Data-driven Weld Nugget Width Prediction with Decision Tree Algorithm
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Summary
The capability of a decision tree algorithm to realize a data-driven resistance spot welding (RSW) weldability prediction is presented and it is concluded that the decision trees help in predicting the nugget width and in determining the impact of design and process parameters to the nuggets width response variable.
- Type
- article
- Published
- 2017-01-01
- Cited by
- 34
- References
- 30
- Access
- Open access
- OpenAlex
- https://openalex.org/W2727628648
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:116634959
Keywords
Decision tree, Weldability, Spot welding, Computer science, Welding
References
- Use of Machine Learning Algorithms for Weld Quality Monitoring using Acoustic Signature
- Classification and Regression by randomForest
- Multivariate Statistical Methods in Quality Management
- Programs for Machine Learning
- Data Mining for the Internet of Things: Literature Review and Challenges
- Prediction and Verification of Resistance Spot Welding Results of Ultra-High Strength Steels through FE Simulations
- Prediction of welding parameters for pipeline welding using an intelligent system
- A review of instance selection methods
- A framework for organizing the space of decision problems with application to solving subjective, context-dependent problems
- Classification and regression trees
- Data Science and its Relationship to Big Data and Data-Driven Decision Making
- Estimation and optimization of depth of penetration in hybrid CO2 LASER-MIG welding using ANN-optimization hybrid model
- Data mining: manufacturing and service applications
- Big data analytics for healthcare
- Assessment of resistance spot welding quality based on ultrasonic testing and tree-based techniques
- Weldability prediction of AHSS stackups using artificial neural network models
- Towards proper-inconsistency in weldability prediction using k-nearest neighbor regression and generalized regression neural network with mean acceptable error
- Anomaly detection: A survey
- Quality monitoring for resistance spot welding using dynamic signals
- Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals
Cited by
- Integration and visualization framework for data-driven resistance spot welded assembly design
- Financial Management and Decision Based on Decision Tree Algorithm
- Semantic weldability prediction with RSW quality dataset and knowledge construction
- A generic data structure for the specific domain of robotic arc welding
- Hybrid Nugget Diameter Prediction for Resistance Spot Welding
- P2P net loan default risk based on Spark and complex network analysis based on wireless network element data environment
- Performance analysis and comparison of machine learning algorithms for predicting nugget width of resistance spot welding joints
- An Automatic Detection and Identification Method of Welded Joints Based on Deep Neural Network
- Online measurement of weld penetration in robotic resistance spot welding using electrode displacement signals
- Machine learning model to predict welding quality using air-coupled acoustic emission and weld inputs
- Predicting tensile-shear strength of nugget using M5P model tree and random forest: An analysis
- Detecção de expulsão em processos de soldagem a ponto por resistência: uma análise comparativa envolvendo métodos baseados em aprendizado de máquina
- Data-driven decision support for process quality improvements
- Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination With Process Signals in Resistance Spot Welding of Advanced High-Strength Steels
- Quality Monitoring of Manufacturing Processes based on Full Data Utilization
- A review on role of artificial intelligence in food processing and manufacturing industry
- Monitoring of resistance spot welding expulsion based on machine learning
- Instance selection-based dissimilar weldment design prediction for resistant spot welding
- Data-Driven Intelligent Model for the Classification, Identification, and Determination of Data Clusters and Defect Location in a Welded Joint
- Understanding geometrical size effect on fatigue life of A588 steel using a machine learning approach
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