Predicting the tensile properties of heat treated and non-heat treated LPBFed AlSi10Mg alloy using machine learning regression algorithms
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| הוצא לאור ב: | PLoS One vol. 20, no. 6 (Jun 2025), p. e0324049 |
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| מחברים אחרים: | , , , , , , , , , , |
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Public Library of Science
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| גישה מקוונת: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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|---|---|---|---|
| 001 | 3215034480 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0324049 |2 doi | |
| 035 | |a 3215034480 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Jatti, Vijaykumar S | |
| 245 | 1 | |a Predicting the tensile properties of heat treated and non-heat treated LPBFed AlSi10Mg alloy using machine learning regression algorithms | |
| 260 | |b Public Library of Science |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In this study, the ability of machine learning algorithms to predict tensile properties of both heat-treated and non-heat treated LPBFed AlSi10Mg alloy is investigated. The data was analyzed using various Machine Learning Regression (MLR) models such as Linear Regression (LR), Gaussian Process Regression (GPR), Random Forest Regression (RFR), and Decision Tree (DT). The AlSi10Mg alloys, heat-treated and non heat-treated, had different tensile characteristics. The tensile characteristics were forecasted using trained and evaluated MLR models. Because the performance of various MLR models has been verified by several performance indicators, such as Root Mean Square Error (RMSE), R2 (coefficient of determination), Mean Square Error (MSE), and Mean Absolute Error (MAE). Moreover, scatter plots were made for checking the accuracy of the forecast. The GPR model demonstrated better prediction performance than the other three models, i.e., higher R2 values and lower error values for the heat-treated samples. For predicting the UTS value of non-heat treated samples, the LR model performs very well with R2 of 1.000. In that case, GPR has the better predictive performance for the other tensile features in non-heat treated samples. Summing up, it is obvious that GPR is well capable of predicting tensile properties of AlSi10Mg alloy with high precision. This indicates how important GPR is to additive manufacturing to achieve great quality. | |
| 653 | |a Solidification | ||
| 653 | |a Tensile properties | ||
| 653 | |a Regression analysis | ||
| 653 | |a Heat treatment | ||
| 653 | |a Aviation | ||
| 653 | |a Heat | ||
| 653 | |a Machine learning | ||
| 653 | |a Additive manufacturing | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Decision trees | ||
| 653 | |a Regression | ||
| 653 | |a Lasers | ||
| 653 | |a Root-mean-square errors | ||
| 653 | |a Neural networks | ||
| 653 | |a Computer aided design--CAD | ||
| 653 | |a 3-D printers | ||
| 653 | |a Algorithms | ||
| 653 | |a Gaussian process | ||
| 653 | |a Stainless steel | ||
| 653 | |a Methods | ||
| 653 | |a Alloys | ||
| 653 | |a Aluminum base alloys | ||
| 653 | |a Product development | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Saiyathibrahim, A | |
| 700 | 1 | |a Yadav, Arvind | |
| 700 | 1 | |a Murali, Krishnan R | |
| 700 | 1 | |a Jayaprakash, B | |
| 700 | 1 | |a Kaushal, Sumit | |
| 700 | 1 | |a Jatti, Vinaykumar S | |
| 700 | 1 | |a Jatti, Ashwini V | |
| 700 | 1 | |a Jatti, Savita V | |
| 700 | 1 | |a Kumar, Abhinav | |
| 700 | 1 | |a Gouadria, Soumaya | |
| 700 | 1 | |a Bonyah, Ebenezer | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 6 (Jun 2025), p. e0324049 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3215034480/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3215034480/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3215034480/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |