome assembly planning problems that contain large constraints. The optimal assembly sequence is obtained through the combination of Guided-GAs and the Connector-based assembly planning context as previously suggested. Finally, practical examples are offered to illustrate the feasibility of Guided-GAs. It is found that Guided-GAs can effectively solve the assembly planning problem of larger constraints.