Deformation and Residual Stress Based Multi-Objective Genetic Algorithm for Welding Sequence Optimization

No abstract is available for this paper.

• Publication year

  2016

• Venue

  Mexican International Conference on Artificial Intelligence

• Publication date

  2016-10-01

• Fields of study

  Materials Science, Computer Science, Engineering

• Identifiers
  DOI 10.1109/MICAI-2016.2016.00021
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• A New Stopping Criterion for Genetic Algorithms

  2016cited by this paper
• Study of the Dynamic Behavior of a Car Body for Mounting the Rear Axle

  2016cited by this paper
• Influence of the welding sequence on residual stress and distortion of fillet welded structures

  2016cited by this paper
• A Genetic Algorithm Approach for an Equitable Treatment of Objective Functions in Multi-objective Optimization Problems

  2014cited by this paper
• Simulation-based numerical optimization of arc welding process for reduced distortion in welded structures

  2014influential reference
• Computational Welding Mechanics

  2014influential reference
• Study of Various Mutation Operators in Genetic Algorithms

  2014cited by this paper
• Welding Sequence Optimization Of Plasma ArcFor Welded Thin Structures

  2012cited by this paper
• Effect of muffler mounting bracket designs on durability

  2011cited by this paper
• A Novel Crossover Operator for Genetic Algorithms: Ring Crossover

  2011cited by this paper
• Determining the Best Mutation Probabilities of a Genetic Algorithm for Mapping Tasks

  2011cited by this paper
• A study on the effect of welding sequence in fabrication of large stiffened plate panels

  2011cited by this paper
• Advanced Engineering Methods for Assessing Welding Distortion in Aero-Engine Assemblies

  2011cited by this paper
• Stopping Criteria for Genetic Algorithms with Application to Multiobjective Optimization

  2010cited by this paper
• Challenges in Verification of CWM Software to Compute Residual Stress and Distortion in Welds

  2010cited by this paper
• Chosen aspects of FEM strength analysis of telescopic jib mounted on mobile platform

  2008cited by this paper
• Optimal design of weld pattern in sheet metal assembly based on a genetic algorithm

  2005influential reference
• Computational Welding Mechanics

  2005influential reference
• A Study of Mutation Methods for Evolutionary Algorithms

  2002cited by this paper
• Clamping and welding sequence optimisation for minimising cycle time and assembly deformation

  2002influential reference
• Robot arc welding task sequencing using genetic algorithms

  2002influential reference
• Optimizing welding sequence with genetic algorithm

  2000cited by this paper
• New stopping criterion for genetic algorithms

  2000cited by this paper
• Evaluation of crossover techniques in genetic algorithm based optimum structural design

  2000cited by this paper
• Stopping Criteria for Finite Length Genetic Algorithms

  1996cited by this paper
• Genetic Algorithms in Search Optimization and Machine Learning

  1988cited by this paper
• Analysis of welded structures

  1982cited by this paper

• A self-learning method with domain knowledge integration for intelligent welding sequence planning

  2025cites this paper
• Robotic welding techniques in marine structures and production processes: A systematic literature review

  2024cites this paper
• Prediction and validation of melt pool dimensions and geometric distortions of additively manufactured AlSi10Mg

  2023cites this paper
• Multi-Objective Welding-Parameter Optimization Using Overlaid Contour Plots and the Butterfly Optimization Algorithm

  2022cites this paper
• NUMERICAL ANALYSIS OF WELDING SEQUENCE INFLUENCING THE QUALITY OF AN AA6060-T4 ALLOY LAP JOINT NUMERIČKA ANALIZA UTICAJA SEKVENCE ZAVARIVANJA NA KVALITET

  2021cites this paper
• Incorporating domain knowledge into reinforcement learning to expedite welding sequence optimization

  2020cites this paper
• A Reinforcement Learning Based Approach for Welding Sequence Optimization

  2018influential citation