Predicting Residual Weld Stress Distribution with an Adaptive Neuro-Fuzzy Inference System
| Autor: | Terumi Nakamura, Houichi Kitano |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Adaptive neuro fuzzy inference system Computer science Mechanical Engineering 02 engineering and technology Welding Stress distribution Residual Industrial and Manufacturing Engineering law.invention 020303 mechanical engineering & transports 020901 industrial engineering & automation 0203 mechanical engineering law Control theory |
| Zdroj: | International Journal of Automation Technology. 12:290-296 |
| ISSN: | 1883-8022 1881-7629 |
| DOI: | 10.20965/ijat.2018.p0290 |
| Popis: | This work is an investigation into the applicability of the adaptive neuro-fuzzy inference system (ANFIS), a machine learning technique, to develop a model of the relation of residual stress distribution in a single weld bead-on-plate part to weld heat input and distance from the center of the weld line. Residual stress distributions required to train the ANFIS model were obtained through thermal elastic-plastic finite element analysis. Appropriate conditions for training the ANFIS model were investigated by evaluating the prediction error of the ANFIS model developed under various conditions. Afterward, residual stress distributions obtained by the developed ANFIS model trained under the appropriate conditions were compared with those obtained through thermal elastic-plastic finite element analysis. Discrepancies between the residual stresses obtained through the ANFIS model and thermal elastic-plastic finite element analysis were smaller than ±40 MPa in all regions. The results suggest that the ANFIS modeling had the ability to learn and generalize residual weld stress distributions in single weld bead-on-plate parts. |
| Databáze: | OpenAIRE |
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