Optimal robotic assembly sequence planning with tool integrated assembly interference matrix

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.

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.

In a multi-plant collaborative manufacturing system, the manufacturing operations and assembly operations for producing a product can be distributed at different plants at various locations. The components are assembled with assembly operations performed in a multi-plant assembly sequence. In this research, a multi-plant assembly sequence planning model is presented by integrating (1) assembly sequence planning, and (2) plant assignment. In assembly sequence planning, the components and assembly operations are sequenced according to the assembly constraints and assembly cost objectives. In plant assignment, the components and assembly operations are assigned to the suitable plants to achieve multi-plant cost objectives. The feasible assembly sequences are generated using the developed graph-based models of assembly precedence graphs and matrices. A genetic algorithm (GA) method is presented to evaluate the multi-plant assembly sequences with an objective of minimizing the total of assembly operational costs and multi-plant costs. The main contribution lies in the new model for multi-plant assembly sequence planning and the new GA encoding scheme for simultaneous assembly sequence planning and plant assignment. Example products are tested and discussed. The test results show that the presented method is feasible and efficient for solving the multi-plant assembly sequence planning problem.

Assembly is a critical step of product manufacturing and processing. With the continuous development of the manufacturing industry, many mechanical and electrical products that need to be assembled have some components that belong to flexible components, which have physical differences from rigid components, such as that flexible components are deformable under the action of force to avoid interference, causing differences in the assembly process and requirements of flexible components compared to rigid components. This paper proposes a novel assembly sequence planning method that considers the characteristics of flexible components. Firstly, in response to the difference between flexible components and rigid components in terms of interference and collision judgment during the assembly process, a novel assembly stress matrix for flexible components is defined. Secondly, considering the influence of flexible components on product assembly, a mathematical model for assembly sequence planning is established, the Flower Pollination Algorithm (FPA) is applied to solve the optimal feasible assembly sequence. Finally, through the case study, the effectiveness of the proposed assembly sequence planning method is verified.

This paper focuses on multi-criteria assembly sequence planning (ASP) known as a large-scale, time-consuming combinatorial problem. Although the ASP problem has been tackled via a variety of optimization techniques, these techniques are often inefficient when applied to larger-scale problems. Genetic algorithm (GA) is the most widely known type of evolutionary computation method, incorporating biological concepts into analytical studies of systems. In this research, an approach is proposed to optimize multi-criteria ASP based on GA. A precedence matrix is proposed to determine feasible assembly sequences that satisfy precedence constraints. A numerical example is presented to demonstrate the performance of the proposed algorithm. The results of comparison in the provided experiment show that the developed algorithm is an efficient approach to solve the ASP problem and can be suitably applied to any kind of ASP with large numbers of components and multi-objective functions.

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.

Assembly sequence planning has drawn the attention of many researchers over the last years. Despite, the great development of CAD software in many aspects (simulation algorithms, interfaces, and ergonomics), automatic generation of assembly sequence planning from CAD assembly remains an issue to improve especially with the increasing complexity of industrial products. In this paper, an approach of assembly sequence planning generation from a CAD assembly model is presented. In order to generate feasible assembly sequence planning, the developed approach begins with the exploration of CAD assembly. Then, a collision analysis is performed to identify all possible obstacles during the components’ motion. An example of CAD assembly is presented in all sections of this paper in order to demonstrate the efficiency of the proposed approach.

In a multi-plant collaborative manufacturing system, the manufacturing and assembly operations for producing a product can be distributed at different plants at different locations. In this research, a multi-plant assembly sequence planning model is presented by integrating assembly sequence planning and plant assignment and is solved using a particle swarm optimisation (PSO) algorithm. In assembly sequence planning, the components and assembly operations are sequenced according to the operational constraints and precedence constraints to achieve assembly cost objectives. In plant assignment, the components and assembly operations are assigned to the suitable plants under the constraints of plant capabilities to achieve multi-plant cost objectives. A new PSO encoding scheme is presented in which a particle is defined by a position matrix defined by the numbers of components and plants. The PSO algorithm simultaneously performs assembly sequence planning of components and assignment of plants with an objective of minimising the total of assembly operational costs and multi-plant costs. The main contribution lies in the new multi-plant assembly sequence planning model and the new PSO solution scheme. An example product is tested and illustrated. The test results show that the presented method is feasible and efficient for solving the multi-plant assembly sequence planning problem.

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set.

Virtual assembly, as a typical application of Virtual Reality (VR), provides an efficient tool to optimise assembly planning and evaluation. In this paper, a VR-based Assembly Planning and Training System (VR-APTS) is introduced. Using a Computer-Aided Design (CAD) assembly model as input, the system can output one or several good assembly sequences with the application of an intelligent bionic algorithm. Key techniques in the development of VR-APTS are discussed. A novel information decomposition and transformation method is put forward to transform data from CAD to VR, and a geometric constraint-based virtual environment is set up to support interactive assembly planning and evaluation. First, an initial optimal assembly sequence is generated by virtual disassembly and ant colony optimisation algorithm. Then, this initial sequence is evaluated by virtual assembly simulation, a more practical and optimal assembly sequence is replaned. This is an iterative process until the best assembly sequence is obtained. An application case is studied to demonstrate the basic advantages of the system for assembly planning and training.

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.

Assembly sequence planning (ASP) and assembly line balancing (ALB) problems are two essential problems in the assembly optimization. This paper proposes an ant colony algorithm for integrating assembly sequence planning and assembly line balancing, to deal with the two problems on parallel, and resolve the possible conflict between two optimization goals. The assembly sequence planning problem and the assembly line balancing problem are discussed, the process of the proposed ant colony algorithm is investigated. The results can provide a set of solutions for decision department in assembly planning.

Assembly and disassembly sequences were planned and assessed independently. However, as eco-friendly product design has been enforced by government regulation, it is needed to plan assembly and disassembly sequence at the same time in early product design stage. This paper aims to propose a sequence structure design method by integrating both assembly and disassembly sequence planning. First, graphs represent feasible assembly sequence with components. Second, adjacency matrices are developed to accumulate the sequence information for computational analysis. Third, a black box diagram is applied to understand and consider the functions of a product during assembly and disassembly operations. Finally, factors are determined to overcome gaps between each assembly and disassembly sequence structure. By considering product architecture with Design Structure Matrix (DSM), we can determine modular design for selective disassembly and interface type for assembly. A case study is performed with two coffee makers to demonstrate the effectiveness of the proposed method.

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.

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.