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1 – 10 of over 5000
Article
Publication date: 2 October 2017

Jifeng Guo, Chengchao Bai and Cheng Chen

In the future, large space truss structures will be likely to require on-orbit assembly. One of the several proposed methods includes cooperative assembly performed by…

Abstract

Purpose

In the future, large space truss structures will be likely to require on-orbit assembly. One of the several proposed methods includes cooperative assembly performed by pressure-suited astronauts during extravehicular activity (EVA) and space robots. An intelligent planning method was presented to generate optimal assembly tasks.

Design/methodology/approach

Firstly, the inherent hierarchical nature of truss structures allows assembly sequences to be considered from strut level and structural volume element (SVE) level. Then, a serial assembly strategy in human-robot environment was applied. Furthermore, a two-level planning algorithm was presented. At the first-level planning, one ant colony algorithm for assembly sequence planning was improved to adopt assembly direction and time as heuristic information and did not consider assembly tasks. And, at the second-level planning, another novel colony algorithm for assembly task planning mainly considered results of the first-level planning, human-robot interactive information, serial assembly strategy and assembly task distributions.

Findings

The proposed two-level planning algorithm is very effective for solving the human and robot cooperative assembly of large space truss structures.

Research limitations/implications

In this paper, the case study is based on the following assumptions: each tetrahedron is assembled by two astronauts; each pentahedron is assembled by three astronauts.

Practical implications

A case illustrates the results of the two-level planning. From this case study, because of geometrical symmetry nature of large space truss structures, the optimal assembly sequences are not only one.

Originality/value

The improved ant colony algorithm can deal with the assembly sequence and task planning in human-robot environment more effectively.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 6
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 14 July 2020

Hongjuan Yang, Jiwen Chen, Chen Wang, Jiajia Cui and Wensheng Wei

The implied assembly constraints of a computer-aided design (CAD) model (e.g. hierarchical constraints, geometric constraints and topological constraints) represent an…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Details

Assembly Automation, vol. 40 no. 5
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 1 March 2005

Cem Sinanoğlu and H. Rıza Börklü

In this paper, an assembly sequence planning system, based on binary vector representations, is developed. The neural network approach has been employed for analyzing…

1587

Abstract

Purpose

In this paper, an assembly sequence planning system, based on binary vector representations, is developed. The neural network approach has been employed for analyzing optimum assembly sequence for assembly systems.

Design/methodology/approach

The input to the assembly system is the assembly's connection graph that represents parts and relations between these parts. The output to the system is the optimum assembly sequence. In the constitution of assembly's connection graph, a different approach employing contact matrices and Boolean operators has been used. Moreover, the neural network approach is used in the determination of optimum assembly sequence. The inputs to the networks are the collection of assembly sequence data. This data is used to train the network using the back propagation (BP) algorithm.

Findings

The proposed neural network model outperforms the available assembly sequenceplanning model in predicting the optimum assembly sequence for mechanical parts. Due to the parallel structure and fast learning of neural network, this kind of algorithm will be utilized to model another types of assembly systems.

Research limitations/implications

In the proposed neural approach, the back propagation algorithm is used. Various training algorithms can be employed.

Practical implications

The simulation results suggest that the neural predictor would be used as a predictor for possible practical applications on modeling assembly sequence planning system.

Originality/value

This paper discusses a new modelling scheme known as artificial neural networks. The neural network approach has been employed for analyzing feasible assembly sequences and optimum assembly sequence for assembly systems.

Details

Assembly Automation, vol. 25 no. 1
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 20 December 2017

Weiwei Wan, Kensuke Harada and Kazuyuki Nagata

The purpose of this paper is to develop a planner for finding an optimal assembly sequence for robots to assemble objects. Each manipulated object in the optimal sequence

Abstract

Purpose

The purpose of this paper is to develop a planner for finding an optimal assembly sequence for robots to assemble objects. Each manipulated object in the optimal sequence is stable during assembly. They are easy to grasp and robust to motion uncertainty.

Design/methodology/approach

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, in which order should one assemble the objects, from which directions should the objects be dropped and 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.

Findings

The proposed planner could plan an optimal sequence to guide robots to do assembly using translational motion. The sequence provides initial and goal configurations to motion planning algorithms and is ready to be used by robots. The usefulness of the proposed method is verified by both simulation and real-world executions.

Originality/value

The paper proposes an assembly planner which can find an optimal assembly sequence automatically without teaching of the assembly orders and directions by skilled human technicians. The planner is highly expected to improve teachingless robotic manufacturing.

Details

Assembly Automation, vol. 38 no. 2
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 8 October 2018

Atul Mishra and Sankha Deb

Assembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization…

Abstract

Purpose

Assembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria. Applications of evolutionary algorithms have shown a lot of promise in terms of lower computational cost and time. But there remain challenges like achieving global optimum in least number of iterations with fast convergence speed, robustness/consistency in finding global optimum, etc. With the above challenges in mind, this study aims to propose an improved flower pollination algorithm (FPA) and hybrid genetic algorithm (GA)-FPA.

Design/methodology/approach

In view of slower convergence rate and more computational time required by the previous discrete FPA, this paper presents an improved hybrid FPA with different representation scheme, initial population generation strategy and modifications in local and global pollination rules. Different optimization objectives are considered like direction changes, tool changes, assembly stability, base component location and feasibility. The parameter settings of hybrid GA-FPA are also discussed.

Findings

The results, when compared with previous discrete FPA and GA, memetic algorithm (MA), harmony search and improved FPA (IFPA), the proposed hybrid GA-FPA gives promising results with respect to higher global best fitness and higher average fitness, faster convergence (especially from the previously developed variant of FPA) and most importantly improved robustness/consistency in generating global optimum solutions.

Practical implications

It is anticipated that using the proposed approach, assembly sequence planning can be accomplished efficiently and consistently with reduced lead time for process planning, making it cost-effective for industrial applications.

Originality/value

Different representation schemes, initial population generation strategy and modifications in local and global pollination rules are introduced in the IFPA. Moreover, hybridization with GA is proposed to improve convergence speed and robustness/consistency in finding globally optimal solutions.

Details

Assembly Automation, vol. 39 no. 1
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 6 August 2018

Remigiusz Romuald Iwańkowicz and Michał Taraska

The purpose of the paper is to develop a method of automatic classification of the components of the assembly units. The method is crucial for developing an automatic ship…

Abstract

Purpose

The purpose of the paper is to develop a method of automatic classification of the components of the assembly units. The method is crucial for developing an automatic ship assembly planning tools. The proposed method takes into account the assumptions specific for shipbuilding technology processes: high complexity of structures, difficult expert-based classification of components, fixed priority relations between connections resulting from geometrical constraints and demands of welding processes.

Design/methodology/approach

The set of ex post determined liaisons and assembly sequences constitutes the database of structures which have been made-up earlier. The components classification problem is solved using matrix coding of graphs. Information in such form is stored in the database. The minimization of number of cycles in the graph of classes sequence and minimization of diversity of classes within all constructions has been proposed as criteria of optimization. The genetic algorithm has been proposed as a solution method.

Findings

The proposed method solves the problem of components’ classifications. It allows setting the pattern of priorities between classes of various connections. This gives a chance to determine the relationship constraints between the connections of new structures for which assembly sequences are not established.

Research limitations/implications

Mathematical formulation of the database is quite laborious. The possibility of partial automation of this process should be considered. Owing to the complexity of the problem, a relatively simple objective function has been proposed. During a ship hull assembly, additional criteria should be taken into account, what will be the direction of further research.

Practical implications

Automatic classification of components is dedicated for implementation in shipyards and similar assembly systems. Tests performed by the authors confirm efficiency of presented method in supporting management of the database and assembly of new structures planning. Suggested activity-oriented approach allows for easy conversion of any assembly unit structure to the form of a matrix.

Originality/value

The new approach for components classification according to its assembly features distinguishes the proposed method from others. The use of nilpotent matrix theory in an acyclicity of graphs analysis is also a unique achievement. Original crossover and mutation operators for assembly sequence were proposed in the article.

Details

Assembly Automation, vol. 38 no. 3
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 1 February 2016

Remigiusz Romuald Iwankowicz

The purpose of this paper is to develop the method of generating assembly sequences, which can be used in the shipbuilding industry. The method must take into account the…

Abstract

Purpose

The purpose of this paper is to develop the method of generating assembly sequences, which can be used in the shipbuilding industry. The method must take into account the assumptions specific for assembly processes of large-size steel ship hulls, among others, a large number of connections, multi-stage and parallel assembly, set priority relations between connections.

Design/methodology/approach

The assembly sequence is presented as a directed acyclic graph, whose vertices are mutually uniquely assigned to connections on a hull structure. The minimization of the number of unmet priority precedence of performing connections has been proposed as a criterion of optimization. The genetic algorithm has been proposed as a method to solve problems.

Findings

The proposed method allows to model the acyclic assembly process of welded structures and find solutions minimizing the objective function even for very complex problems. Because of this, the method has a chance to be used in shipbuilding.

Research limitations/implications

Mathematical formulation of priority assumptions is quite laborious. The possibility of partial automation of this process should be considered. Due to the complexity of the problem, a relatively simplified objective function has been proposed. In assembling a hull, additional criteria should be taken into account. It is the direction of further research.

Practical implications

The method can be successfully used in shipbuilding and in planning the production of other steel welded structures, among others, tanks, components of bridges, offshore structures. Examples of calculations were performed on an actual structure of a hull fragment.

Originality/value

A new way of coding the acyclic serial-parallel sequence was designed. The proposed method allows to analyse the sequence using the graph theory. Original, two-part crossover and mutation operators for assembling sequence were proposed.

Details

Assembly Automation, vol. 36 no. 1
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 26 September 2008

Kai‐Fu Zhang, Hui Cheng and Yuan Li

Complex products, such as aircrafts and ships, are assembled from many parts and there are many available assembly sequences. Selecting the best from the available assembly

Abstract

Purpose

Complex products, such as aircrafts and ships, are assembled from many parts and there are many available assembly sequences. Selecting the best from the available assembly sequences is challenging because of many factors, such as assembly performance, assemblability, assembly cost, assembly quality and assembly time. The purpose of this paper is to investigate a new and efficient algorithm aimed at this goal.

Design/methodology/approach

A new and efficient algorithm evaluating assembly sequences based on multi‐objective harmonious colony‐decision method is presented. This algorithm mainly includes three key steps: first, presenting the priority relationship between assembly sequences and the coefficient matrix for these objectives: assembly performance, assemblability, assembly cost, assembly quality, assembly time, and so on by several experts; second, calculating the maximum of harmonious values and harmonious priority value; third, if the maximum of harmonious priority value is not negative, the algorithm ends. Then the priority relationships of assembly sequences are obtained and the optimal assembly sequence can be selected.

Findings

This algorithm can efficiently support several experts to evaluate assembly sequences according to plenty of evaluation objectives and then to output a harmonious and recognized priority relationship of assembly sequences.

Practical implications

The algorithm is applied successfully to evaluate assembly sequences and select the optimal assembly sequence for component of aircraft's wing with fixtures.

Originality/value

This algorithm provides a new method to synthetically evaluate assembly sequences for complex product according to multi‐objectives.

Details

Assembly Automation, vol. 28 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 31 July 2009

Hongbo Shan, Shenhua Zhou and Zhihong Sun

The purpose of this paper is to propose a novel method under the name of genetic simulated annealing algorithm (GSAA) and ant colony optimization (ACO) algorithm for…

Abstract

Purpose

The purpose of this paper is to propose a novel method under the name of genetic simulated annealing algorithm (GSAA) and ant colony optimization (ACO) algorithm for assembly sequence planning (ASP) which is possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.

Design/methodology/approach

Based on the genetic algorithm (GA), simulated annealing, and ACO algorithm, the GSAA are put forward. A case study is presented to validate the proposed method.

Findings

This GSAA has better optimization performance and robustness. The degree of dependence on the initial assembly sequence about GSAA is decreased. The optimization assembly sequence still can be obtained even if the assembly sequences of initial population are infeasible. By combining GA and simulated annealing (SA), the efficiency of searching and the quality of solution of GSAA is improved. As for the presented ACO algorithm, the searching speed is further increased.

Originality/value

Traditionally, GA heavily depends on the choosing original sequence, which can result in early convergence in iterative operation, lower searching efficiency in evolutionary process, and non‐optimization of final result for global variable. Similarly, SA algorithms may generate a great deal of infeasible solutions in the evolution process by generating new sequences through exchanging position of the randomly selected two parts, which results in inefficiency of the solution‐searching process. In this paper, the proposed GSAA and ACO algorithm for ASP are possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.

Details

Assembly Automation, vol. 29 no. 3
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 23 August 2019

Minghui Zhao, Xian Guo, Xuebo Zhang, Yongchun Fang and Yongsheng Ou

This paper aims to automatically plan sequence for complex assembly products and improve assembly efficiency.

310

Abstract

Purpose

This paper aims to automatically plan sequence for complex assembly products and improve assembly efficiency.

Design/methodology/approach

An assembly sequence planning system for workpieces (ASPW) based on deep reinforcement learning is proposed in this paper. However, there exist enormous challenges for using DRL to this problem due to the sparse reward and the lack of training environment. In this paper, a novel ASPW-DQN algorithm is proposed and a training platform is built to overcome these challenges.

Findings

The system can get a good decision-making result and a generalized model suitable for other assembly problems. The experiments conducted in Gazebo show good results and great potential of this approach.

Originality/value

The proposed ASPW-DQN unites the curriculum learning and parameter transfer, which can avoid the explosive growth of assembly relations and improve system efficiency. It is combined with realistic physics simulation engine Gazebo to provide required training environment. Additionally with the effect of deep neural networks, the result can be easily applied to other similar tasks.

Details

Assembly Automation, vol. 40 no. 1
Type: Research Article
ISSN: 0144-5154

Keywords

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