A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Multi-objective factors optimization in fused deposition modelling with particle swarm optimization and differential evolution
https://orcid.org/http://orcid.org/0000-0003-0667-8851
The design of any system contemplates the elaboration of a prototype of the entire system or some parts, before the manufacturing phase. Nowadays, rapid prototyping (RP) is widely used by the designers. Achieving good manufacturing performances needs to handle various process parameters. Most works deal with single objective process parameters. The reality is quite different and the processes involve conflicting objectives. This paper addresses the multi-objective factors optimization of the fused deposition modelling (FDM) technology. The problem is converted into a single one using the weighted-sum method and then solved by resorting to two nature-inspired computing techniques, namely particle swarm optimization (PSO) and differential evolution (DE). The results obtained are compared.
Multi-objective factors optimization in fused deposition modelling with particle swarm optimization and differential evolution
https://orcid.org/http://orcid.org/0000-0003-0667-8851
The design of any system contemplates the elaboration of a prototype of the entire system or some parts, before the manufacturing phase. Nowadays, rapid prototyping (RP) is widely used by the designers. Achieving good manufacturing performances needs to handle various process parameters. Most works deal with single objective process parameters. The reality is quite different and the processes involve conflicting objectives. This paper addresses the multi-objective factors optimization of the fused deposition modelling (FDM) technology. The problem is converted into a single one using the weighted-sum method and then solved by resorting to two nature-inspired computing techniques, namely particle swarm optimization (PSO) and differential evolution (DE). The results obtained are compared.
Multi-objective factors optimization in fused deposition modelling with particle swarm optimization and differential evolution
https://orcid.org/http://orcid.org/0000-0003-0667-8851
The design of any system contemplates the elaboration of a prototype of the entire system or some parts, before the manufacturing phase. Nowadays, rapid prototyping (RP) is widely used by the designers. Achieving good manufacturing performances needs to handle various process parameters. Most works deal with single objective process parameters. The reality is quite different and the processes involve conflicting objectives. This paper addresses the multi-objective factors optimization of the fused deposition modelling (FDM) technology. The problem is converted into a single one using the weighted-sum method and then solved by resorting to two nature-inspired computing techniques, namely particle swarm optimization (PSO) and differential evolution (DE). The results obtained are compared.
Multi-objective factors optimization in fused deposition modelling with particle swarm optimization and differential evolution
Int J Interact Des Manuf
Mellal, Mohamed Arezki (author) / Laifaoui, Chahinaze (author) / Ghezal, Fahima (author) / Williams, Edward J. (author)
2022-12-01
6 pages
Article (Journal)
Electronic Resource
English
Rapid prototyping , Fused deposition modelling , Multi-objective optimization , Weighted-sum method , Particle swarm optimization , Differential evolution Engineering , Engineering, general , Engineering Design , Mechanical Engineering , Computer-Aided Engineering (CAD, CAE) and Design , Electronics and Microelectronics, Instrumentation , Industrial Design
Optimizing multiple process parameters in fused deposition modeling with particle swarm optimization
| Springer Verlag | 2020
Multi-objective optimization of a Stirling cooler using particle swarm optimization algorithm
| Taylor & Francis Verlag | 2022
| British Library Conference Proceedings | 2010
| British Library Online Contents | 2014