A Data-Driven Methodology for Obtaining the Stress–Strain Curves of Metallic Materials Using Discrete Indentation Tests
Determining the stress–strain curve and other plastic properties using instrumented indentation techniques has long been a topic of active study. The potential to use small, geometrically simple specimens and to characterize a component under service without the need to remove material for specimen...
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MDPI AG
2025-01-01
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| author | Nitzan Rom Elad Priel |
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| description | Determining the stress–strain curve and other plastic properties using instrumented indentation techniques has long been a topic of active study. The potential to use small, geometrically simple specimens and to characterize a component under service without the need to remove material for specimen preparation makes this methodology highly attractive to many industries. In this study, a data-driven approach that leverages machine learning and finite element analysis was used to construct a model called ‘Brilearn’ that predicts the stress–plastic strain curve of metallic materials. The framework consists of a novel model for predicting the hardening curve, the classical Tabor model for predicting the yield stress for materials with yield stress lower than 100 MPa, and an XGBoost model for predicting the yield stress for metals with yield stress higher than 100 MPa. The model was validated against experimental data on Al1100, Al6061-T6, Al7075-T6, and brass and copper alloys, features error predictions of 8.4 ± 8.5% for the yield stress and 3.2 ± 4% for a complete curve ranging from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mover accent="true"><mrow><mi>ε</mi></mrow><mo>¯</mo></mover></mrow><mrow><mi>p</mi></mrow></msup><mo>=</mo><mn>0</mn></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mover accent="true"><mrow><mi>ε</mi></mrow><mo>¯</mo></mover></mrow><mrow><mi>p</mi></mrow></msup><mo>=</mo><mn>0.15</mn></mrow></semantics></math></inline-formula>. The model is especially suited for the determination of the stress–plastic strain curves for components in service since only two simple indentation tests are required. |
| format | Article |
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| institution | Kabale University |
| issn | 2075-4701 |
| language | English |
| publishDate | 2025-01-01 |
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| series | Metals |
| spelling | doaj-art-1af69d0151834419bc60b01b477cc7122025-01-24T13:41:29ZengMDPI AGMetals2075-47012025-01-011514010.3390/met15010040A Data-Driven Methodology for Obtaining the Stress–Strain Curves of Metallic Materials Using Discrete Indentation TestsNitzan Rom0Elad Priel1Israel Atomic Energy Commission, Tel-Aviv 61070, IsraelDepartment of Mechanical Engineering, Center for Thermo-Mechanics and Failure of Materials (CTMFM), Shamoon College of Engineering, Be’er-Sheva 84100, IsraelDetermining the stress–strain curve and other plastic properties using instrumented indentation techniques has long been a topic of active study. The potential to use small, geometrically simple specimens and to characterize a component under service without the need to remove material for specimen preparation makes this methodology highly attractive to many industries. In this study, a data-driven approach that leverages machine learning and finite element analysis was used to construct a model called ‘Brilearn’ that predicts the stress–plastic strain curve of metallic materials. The framework consists of a novel model for predicting the hardening curve, the classical Tabor model for predicting the yield stress for materials with yield stress lower than 100 MPa, and an XGBoost model for predicting the yield stress for metals with yield stress higher than 100 MPa. The model was validated against experimental data on Al1100, Al6061-T6, Al7075-T6, and brass and copper alloys, features error predictions of 8.4 ± 8.5% for the yield stress and 3.2 ± 4% for a complete curve ranging from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mover accent="true"><mrow><mi>ε</mi></mrow><mo>¯</mo></mover></mrow><mrow><mi>p</mi></mrow></msup><mo>=</mo><mn>0</mn></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mover accent="true"><mrow><mi>ε</mi></mrow><mo>¯</mo></mover></mrow><mrow><mi>p</mi></mrow></msup><mo>=</mo><mn>0.15</mn></mrow></semantics></math></inline-formula>. The model is especially suited for the determination of the stress–plastic strain curves for components in service since only two simple indentation tests are required.https://www.mdpi.com/2075-4701/15/1/40machine learningindentationstress–strain curve |
| spellingShingle | Nitzan Rom Elad Priel A Data-Driven Methodology for Obtaining the Stress–Strain Curves of Metallic Materials Using Discrete Indentation Tests Metals machine learning indentation stress–strain curve |
| title | A Data-Driven Methodology for Obtaining the Stress–Strain Curves of Metallic Materials Using Discrete Indentation Tests |
| title_full | A Data-Driven Methodology for Obtaining the Stress–Strain Curves of Metallic Materials Using Discrete Indentation Tests |
| title_fullStr | A Data-Driven Methodology for Obtaining the Stress–Strain Curves of Metallic Materials Using Discrete Indentation Tests |
| title_full_unstemmed | A Data-Driven Methodology for Obtaining the Stress–Strain Curves of Metallic Materials Using Discrete Indentation Tests |
| title_short | A Data-Driven Methodology for Obtaining the Stress–Strain Curves of Metallic Materials Using Discrete Indentation Tests |
| title_sort | data driven methodology for obtaining the stress strain curves of metallic materials using discrete indentation tests |
| topic | machine learning indentation stress–strain curve |
| url | https://www.mdpi.com/2075-4701/15/1/40 |
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