Kim et al., 2004 - Google Patents
Optimization of a gas metal arc welding process using the desirability function and the genetic algorithmKim et al., 2004
- Document ID
- 13657308816572411521
- Author
- Kim D
- Rhee S
- Publication year
- Publication venue
- Proceedings of the institution of mechanical engineers, part B: Journal of engineering manufacture
External Links
Snippet
The dual response approach is a method of determining the optimal welding conditions in consideration of both the mean value and variance of a characteristic. The definition of the objective function is important, as the results of the optimization problem are influenced by …
- 238000005457 optimization 0 title abstract description 39
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Lostado et al. | Combining soft computing techniques and the finite element method to design and optimize complex welded products | |
Lei et al. | Real-time weld geometry prediction based on multi-information using neural network optimized by PCA and GA during thin-plate laser welding | |
Kim et al. | Modelling and optimization of a GMA welding process by genetic algorithm and response surface methodology | |
Kim et al. | Optimization of a gas metal arc welding process using the desirability function and the genetic algorithm | |
CN106873381A (en) | Spray ammonia control system | |
CN114969553B (en) | Welding cost and process parameter comprehensive intelligent recommendation method based on knowledge graph | |
Vimal et al. | Optimization of process parameters of SMAW process using NN-FGRA from the sustainability view point | |
CN113199184A (en) | Weld joint shape prediction method based on improved self-adaptive fuzzy neural network | |
Kim et al. | Optimization of GMA welding process using the dual response approach | |
Hongyu et al. | Prediction of two-dimensional topography of laser cladding based on neural network | |
Casalino et al. | A model for evaluation of laser welding efficiency and quality using an artificial neural network and fuzzy logic | |
Vasudevan et al. | Genetic algorithm for optimisation of A-TIG welding process for modified 9Cr–1Mo steel | |
CN118616989A (en) | Automatic welding control system based on path fitting | |
Zhan et al. | The feasibility of intelligent welding procedure qualification system for Q345R SMAW | |
JP4857228B2 (en) | Plant operation optimization device | |
Ghaderi et al. | The application of imperialist competitive algorithm for optimization of deposition rate in submerged arc welding process using TiO 2 nano particle | |
Lin | Optimizing the auto-brazing process quality of aluminum pipe and flange via a Taguchi-Neural-Genetic approach | |
Castorena et al. | Parameter prediction with Novel enhanced Wagner Hagras interval Type-3 Takagi–Sugeno–Kang Fuzzy system with type-1 non-singleton inputs | |
Mollah et al. | Modeling of TIG welding and abrasive flow machining processes using radial basis function networks | |
Carrino et al. | A neuro-fuzzy approach for increasing productivity in gas metal arc welding processes | |
Surender et al. | Fuzzy Logic‐Based Techniques for Modeling the Correlation between the Weld Bead Dimension and the Process Parameters in MIG Welding | |
Pal et al. | Optimisation of weld deposition efficiency in pulsed MIG welding using hybrid neuro-based techniques | |
Lin | The use of the Taguchi method and a neural-genetic approach to optimize the quality of a pulsed Nd: YAG laser welding process | |
Sen et al. | Optimal selection of machining conditions in the electrojet drilling process using hybrid NN-DF-GA approach | |
Zhou et al. | Optimization of laser spiral welding using Response surface methodology and genetic algorithms |