Papers by Keyword: Assembly Sequence

Abstract: If a product has more than one component, then it must be assembled. The complexity of assembling a product is often subject to the number of assembly components that lead to possible assembly sequences in various forms. Also, it is widely understood that efficiency of assembling a product by reducing assembly times (therefore costs) is vital particularly for small manufacturing companies to survive in an increasingly competitive market. Ideally, it is helpful for determining an optimal assembly sequence of a product at the early design stage. Nevertheless, it may find inefficient using the heuristic approaches in acquisition of a quick solution in terms of an optimal assembly sequence with a minimal assembly time. By contrast, the study indicates that the genetic algorithm (GA) can be used as a cost-effective way for solving an assembly sequence optimisation problem of a product. This paper presents an investigation into a GA used for solving an assembly sequence problem of a ball pen. The study demonstrates that it can provide a quick solution in obtaining an optimal or near-optimal assembly sequence of the product for a small-sized company.

Abstract: 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.

Abstract: Based on the parallelism of actual assembly sequence, a unified algorithm -"Optimal order reference set" is proposed for assembly performance membership degree calculation. An evaluation index system is established according to the requirements of practical assembly process. By using analytic hierarchy process for comprehensive evaluation of indexes, concrete application steps are verified through an example of a worm gear subassembly. Practical application shows that the new method satisfied the requirements of the integrity of index system and the parallelism of practical assembly sequence.

Abstract: In order to evaluate the assembly precision of the CNC machine tools in different assembly sequences more accurately and effectively, this paper proposes a new assembly sequence deviation model and quality evaluation approach considering characteristic parameters of glued joint. A BP neural network model is established for predicting characteristic parameters of glued joint in each assembly step. The method is able to obtain the assembly sequence with best assembly quality and improve the design tolerance of parts.

Abstract: The purposed research joins back-propagation neural network (BPNN) algorithm, using NX/KF second development module to create feasible assembly sequences KBE system access the volume, weight, feature, relationship number and total penalty value, and predict the feasible assembly sequence via a robust BPNN engine.

Abstract: Using the second development tooling of NX/KF, joining the KBE system and back-propagation neural network (BPNN) algorithm, it can access the volume, weight, feature number and input the related parameters (contacting relationship number and total penalty value), predict the feasible assembly sequence, reduce assembly working time and production cost.

Abstract: In product assembly, optimized sequence is a prerequisite for automated systems. The assembly process can be optimized through appropriate selection and allocation of the given tasks in a multi-device framework. These two discrete tasks need to be integrated to produce the optimum result and a cost effective system to cope with the needs of the system, the present work attempts to generate an automatic assembly sequence and seeks for optimal allocation of tasks amongst the available robots. Further, an effective task allocation approach considers the capabilities of the deployable robots. This paper presents an integrated approach for assembly sequence generation and task allocation for multi-robot systems by considering their capability in terms of time and space. An example of a 21 part drive assembly is given to illustrate the concept and procedure of the proposed methodology.

Abstract: This study is a concentrated at the strategy of repeats in DNA sequence assembly. To deal with the sequences with repeats is a difficult problem in DNA sequence assembly. On basis of strategy learning about masking repeats，this study proposes a method based on mate-pair analysis, and a developing strategy on Amos platform.

Abstract: This paper proposes a genetic algorithm to generate and optimize assembly sequences for compliant assemblies. An assembly modeling is presented to describe the geometry of the assembly, which includes three sets of parts, relationships and joints among the parts. Based on the assembly modeling, an assembly sequence is denoted as an individual, which is assigned an evaluation function that consists of the fitness and constraint functions. The fitness function is used to evaluate feasible sequences; in addition, the constraint function is employed to evolve unfeasible sequences. The genetic algorithm starts with a randomly initial population of chromosomes, evolves new populations by using reproduction, crossover and mutation operations, and terminates until acceptable sequences output. Finally an auto-body side assembly is used to illustrate the algorithm of assembly sequence generation and optimization.

Abstract: A particle swarm algorithm is proposed to generate optimal assembly sequences for compliant assemblies. Firstly, the liaison graph and the adjacency matrix describe the geometry of the compliant assemblies. An assembly sequence is represented by a character string, whose length is the number of all parts. The conceptual tolerance analysis is used to evaluate feasible sequences. Thereafter, the particle swarm algorithm is presented to generate assembly sequences, in which the elite ratio is applied to improve optimization results. Finally a fender assembly is used to illustrate the algorithm of assembly sequence generation and optimization.