https://orcid.org/0000-0003-2465-0588
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ABSTRACT
The sensitivity of magnetic non-destructive methods to both chemical composition and microstructure has limited their potential application for determination of mechanical properties in plain carbon steels under conditions of varying carbon content and microstructure. The present paper investigates advantages of applying an artificial neural network (ANN) method to magnetic hysteresis loop (MHL) method for non-destructively measuring mechanical properties of plain carbon steels with unknown carbon and microstructure (resulting from various heat-treating processes). Artificial neural network used in this study is a generalised regression neural network (GRNN), since it has reportedly high performance in estimation and function approximation and could be trained very fast. After it is appropriately trained, the neural network takes one of the four magnetic parameters (or any combination of them) extracted from the measured hysteresis loop to estimate the desired mechanical parameters (hardness, tensile strength, yield strength, and elongation) of the sample under test. The results revealed that the proposed methodology can be a very effective tool to estimate the mechanical properties of the hypoeutectic plain carbon steel sample with unknown carbon content and heat treatment background if appropriate combination of magnetic properties is used as the GRNN inputs.
Nomenclature
| ANN | = | Artificial neural network |
| ‘A’ treatment | = | Cooling in off-furnace after austenitizing process |
| ‘A1’ treatment | = | Cooling in still air after austenitizing process |
| ‘A2’ treatment | = | Cooling in slightly agitated air after austenitizing process |
| ‘Q’ treatment | = | Quenching |
| ‘T2-T6’ treatments | = | Quench/tempering in the range of 200 to 600 °C |
| A/D | = | Analog-to-Digital |
| AISI | = | American iron and steel institute |
| ANFIS | = | Adaptive neuro-fuzzy inference system |
| ASTM | = | American Society for Testing and Materials |
| Bmax | = | Maximum flux density (T) |
| CNN | = | Convolutional neural network |
| D/A | = | Digital-to-Analog |
| EC | = | Eddy current |
| GRNN | = | Generalised regression neural network |
| Hc | = | Coercivity (A/m) |
| Max µDiff | = | Maximum differential permeability |
| MBN | = | Magnetic Barkhausen noise |
| MHL | = | Magnetic hysteresis loop |
| NDC | = | Non-destructive characterisation |
| PSO | = | Particle swarm optimisation |
| RBF | = | Radial based function |
| RMS | = | Root mean square |
| SAW | = | Surface acoustic wave |
| SEM | = | Scanning electron microscope |
| SVM | = | Support vector machine |
| TTT | = | Time-temperature-transformation |
| UTS | = | Ultimate tensile strength (MPa) |
| WH | = | Hysteresis loss (J/m3) |
| Wt.% | = | Weight percent |
| YS | = | Yield strength (MPa) |
Disclosure statement
No potential conflict of interest was reported by the author(s).