Nothing Special   »   [go: up one dir, main page]

Lostado et al., 2015 - Google Patents

Combining soft computing techniques and the finite element method to design and optimize complex welded products

Lostado et al., 2015

View PDF
Document ID
3876032115136086038
Author
Lostado R
Martinez R
Mac Donald B
Villanueva P
Publication year
Publication venue
Integrated Computer-Aided Engineering

External Links

Snippet

One of the main objectives when designing welded products is to reduce strains and deformations. Strains can cause excessive angular distortion. This results in a welded product that does not meet acceptable tolerances. The geometry of the weld bead (height …
Continue reading at journals.sagepub.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive 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
Vedrtnam et al. Optimizing submerged arc welding using response surface methodology, regression analysis, and genetic algorithm
Sathiya et al. Optimization of friction welding parameters using evolutionary computational techniques
Lundbäck et al. Modelling of metal deposition
Nagaraju et al. Optimization of welding process parameters for 9Cr-1Mo steel using RSM and GA
Hu et al. Welding parameters prediction for arbitrary layer height in robotic wire and arc additive manufacturing
Islam et al. Process parameter optimization of lap joint fillet weld based on FEM–RSM–GA integration technique
Sathiya et al. Optimal design for laser beam butt welding process parameter using artificial neural networks and genetic algorithm for super austenitic stainless steel
CN112632720A (en) Multidimensional data fusion and quantitative modeling method for metal additive manufacturing process system
Vasudevan et al. Genetic-algorithm-based computational models for optimizing the process parameters of A-TIG welding to achieve target bead geometry in type 304 L (N) and 316 L (N) stainless steels
Tian et al. A study on the prediction of inherent deformation in fillet-welded joint using support vector machine and genetic optimization algorithm
Ai et al. A defect-responsive optimization method for the fiber laser butt welding of dissimilar materials
Singh et al. Modelling of weld-bead geometry and hardness profile in laser welding of plain carbon steel using neural networks and genetic algorithms
Sarkar et al. Machine learning method to predict and analyse transient temperature in submerged arc welding
Ai et al. Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials
Kim et al. Review on machine learning based welding quality improvement
Sada The use of multi-objective genetic algorithm (MOGA) in optimizing and predicting weld quality
Li et al. A variable precision rough set based modeling method for pulsed GTAW
Vasudevan et al. Genetic algorithm for optimisation of A-TIG welding process for modified 9Cr–1Mo steel
Castorena et al. Parameter prediction with Novel enhanced Wagner Hagras interval Type-3 Takagi–Sugeno–Kang Fuzzy system with type-1 non-singleton inputs
Uzun et al. The use of eigenstrain theory and fuzzy techniques for intelligent modeling of residual stress and creep relaxation in welded superalloys
Kumar et al. Tailoring fillet weld geometry using a genetic algorithm and a neural network trained with convective heat flow calculations
Li et al. Prediction of welding deformation and residual stress of a thin plate by improved support vector regression
Verma et al. Genetic-neural optimization approach for gas metal arc welding of dissimilar aluminium alloys of AA5083-O/AA6061-T6
Fuzeau et al. Optimization of welding process parameters for reduced activation ferritic-martensitic (RAFM) steel