scholarly journals Knowledge-Based Approach to Assembly Sequence Planning for Wind-Driven Generator

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Meiping Wu ◽  
Yi Zhao ◽  
Chenxin Wang
Keyword(s):  
Manufacturing Industry ◽  
Assembly Sequence Planning ◽  
Product Launch ◽  
Assembly Planning ◽  
Planning Problem ◽  
Efficient Computation ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Novel Approach ◽  
The Right

Assembly sequence planning plays an essential role in the manufacturing industry. However, there still exist some challenges for the research of assembly planning, one of which is the weakness in effective description of assembly knowledge and information. In order to reduce the computational task, this paper presents a novel approach based on engineering assembly knowledge to the assembly sequence planning problem and provides an appropriate way to express both geometric information and nongeometric knowledge. In order to increase the sequence planning efficiency, the assembly connection graph is built according to the knowledge in engineering, design, and manufacturing fields. Product semantic information model could offer much useful information for the designer to finish the assembly (process) design and make the right decision in that process. Therefore, complex and low-efficient computation in the assembly design process could be avoided. Finally, a product assembly planning example is presented to illustrate the effectiveness of the proposed approach. Initial experience with the approach indicates the potential to reduce lead times and thereby can help in completing new product launch projects on time.

Beyond Optimal Sequencing: Defining Part Orientation and Worker Allocation in Assembly

2015 ◽  
Author(s):  
Nima Rafibakhsh ◽  
Matthew I. Campbell
Keyword(s):  
Assembly Sequence Planning ◽  
Assembly Planning ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Worker Allocation ◽  
Optimal Sequencing ◽  
Novel Approach ◽  
Processing Step ◽  
Minimum Number ◽  
Part Orientation

Assembly Sequence planning is a tedious but crucial task in manufacturing a product. A good assembly plan will lead to minimum wasted time and maximum capacity of resources. Typically, research in Automated Assembly Planning and Assembly Sequence Planning (AAP and ASP) only define the sequence that the parts should be assembled with no information for specifying additional details to make the plan complete and optimal. In this paper we introduce a post-processing step (after the sequence of parts has been found) with focus on optimal part orientation and worker allocation. The paper has two main sections: the first section uses Dijkstra’s algorithm to obtain part orientation with minimum assembly cost. For the second part of the paper, a novel approach is proposed based on a line balancing technique to find the minimum number of workers needed to achieve the minimum make-span time. These necessary details in AAP give real time feedback to designers to analyze their design with production and assembly line information.

An Ant Colony Algorithm for Integrating Assembly Sequence Planning and Assembly Line Balancing

2013 ◽  
Vol 397-400 ◽  
pp. 2570-2573 ◽  
Author(s):  
Zhuo Yang ◽  
Cong Lu ◽  
Hong Wang Zhao
Keyword(s):  
Ant Colony Algorithm ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Assembly Sequence Planning ◽  
Ant Colony ◽  
Line Balancing ◽  
Assembly Planning ◽  
Planning Problem ◽  
Assembly Sequence ◽  
Sequence Planning

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.

A new discrete double-population firefly algorithm for assembly sequence planning

2016 ◽  
Vol 230 (12) ◽  
pp. 2229-2238 ◽  
Author(s):  
Zaifang Zhang ◽  
Baoxun Yuan ◽  
Zhinan Zhang
Keyword(s):  
Firefly Algorithm ◽  
Fitness Function ◽  
Assembly Sequence Planning ◽  
Movement Direction ◽  
Assembly Planning ◽  
Optimal Size ◽  
Planning Problem ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Double Population

Assembly sequence planning is a critical step of assembly planning in product digital manufacturing. It is a combinational optimization problem with strong constraints. Many studies devoted to propose intelligent algorithms for efficiently finding a good assembly sequence to reduce the manufacturing time and cost. Considering the unfavorable effects of penalty function in the traditional algorithms, a new discrete firefly algorithm is proposed based on a double-population search mechanism for the assembly sequence planning problem. The mechanism can guarantee the population diversity and enhance the local and global search capabilities by using the parallel evolution of feasible and infeasible solutions. All parts composed of the assembly are assigned as the firefly positions, and the corresponding movement direction and distance of each firefly are defined using vector operations. Three common objectives, including assembly stability, assembly polymerization and change number of assembly direction, are taken into account in the fitness function. The proposed approach is successfully applied in a real-world assembly sequence planning case. The sizes of feasible and infeasible populations are adequately discussed and compared, of which the optimal size combination is used for initializing the firefly algorithm. The application results validate the feasibility and effectiveness of the discrete double-population firefly algorithm for solving assembly sequence planning problem.

Modelling of assembly sequence planning problem using base part concept

2018 ◽  
Author(s):  
M. A. Abdullah ◽  
M. F. F. Ab Rashid ◽  
Z. Ghazalli ◽  
N. M. Z. Nik Mohamed ◽  
A. N. Mohd Rose
Keyword(s):  
Assembly Sequence Planning ◽  
Planning Problem ◽  
Assembly Sequence ◽  
Sequence Planning

A hybrid Ant-Wolf Algorithm to optimize assembly sequence planning problem

2017 ◽  
Vol 37 (2) ◽  
pp. 238-248 ◽  
Author(s):  
Mohd Fadzil Faisae Ab Rashid
Keyword(s):  
Assembly Sequence Planning ◽  
Search Direction ◽  
Local Optimum ◽  
Planning Problem ◽  
Gray Wolf ◽  
Assembly Sequence ◽  
Algorithm Performance ◽  
Content Type ◽  
Sequence Planning ◽  
Updating Procedure

Purpose This paper aims to optimize the assembly sequence planning (ASP) problem using a proposed hybrid algorithm based on Ant Colony Optimization (ACO) and Gray Wolf Optimizer (GWO). The proposed Hybrid Ant-Wolf Algorithm (HAWA) is designed to overcome premature convergence in ACO. Design/methodology/approach The ASP problem is formulated by using task-based representation. The HAWA adopts a global pheromone-updating procedure using the leadership hierarchy concept from the GWO into the ACO to enhance the algorithm performance. In GWO, three leaders are assigned to guide the search direction, instead of a single leader in most of the metaheuristic algorithms. Three assembly case studies used to test the algorithm performance. Findings The proposed HAWA performed better in comparison to the Genetic Algorithm, ACO and GWO because of the balance between exploration and exploitation. The best solution guides the search direction, while the neighboring solutions from leadership hierarchy concept avoid the algorithm trapped in a local optimum. Originality/value The originality of this research is on the proposed HAWA. In addition to the standard pheromone-updating procedure, a global pheromone-updating procedure is introduced, which adopted leadership hierarchy concept from GWO.

Assembly Modeling and Sequence Planning Based on the Kinematics of Features

1996 ◽  
Author(s):  
Zhi-Kui Ling
Keyword(s):  
Degrees Of Freedom ◽  
Planning Process ◽  
Assembly Sequence Planning ◽  
Degree Of Freedom ◽  
Assembly Planning ◽  
Assembly Sequence ◽  
Assembly Modeling ◽  
Sequence Planning ◽  
Local Degree ◽  
Modeling Strategy

Abstract A feature-based model is proposed for assembly sequence planning automation. The fundamental assembly modeling strategy for a product is based on the mating features of its components. The objectives of this study are to integrate assembly planning of a product with its CAD model, generate a correct and practical assembly sequence, and establish a software system to carry out the planning process. A disassembly approach in assembly planning is used in this study. The degree of freedom information between two mating features is used to characterize their kinematic conditions. The intersection calculation of the degrees of freedom on all features of a component provides its local degree of freedom which is used to set up functional precedence relationship. In some cases where functional precedence relationship can not be detected by geometric reasoning, clipping of the known “common sense” relationship is applied by an user. A bounding box checking approach is used to ensure no global collision during assembly. Furthermore, a set of criteria and heuristic rules based on assembly feasibility, manipulability, assembly direction, cost and stability is used to choose a good assembly sequence.

Assembly Sequence Planning Based on Max-Min Ant Colony System

2013 ◽  
Vol 284-287 ◽  
pp. 2220-2227 ◽  
Author(s):  
Jia Peng Yu ◽  
Cheng En Wang
Keyword(s):  
Performance Appraisal ◽  
Assembly Sequence Planning ◽  
Ant Colony ◽  
Planning System ◽  
Assembly Planning ◽  
Group Method ◽  
Ant Colony System ◽  
Assembly Sequence ◽  
Ant System ◽  
Sequence Planning

An assembly sequence planning (ASP) method that combined the advantages of ant colony system (ACS) and Max-Min ant system (MMAS) is proposed. To identify the best sequence easily, five optimization criterions are automatically quantified, including reorientation, parallelism, continuity, stability and auxiliary stroke, and then integrated into the multi-objective heuristic and fitness functions of ant colony optimization (ACO). To improve the search capability for the global-best sequence, several measures are presented from aspects of determining number of ant, max-min pheromone limits, performance appraisal for initial components allocation and the group method for same components. The ASP algorithm based on Max-Min ant colony system (MMACS) is proposed. An assembly planning system AutoAssem is developed based on Siemens NX platform, and the actual effectiveness of each optimization measure is testified through case study of a valve.

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