Mechanical assembly planning using ant colony optimization

Wang, Hui; Rong, Yiming; Xiang, Dong

doi:10.1016/j.cad.2013.09.001

Cited by 63 publications

(36 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…If the value of a ii is 1, it means that a i is the base part for assembly. 4. If the value of a ij is ''.x i ,'' it means that the assembling of a i and a j must be before the reference connection of x i .…”

Section: Representation Of Precedence Constraintmentioning

confidence: 99%

“…P 1 Pump cover P 13 Bearing end-shield P 2 Turbine wheel nut P 14 Screw 6 3 M6 3 12 P 3 Washer P 15 Bracket P 4 Turbine wheel P 16 Bearing P 5 Bolt 6 3 M8 3 12 P 17…”

Section: Ease Of Measurementmentioning

confidence: 99%

“…When the number of components increases, the solution space is excessively increased and leads to combinatorial explosion problem. Many researchers try to use soft computing approach to solve assembly planning problem, and several intelligent algorithms have been used, such as genetic algorithm, 2 artificial neural network, 3 ant colony optimization (ACO) algorithm, 4 particle swarm optimization algorithm, 5 and imperialist competitive algorithm. 6 Also for automatic assembly planning, the most important evaluation objectives are minimizing the number of assembly direction changes, minimizing the number of assembly tool changes, minimizing the number of assembly type changes, and maximizing assembly stability and parallelism.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A hybrid approach for haptics-based intelligent assembly planning

Xia

2016

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Assembly planning is normally realized by automatic approach or interactive approach. Both of these methods have promising advantages, but they also have inevitable shortcomings. This article proposes a hybrid approach to combine automatic assembly planning and interactive assembly planning to generate optimal assembly sequence for industrial products. By defining a novel precedence constraint table to represent the assembly precedence relationships between components, the initial feasible assembly sequence can be generated by intelligent algorithm. This assembly sequence can be interactively simulated and evaluated in a haptics-based virtual environment. By simulating the multiple geometry constraints-based assembly process and assembly tool operation process, more practical precedence constraints and evaluation rules can be recognized, and more optimal assembly sequence can be re-planned. This is an iterative process until the best assembly sequence is obtained. The proposed hybrid approach can utilize the advantages of both methods, such as computer intelligence and human knowledge, and overcome their shortcomings. The generated results are more practical and flexible than traditional methods.

show abstract

Section: Representation Of Precedence Constraintmentioning

confidence: 99%

“…P 1 Pump cover P 13 Bearing end-shield P 2 Turbine wheel nut P 14 Screw 6 3 M6 3 12 P 3 Washer P 15 Bracket P 4 Turbine wheel P 16 Bearing P 5 Bolt 6 3 M8 3 12 P 17…”

Section: Ease Of Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid approach for haptics-based intelligent assembly planning

Xia

2016

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…Se trata de relaciones dos a dos entre los distintos componentes del producto que determinan cuáles se deben ensamblar entre sí. La realización de todas las uniones conduce a la obtención del producto completamente ensamblado [26][27] [61]. La definición de la secuencia de montaje se puede entender también como la secuenciación de las uniones del modelo de producto.…”

Section: Enfoques Del Diseño Conceptual De La Industrializaciónunclassified

“…Para ello también se emplea el concepto de uniones entre componentes. Cada sub montaje implica un número de uniones, por lo que su secuencia de montaje se podrá obtener realizando una ordenación de las mismas [9][17] [26] [61].La estructura resultante de realizar esta ordenación se denomina vista As Prepared. En el ejemplo de la Figura 7, se ha definido para los 3 sub montajes, las secuencias de uniones (4,5), (1,2) y (6,3), respectivamente.…”

Section: Enfoques Del Diseño Conceptual De La Industrializaciónunclassified

Metodología para la generación y evaluación multicriterio de soluciones conceptuales de diseño de líneas de montaje final de producto en el sector aeronáutico

Page¹

View full text Add to dashboard Cite

Business process optimization using the ant colony system

2018

Manage Decis Econ

View full text Add to dashboard Cite

Process optimization is a key consideration in workflow management. Implementing an efficient workflow may improve customer satisfaction and enhance productivity of an enterprise. Many optimization tools have been introduced to solve scheduling problems in the manufacturing environment, but most of them have not drawn much attention of decision makers for workflow analyses. This is mainly due to the difference between business operations and manufacturing processes that the process optimization tools cannot be directly applied for analyzing business workflows.Scholars have associated the attributes of workflow in the business environment with those of scheduling concepts to facilitate the use of job shop scheduling techniques for solving workflow problems. However, there is still not much discussion on the use of metaheuristic algorithms for workflow analyses. This paper proposes the use of a systematic approach that entails the ant colony optimization algorithm for identifying the best task sequence in support of processing time analyses. The applicability of the proposed approach is demonstrated with a case example. The result shows that a better operation sequence in terms of shorter processing duration can be obtained by the proposed approach.

show abstract

Mechanical assembly planning using ant colony optimization

Cited by 63 publications

References 20 publications

A hybrid approach for haptics-based intelligent assembly planning

A hybrid approach for haptics-based intelligent assembly planning

Metodología para la generación y evaluación multicriterio de soluciones conceptuales de diseño de líneas de montaje final de producto en el sector aeronáutico

Business process optimization using the ant colony system

Contact Info

Product

Resources

About