Automated Initial-Population Generation for Genetic-Algorithm-Based Assembly Planning

2000 ◽  
Author(s):  
Gregory C. Smith ◽  
Shiang-Fong Chen
Keyword(s):  
Genetic Algorithm ◽  
State Of The Art ◽  
Assembly Planning ◽  
Initial Population ◽  
Search Efficiency ◽  
Assembly Sequence ◽  
Assembly Sequences ◽  
Component Assembly ◽  
Product Assembly ◽  
Initial Assembly

Abstract Genetic algorithms show particular promise for automated assembly planning. As a result, several recent research reports present genetic-algorithm-based mechanical-product assembly planners. However, genetic-algorithm-based assembly planners require an initial assembly-sequence population, and search efficiency greatly depends upon input-population quality. State-of-the-art genetic-algorithm-based assembly planners use one of two techniques for generating an initial assembly-sequence population: use a user-supplied assembly-sequence set or use a randomly generated assembly-sequence set. Generating a user-supplied initial population requires a substantial amount of manpower. Using a randomly generated initial population reduces search efficiency. As a result, we propose an algorithm for automatically generating an initial assembly-sequence population. Our algorithm calculates component assembly complexity and uses both component assembly complexity and component connectivity to automatically generate a valid assembly-sequence population. Using automatically generated initial populations, we achieve search efficiencies comparable to search efficiencies achieved when using user-supplied initial assembly-sequence populations, while eliminating manpower required to generate user-supplied assembly sequences.

Assembly Sequence Generation and Optimization for Compliant Assemblies Based on Genetic Algorithm

2011 ◽  
Vol 230-232 ◽  
pp. 978-981
Author(s):  
Yan Feng Xing ◽  
Yan Song Wang ◽  
Xiao Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Initial Population ◽  
Assembly Sequence ◽  
Constraint Function ◽  
Assembly Modeling ◽  
Sequence Generation ◽  
Assembly Sequences ◽  
Assembly Sequence Generation ◽  
Crossover And Mutation

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.

Structural Analysis of Product and Computer-Aided Assembly Planning in AssemBL Software Package

2018 ◽  
pp. 11-33
Author(s):  
A. N. Bozhko
Keyword(s):  
Computer Aided Design ◽  
State Of The Art ◽  
Graph Model ◽  
Design System ◽  
Assembly Planning ◽  
Complex Products ◽  
Assembly Sequences ◽  
Computer Aided ◽  
Cad Cam ◽  
Aided Design

Computer-aided design of assembly processes (Computer aided assembly planning, CAAP) of complex products is an important and urgent problem of state-of-the-art information technologies. Intensive research on CAAP has been underway since the 1980s. Meanwhile, specialized design systems were created to provide synthesis of assembly plans and product decompositions into assembly units. Such systems as ASPE, RAPID, XAP / 1, FLAPS, Archimedes, PRELEIDES, HAP, etc. can be given, as an example. These experimental developments did not get widespread use in industry, since they are based on the models of products with limited adequacy and require an expert’s active involvement in preparing initial information. The design tools for the state-of-the-art full-featured CAD/CAM systems (Siemens NX, Dassault CATIA and PTC Creo Elements / Pro), which are designed to provide CAAP, mainly take into account the geometric constraints that the design imposes on design solutions. These systems often synthesize technologically incorrect assembly sequences in which known technological heuristics are violated, for example orderliness in accuracy, consistency with the system of dimension chains, etc.An AssemBL software application package has been developed for a structured analysis of products and a synthesis of assembly plans and decompositions. The AssemBL uses a hyper-graph model of a product that correctly describes coherent and sequential assembly operations and processes. In terms of the hyper-graph model, an assembly operation is described as shrinkage of edge, an assembly plan is a sequence of shrinkages that converts a hyper-graph into the point, and a decomposition of product into assembly units is a hyper-graph partition into sub-graphs.The AssemBL solves the problem of minimizing the number of direct checks for geometric solvability when assembling complex products. This task is posed as a plus-sum two-person game of bicoloured brushing of an ordered set. In the paradigm of this model, the brushing operation is to check a certain structured fragment for solvability by collision detection methods. A rational brushing strategy minimizes the number of such checks.The package is integrated into the Siemens NX 10.0 computer-aided design system. This solution allowed us to combine specialized AssemBL tools with a developed toolkit of one of the most powerful and popular integrated CAD/CAM /CAE systems.

Intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Hongjuan Yang ◽  
Jiwen Chen ◽  
Chen Wang ◽  
Jiajia Cui ◽  
Wensheng Wei
Keyword(s):  
Prior Knowledge ◽  
Semantic Knowledge ◽  
Knowledge System ◽  
Cad Model ◽  
Assembly Sequence ◽  
Content Type ◽  
Assembly Sequences ◽  
Spatio Temporal ◽  
Product Assembly ◽  
Intelligent Planning

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.

A pragmatic approach to interactive assembly sequence evaluation

2003 ◽  
Vol 217 (4) ◽  
pp. 541-550 ◽  
Author(s):  
C J Barnes ◽  
G E M Jared ◽  
K G Swift
Keyword(s):  
Relevant Literature ◽  
Stability Index ◽  
Design System ◽  
Assembly Planning ◽  
Assembly Sequence ◽  
Industrial Case Study ◽  
Assembly Sequences ◽  
Product And Process ◽  
Difficulty Index

An assembly-oriented design system has been developed which includes several analysis tools to improve product assemblability during product development. One of these tools supports the parallel development of the product design and the assembly sequence, thus exploiting the benefits of concurrent consideration of product and process. However, this approach requires some method for evaluating the sequence against requirements. Previous work on assembly sequence evaluation has concentrated on identifying the best from a set of ranked alternatives. When a single sequence is constructed, as with this tool, another method is needed. This paper reports the development of this novel methodology for evaluating individual assembly sequences. A review of the relevant literature has found several measures for identifying good assembly sequences from a ranked list and the fundamental sequence attributes extrapolated and aggregated. This leads to the proposal of four new indices: insertion index, stability index, difficulty index and complexity index. A large number of assembly sequences have been analysed to define limiting values for the indices such that they can quantify the potential of an incomplete sequence resulting in a satisfactory solution. The application of these indices in concurrent design and assembly planning is illustrated through an industrial case study.

CAD Model Assembly Hierarchy Reorganization for Application in Virtual Assembly: A Hybrid Approach Using the CAD System and a Visualization Tool

2003 ◽  
Author(s):  
YoungJun Kim ◽  
Uma Jayaram ◽  
Sankar Jayaram ◽  
Venkata K. Jandhyala ◽  
Tatsuki Mitsui
Keyword(s):  
Hybrid Method ◽  
Hybrid Approach ◽  
Virtual Assembly ◽  
Assembly Planning ◽  
Visualization Tool ◽  
Assembly Model ◽  
Cad System ◽  
Assembly Sequences ◽  
Component Assembly ◽  
Time Required

The hierarchy of assembly components in a CAD assembly model is rarely a true representation of the sequence of assembly of these components during manufacturing. Thus, any assembly planning or evaluation software system needs to re-order and re-group the various components of the CAD assembly model to reflect the sequence of component assembly. Although all parametric CAD systems allow reorganization of the assembly tree, it is a difficult and timeconsuming process due to the relationships and constraints between the various components. We propose an alternative hybrid method that couples the CAD system and a visualization tool that supports reorganization, while preserving data, to allow fast and easy rearranging of the assembly hierarchy. Also, after the reorganization, polygonal representations of the new sub-assemblies are created and the original constraints are also transformed in a consistent manner. As a next logical step, we compare the time required to rearrange the assembly hierarchy using both methods — the CAD system alone and the hybrid system. A statistical analysis using three treatment factors indicates that if the number of components is more than 15, then it is more efficient to use the hybrid method over the CAD system. The overarching goal was to allow fast and efficient creation of different assembly hierarchies to allow the corresponding assembly sequences to be verified in a virtual assembly application that derives its models and constraints from the assembly hierarchy in the CAD system. We have implemented the method to allow the successful reorganization and virtual assembly verification of many industry models, some with several hundred components, provided by various industry partners.

A Methodology for Assembly Sequence Optimization by Hybrid Cuckoo-Search Genetic Algorithm

2018 ◽  
Vol 17 (01) ◽  
pp. 47-59 ◽  
Author(s):  
G. V. S. K. Karthik ◽  
Sankha Deb
Keyword(s):  
Genetic Algorithm ◽  
Cuckoo Search ◽  
Metaheuristic Algorithm ◽  
Assembly Sequence ◽  
Sequence Optimization ◽  
Assembly Sequences ◽  
Ga Algorithm ◽  
The Cost ◽  
The Given ◽  
Optimal Assembly

In this paper, we have proposed and implemented a methodology for assembly sequence optimization by using a nature-inspired metaheuristic algorithm, known as hybrid cuckoo-search genetic algorithm (CSGA). The cost criteria for optimization in the present formulation takes into consideration the total assembly time and the number of reorientations during the assembly process. To demonstrate the application of the CSGA, an example assembly containing 19 parts has been presented and the results have been compared with those of another metaheuristic algorithm, Genetic Algorithm (GA). From the results, it has been observed that for the given problem, the CSGA not only produces optimal assembly sequences with costs comparable to that of GA, but the convergence of CSGA algorithm has been found to be faster than the GA algorithm.

Genetic Algorithm for Assembly Sequences Planning Based on Heuristic Assembly Knowledge

2010 ◽  
Vol 44-47 ◽  
pp. 3657-3661 ◽  
Author(s):  
Hao Pan ◽  
Wen Jun Hou ◽  
Tie Meng Li
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Graph Model ◽  
Initial Population ◽  
New Approach ◽  
Assembly Sequences ◽  
Connection Graph ◽  
Assembly Sequences Planning

To improve the efficiency of Assembly Sequences Planning (ASP), a new approach based on heuristic assembly knowledge and genetic algorithm was proposed. First, Connection Graph of Assembly (CGA) was introduced, and then, assembly knowledge was described in the form of Assembly Rings, on that basis, the assembly connection graph model containing Assembly Rings was defined, and the formation of initial population algorithm was given. In addition, a function was designed to measure the feasible assembly and then the genetic algorithm fitness function was given. Finally, an example was shown to illustrate the effectiveness of the algorithm.

Assembly Process Planning Method for Complex Electromechanical Products

2014 ◽  
Vol 488-489 ◽  
pp. 1260-1263
Author(s):  
Bo Sun ◽  
Lu Liu
Keyword(s):  
Genetic Algorithm ◽  
Process Planning ◽  
Ant Colony Algorithm ◽  
Planning Method ◽  
Assembly Process ◽  
Assembly Sequence ◽  
Constraint Matrix ◽  
Assembly Sequences ◽  
Electromechanical Products ◽  
Novel Method

Currently, neither the efficiency nor the effectiveness is sufficient in the area of the assemble optimization that commonly involves the genetic algorithm. A novel method to solve the cumbersome problem in the optimization of assembly sequences was proposed. On the basis of the assembly constraint matrix, the optimized assembly sequence is obtained with the proposed evaluating factors of the process requirement. That is the evolution of the original genetic algorithm to a certain extent. The effectiveness of the proposed method was proved by the comparison with the ant colony algorithm.

scholarly journals Optimum Assembly Sequence Planning System Using Discrete Artificial Bee Colony Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Özkan Özmen ◽  
Turgay Batbat ◽  
Tolgan Özen ◽  
Cem Sinanoğlu ◽  
Ayşegül Güven
Keyword(s):  
Computer Aided Design ◽  
Artificial Bee Colony ◽  
Three Dimensional ◽  
Assembly Sequence Planning ◽  
Planning System ◽  
Assembly Planning ◽  
Assembly Sequence ◽  
Bee Colony ◽  
Assembly Sequences ◽  
The Matrix

Assembly refers both to the process of combining parts to create a structure and to the product resulting therefrom. The complexity of this process increases with the number of pieces in the assembly. This paper presents the assembly planning system design (APSD) program, a computer program developed based on a matrix-based approach and the discrete artificial bee colony (DABC) algorithm, which determines the optimum assembly sequence among numerous feasible assembly sequences (FAS). Specifically, the assembly sequences of three-dimensional (3D) parts prepared in the computer-aided design (CAD) software AutoCAD are first coded using the matrix-based methodology and the resulting FAS are assessed and the optimum assembly sequence is selected according to the assembly time optimisation criterion using DABC. The results of comparison of the performance of the proposed method with other methods proposed in the literature verify its superiority in finding the sequence with the lowest overall time. Further, examination of the results of application of APSD to assemblies consisting of parts in different numbers and shapes shows that it can select the optimum sequence from among hundreds of FAS.

Research of Alloy Orb on a New Immune Genetic Algorithm

2013 ◽  
Vol 710 ◽  
pp. 735-738
Author(s):  
Shu Zhang
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Evolutionary Process ◽  
Search Space ◽  
Dynamic Environments ◽  
Initial Population ◽  
Search Efficiency ◽  
Immune Genetic Algorithm ◽  
Multimodal Function ◽  
Standard Genetic Algorithm

The standard Genetic Algorithm has several limitations when dealing with dynamic environments. The most harmful limitation as to do with the tendency for the large majority of the members of a population to convergence prematurely to a particular region of the search space, making thus difficult for the GA to find other solutions when changes in the environment occur. In order to avoid the premature convergence and the slow search efficiency at posterior evolutionary process of the traditional immune genetic algorithm, a new immune genetic algorithm is proposed. This algorithm uses orthogonal crossover to generate initial population and uses elitist-crossover to increase the good patterns of the population and uses hybrid mutation to increase the ability of local and global optimization. It has shown fascinating results when being used in the optimization of multimodal function.

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