Design for Assembly Approach for Energy-Efficient Optimal Assembly Sequence Planning Using Improved Firefly Algorithm

2019 ◽  
pp. 665-675
Author(s):  
Gunji Bala Murali ◽  
B. B. V. L. Deepak ◽  
Golak Bihari Mahanta ◽  
Amruta Rout ◽  
B. B. Biswal
Keyword(s):  
Energy Efficient ◽  
Firefly Algorithm ◽  
Assembly Sequence Planning ◽  
Design For Assembly ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Improved Firefly Algorithm ◽  
Optimal Assembly

scholarly journals A case study of energy efficient assembly sequence planning problem

2019 ◽  
Vol 469 ◽  
pp. 012013 ◽  
Author(s):  
M A Abdullah ◽  
M F F Ab Rashid ◽  
Z Ghazali ◽  
A N M Rose
Keyword(s):  
Energy Efficient ◽  
Assembly Sequence Planning ◽  
Planning Problem ◽  
Assembly Sequence ◽  
Sequence Planning

Hybridized Cuckoo–Bat Algorithm for Optimal Assembly Sequence Planning

2018 ◽  
pp. 627-638
Author(s):  
Balamurali Gunji ◽  
B. B. V. L. Deepak ◽  
Amruta Rout ◽  
Golak Bihari Mohanta ◽  
B. B. Biswal
Keyword(s):  
Bat Algorithm ◽  
Assembly Sequence Planning ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Optimal Assembly

Optimal Assembly Sequence Planning Using Hybridized Immune-Simulated Annealing Technique

2017 ◽  
Vol 4 (8) ◽  
pp. 8313-8322 ◽  
Author(s):  
G. Bala Murali ◽  
B.B.V.L. Deepak ◽  
M.V.A. Raju Bahubalendruni ◽  
B.B. Biswal
Keyword(s):  
Simulated Annealing ◽  
Assembly Sequence Planning ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Optimal Assembly

scholarly journals RESEARCH ON ASSEMBLY SEQUENCE PLANNING AND OPTIMIZATION OF PRECAST CONCRETE BUILDINGS

2018 ◽  
Vol 24 (2) ◽  
pp. 106-115 ◽  
Author(s):  
Yaowu WANG ◽  
Zhenmin YUAN ◽  
Chengshuang SUN
Keyword(s):  
Precast Concrete ◽  
Complex Structure ◽  
Assembly Sequence Planning ◽  
Parametric Modelling ◽  
Assembly Sequence ◽  
Improved Genetic Algorithm ◽  
Dynamic Adjustment ◽  
Sequence Planning ◽  
Concrete Buildings ◽  
Optimal Assembly

Due to more complex structure and increasing prefabrication rate of precast concrete buildings, the assembly order between their constituent components is getting more and more attention. In order to solve the assembly sequence planning and optimization (ASPO) problem in precast concrete buildings, Building Information Modelling (BIM) and Improved Genetic Algorithm (IGA) are organically combined to propose a new method called BIM-IGA-based ASPO method. This method uses BIM for parametric modelling, uses IGA to search for an optimal assembly sequence, and then uses BIM again for visual simulation to further test the assembly sequence. Besides, IGA, which is improved in coding mode, crossover operation and mutation operation, is also used to achieve the dynamic adjustment of assembly sequence in construction process. A full-text example is used to explain the detailed operating principle of BIM-IGA-based ASPO method. The results indicate that the method can effectively find an optimal assembly sequence to reduce the assembly difficulty of a precast concrete building

scholarly journals Intelligent robot of inclined assembly sequence planning in Industrial 4.0

2018 ◽  
Vol 192 ◽  
pp. 01006
Author(s):  
Yu Cheng Chiang ◽  
Chumpol Yuangyai ◽  
Chen yang Cheng
Keyword(s):  
Local Coordinate System ◽  
Intelligent Manufacturing ◽  
Assembly Sequence Planning ◽  
Robotic Arm ◽  
Cyber Physical System ◽  
Assembly Sequence ◽  
Objective Functions ◽  
Assembly Operation ◽  
Sequence Planning ◽  
Optimal Assembly

In the industry 4.0, the Cyber-physical system (CPS) is one of the most important core which makes the manufacturing process more intelligent. Intelligent assembly operation is an important key in intelligent manufacturing of CPS. To complete the intelligent assembly operation, the cooperation between assembly robotic arm and assembly sequence planning (ASP) is necessary. However, the ASP and writing robotic codes manually is time consuming and requires professional knowledge and experience. Because the Local Coordinate System (LCS) is often ignored when checking for interference. If product have inclined interference and without considering LCS and causing and infeasible ASP. Therefore, this paper proposes a LCCPIAS (Local Coordinate Cyber-Physical Intelligent Assembly System) system to achieve three objective functions. First, this paper presents a dual-projected-based interference analysis approach (DPIAA) that analyzes the relations between components. Second, this paper generates optimal assembly sequence automatically to let the assembly sequence more suitable for the robotic arm to perform the assembly operation. The last one is LCS can recognize inclined interference between components and generate feasible ASP. Furthermore, this paper uses CAD model to verify that the DPIAA is faster and consider LCS interference can solve inclined interference problem. In the future assembly factory, the proposed method can help to realize intelligent manufacturing.

Assembly Sequence Planning Based on Connector Structure and Ant Colony Algorithm

2013 ◽  
Vol 712-715 ◽  
pp. 2482-2486 ◽  
Author(s):  
Ying Ying Su ◽  
Hai Dong ◽  
Di Liang
Keyword(s):  
Ant Colony Algorithm ◽  
Assembly Sequence Planning ◽  
Precedence Constraint ◽  
Ant Colony ◽  
Ant Algorithm ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Assembly Sequences ◽  
Assembly Unit ◽  
Optimal Assembly

For the purpose of effectively reducing the degree of complexity and improving the efficiency, the method of assembly sequence planning based on connector structure and ant algorithm was proposed. The concept of connector structure was presented, which was regarded as basic assembly unit to cover features of assembly parts. Then, a model of assembly sequence planning was built, which represented the precedence constraint relationship among connector structures. Additionally, the combination of the connector structure concept and characteristics of ant colony algorithm was developed for generating optimal assembly sequences under the guidance of precedence relations in the model. Finally, an example was studied to illustrate the effectiveness of the strategy.

A Multi-Level Genetic Assembly Planner

2000 ◽  
Author(s):  
Shiang-Fong Chen ◽  
Yong-Jin Liu
Keyword(s):  
Assembly Sequence Planning ◽  
Geometric Constraints ◽  
Control Parameters ◽  
Assembly Sequence ◽  
Low Level ◽  
Sequence Planning ◽  
Assembly Sequences ◽  
Multi Level ◽  
High Level ◽  
Optimal Assembly

Abstract 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.

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