A genetic-algorithm-based approach to the generation of robotic assembly sequences

Hong, Dae Sun; Cho, H.S.

doi:10.1016/s0967-0661(98)00177-4

Cited by 41 publications

(17 citation statements)

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“…In this method, a candidate solution is represented as an individual with a set of properties called chromosomes, and the group of individuals is called a population. The population evolves to the next generation through crossover and mutation with the two-point crossover method adopted, the mutation operates on the offspring created in the crossover step based on set probability [32]. After the reproductive operations, the fitness of the offspring is assessed and compared against the parents.…”

Section: The Genetic Algorithmmentioning

confidence: 99%

Optimisation process for robotic assembly of electronic components

Giannetti

2018

Int J Adv Manuf Technol

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Adoption of robots in the manufacturing environment is a way to improve productivity, and the assembly of electronic components has benefited from the adoption of highly dedicated automation equipment. Traditionally, articulated 6-axis robots have not been used in electronic surface mount assembly. However, the need for more flexible production systems that can be used for low to medium production builds means that these robots can be used due to their high degrees of flexibility, excellent repeatability and increasingly lower investment costs. This research investigated the application of an articulated robot with six degrees of freedom to assemble a multi-component printed circuit board (PCB) for an electronic product. A heuristic methodology using a genetic algorithm was used to plan the optimal sequence and identify the best location of the parts to the assembly positions on the PCB. Using the optimised paths, a condition monitoring method for cycle time evaluation was conducted using a KUKA KR16 assembly cell together with four different robot path motions. The genetic algorithm approach together with different assembly position iterations identified an optimisation method for improved production throughput using a non-traditional but highly flexible assembly method. The application of optimised articulated robots for PCB assembly can bridge the gap between manual assembly and the high-throughput automation equipment.

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Section: The Genetic Algorithmmentioning

confidence: 99%

Optimisation process for robotic assembly of electronic components

Giannetti

2018

Int J Adv Manuf Technol

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“…Sebaaly and Fujimoto (1996) presented a mathematical model as well as a genetic algorithm to determine the assembly planning for an air conditioner. Hong and Cho (1999) also used a genetic algorithm to deal with the assembly problem of an electrical relay and automobile alternator with 10 and 13 components, respectively. In addition, Ozdamar (1999) reported three advantages of GAs when carrying out parts assembly: (1) adopting multi-point searching;…”

Section: Genetic Algorithmsmentioning

confidence: 99%

A multi-phase model for product part change problems

Wang

2008

International Journal of Production Research

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The parts of a product may be changed over the product life cycle for the purpose of enhancing profits, that is to say, product part change has a direct impact on production efficiency and customer satisfaction. The analysis of product part change can provide producers with related components/part suppliers by analysing the product's configuration. Before performing the physical assembly of a specific product, selected part suppliers are connected and have to verify their ability to assemble. This paper proposes an assessment model consisting of three major phases: (1) analysis of the assembly relationship among the parts;(2) preliminary selection of the appropriate parts and their suppliers; and (3) solution selection for supplier association. The proposed model solves the product part change problem for a given related quality level, operation and cost data. The liaison graph, fuzzy theory and value engineering approaches are employed in phases 1 and 2 to analyse the product's configuration and assembly relationships, to deal with production data and to carry out the preliminary analysis. To solve the solution selection model of phase 3, a genetic algorithm based heuristic approach was developed. Finally, the configuration of the stand module of TFT-LCD is used as an example to demonstrate the application and working of the proposed approach. The results of the illustrated example are used to demonstrate its efficiency and effectiveness in solving the part change problem.

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“…Dini et al [19] proposed a method using genetic algorithms to generate and evaluate the assembly sequence, and adopted a fitness function considering simultaneously the geometric constraints and some assembly process, including the minimization of gripper changes and object orientations, and the possibility of grouping similar assembly operations. Hong and Cho [20] proposed a GA-based approach to generate the assembly sequence for robotic assembly, and the fitness function is constructed based on the assembly costs that are reflected by the degree of motion instability, and assembly direction changes are assigned with different weights. Lazzerini and Marcelloni [21] used GA to generate and assess the assembly plans.…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Assembly Planning Approach Using a Multi-Objective Genetic Algorithm

Wong

Fuh

2006

Proceedings of the Institution of Mechanical Engineers, Part B:

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This paper proposes an assembly planning approach using a multi-objective genetic algorithm (GA). The influence of tolerance and clearance on product assemblability in different assembly sequences is considered and used as a constraint in assembly planning. For a more comprehensive search for feasible non-dominated solutions, this paper proposes a multiobjective genetic algorithm that establishes different fitness functions through a fuzzy weight distribution algorithm. It also considers the experience of the decision maker. The results of the case study are given to verify the proposed approach.

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A genetic-algorithm-based approach to the generation of robotic assembly sequences

Cited by 41 publications

References 0 publications

Optimisation process for robotic assembly of electronic components

Optimisation process for robotic assembly of electronic components

A multi-phase model for product part change problems

An Enhanced Assembly Planning Approach Using a Multi-Objective Genetic Algorithm

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