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PREDICTION OF HARDNESS VALUES OF AGED SELECTIVE LASER MELTED AlSi10Mg ALLOY DATA WITH MACHINE LEARNING METHODS

Yıl 2025, Cilt: 9 Sayı: 1, 53 - 62, 30.04.2025

Murat İnce , Hatice Varol Özkavak

https://doi.org/10.46519/ij3dptdi.1610116

Öz

Aluminum manufactured with the Selective Laser Melting (SLM) method has been the subject of many research due to the benefits it provides, especially when used in the automotive and aviation industries. Therefore, it is important to examine and improve the mechanical properties of Al parts produced by the SLM method. Many experiments are needed to examine and improve the mechanical properties of SLM Al materials. This situation causes losses in terms of both time and cost. In this study, aims to estimate the hardness values of SLM AlSi10Mg materials that have been aged. For this purpose, aging processes were applied to SLM AlSi10Mg materials at different times and temperatures, and different machine learning methods were used to predict the hardness values using the hardness values obtained because of the process. Random Forest Regression (RFR) algorithm and Artificial Neural Network (ANN) were used in the study. As a result of the study, it was determined that the hardness values estimated by the ANN (R2 0.9276) method were close to the real hardness values. This is proof that it is possible to predict hardness values using the machine learning method.

Anahtar Kelimeler

SLM AlSi10Mg Aging Artificial Neural Networks Machine Learning Regression

Kaynakça

  • 1. Gibson I., Rosen D.W., Stucker B., “Additive Manufacturing Technologies - Rapid Prototyping to Direct Digital Manufacturing”, Pages 1-625, USA,2015.
  • 2. J.H. Martin, B.D. Yahata, J.M. Hundley, J.A. Mayer, T.A. Schaedler, and T.M. Pollock, “3D Printing Of High-Strength Aluminium Alloys”, Nature, Vol. 549, Pages 365-369. 2017,
  • 3. A. Hadadzadeh, B.S. Amirkhiz, S. Shakerin, J. Kelly, J. Li, and M. Mohammadi, “Microstructural Investigation and Mechanical Behavior of a Two-Material Component Fabricated through Selective Laser Melting of AlSi10Mg on an Al-Cu-Ni-Fe-Mg Cast Alloy Substrate”, Addit. Manuf.,Vol. 31, Pages 100937,2020.
  • 5. A.G. Demir and C.A. Biffi, “Micro Laser Metal Wire Deposition Of Thin-Walled Al Alloy Components: Process And Material Characterization”, J. Manuf. Process., Vol. 37, Pages 362–369,2019.
  • 5. Q. Yan, B. Song, and Y. Shi, “Comparative Study Of Performance Comparison Of Alsi10mg Alloy Prepared By Selective Laser Melting And Casting”, J. Mater. Sci. Technol., Vol. 41, Pages 199–208,2020.
  • 6. Y. Cao, X. Lin, Q.Z. Wang, S.Q. Shi, L. Ma, N. Kang, and W.D. Huang, “Microstructure Evolution And Mechanical Properties at High Temperature of Selective Laser Melted AlSi10Mg”, J. Mater. Sci. Technol., Vol. 62, Pages 162-172, 2021,
  • 7. N.O. Larrosa, W. Wang, N. Read, M.H. Loretto, C. Evans, J. Carr, U. Tradowsky, M.M. Attallah, and P.J. Withers, “Linking Microstructure and Processing Defects to Mechanical Properties of Selectively Laser Melted AlSi10Mg Alloy”, Theor. Appl. Fract. Mech., Vol. 98, Pages 123-133, 2018.
  • 8. L. Zhuo, Z. Wang, H. Zhang, E. Yin, Y. Wang, T. Xu, and C. Li, “Effect of Post-Process Heat Treatment on Microstructure and Properties of Selective Laser Melted AlSi10Mg Alloy”, Mater. Lett., Vol. 234, Pages 196-200, 2019.
  • 9. J. Bi, Z. Lei, Y. Chen, X. Chen, N. Lu, Z. Tian, and X. Qin,” An Additively Manufactured Al-14.1 Mg-0.47 Si-0.31 Sc-0.17 Zr Alloy with High Specific Strength, Good Thermal Stability and Excellent Corrosion Resistance”, J. Mater. Sci. Technol., Vol. 67, Pages23-35, 2021,
  • 10. A. Heinz, A. Haszler, C. Keidel, S. Moldenhauer, R. Benedictus, and W.S. Miller, “Recent Development in alumiNIUM ALloys for Aerospace Applications”, Mater. Sci. Eng. A, Vol. 280, Pages 102-107,2000.
  • 11. F. Calignano, “Design Optimization of Supports for Overhanging Structures in Aluminum and Titanium Alloys by Selective Laser Melting”, Mater. Des., Vol. 64, Pages 203-213,2014.
  • 12. L. Thijs, K. Kempen, J.P. Kruth, and J. Van Humbeeck, “”Fine-structured Aluminium Products with Controllable Texture by Selective Laser Melting of Pre-alloyed AlSi10Mg Powder”, Acta Mater., Vol. 61, Pages 1809-1819, 2013.
  • 13. N.T. Aboulkhair, M. Simonelli, L. Parry, I. Ashcroft, C. Tuck, and R. Hague, “Prog. 3D Printing of Aluminium Alloys: Additive Manufacturing of Aluminium Alloys using Selective Laser Melting”, Mater. Sci., Vol. 106, Pages 100578, 2019.
  • 14. E. Brandl, U. Heckenberger, V. Holzinger, and D. Buchbinder, “Additive Manufactured AlSi10Mg Samples using Selective Laser Melting (SLM): Microstructure, High Cycle Fatigue, and Fracture Behavior”, Mater. Des., Vol. 34, Pages 159-169,2012.
  • 15. K. Kempen, L. Thijs, J. Van Humbeeck, and J.P. Kruth, “Processing AlSi10Mg by Selective Laser Melting: parameter optimisation and material characterisation”, Mater. Sci. Technol., Vol. 31, Pages 917-923,2015.
  • 16. N. Takata, H. Kodaira, K. Sekizawa, A. Suzuki, and M. Kobashi, “Change in Microstructure of Selectively Laser Melted AlSi10Mg Alloy with Heat Treatments”, Mater. Sci. Eng. A., Vol. 704, Pages 218-228,2017.
  • 17. R. Casati, M. Hamidi Nasab, M. Coduri, V. Tirelli, and M. Vedani, “Effects of Platform Pre-Heating and Thermal-Treatment Strategies on Properties of AlSi10Mg Alloy Processed by Selective Laser Melting”, Met., Basel, Vol. 8, Pages 954,2018.
  • 18. I. Rosenthal, R. Shneck, and A. Stern, “Heat Treatment Effect on the Mechanical Properties and Fracture Mechanism in Alsi10Mg Fabricated by Additive Manufacturing Selective Laser Melting Process”, Mater. Sci. Eng. A., Vol. 729, Pages 310-322,2018.
  • 19. Q. Han and Y. Jiao,” Effect of Heat Treatment and Laser Surface Remelting on Alsi10Mg Alloy Fabricated by Selective Laser Melting”, Int. J. Adv. Manuf. Technol., Vol. 102, Pages 3315-3324,2019.
  • 20. L.F. Wang, J. Sun, X.L. Yu, Y. Shi, X.G. Zhu, L.Y. Cheng, H.H. Liang, B. Yan, and L.J. Guo, “Enhancement in Mechanical Properties of Selectively Laser-Melted Alsi10mg Aluminum Alloys by T6-Like Heat Treatment”, Mater. Sci. Eng. A.,Vol. 734, Pages 299-310,2018.
  • 21. W. Li, S. Li, J. Liu, A. Zhang, Y. Zhou, Q. Wei, C. Yan, and Y. Shi, “Effect of Heat Treatment on Alsi10Mg Alloy Fabricated by Selective Laser Melting: Microstructure Evolution, Mechanical Properties And Fracture Mechanism”, Mater. Sci. Eng. A., Vol. 663, Pages 116-125,2016.
  • 22. X. Li, X. Wang, M. Saunders, A. Suvorova, L. Zhang, Y. Liu, M. Fang, Z. Huang, and T.B. Sercombe, “A Selective Laser Melting and Solution Heat Treatment Refined Al–12si Alloy with a Controllable Ultrafine Eutectic Microstructure and 25% Tensile Ductility”, Acta Mater.,Vol. 95, Pages 74-82,2015.
  • 23. E. Sjölander and S. Seifeddine, The Heat Treatment of Al–Si–Cu–Mg Casting Alloys, J. Mater. Process. Technol., Vol. 210, Pages 1249-1259,2010.
  • 24. M. Moustafa, F. Samuel, and H. Doty, “Effect of Solution Heat Treatment and Additives on the Microstructure of Al-Si (A413. 1) Automotive Alloys”, J. Mater. Sci., Vol. 38, Pages 4507-4522,2003.
  • 25. A.H. Maamoun, M. Elbestawi, G.K. Dosbaeva, and S.C. Veldhuis, “Thermal Post-Processing of Alsi10Mg Parts Produced by Selective Laser Melting using Recycled Powder”, Addit. Manuf., Vol. 21, Pages 234-247,2018.
  • 26. U.M.R. Paturi, S. Cheruku, V.P.K. Pasunuri, S. Salike, N.S. Reddy, and S. Cheruku, “Machine Learning and Statistical Approach in Modeling and Optimization of Surface Roughness in Wire Electrical Discharge Machining”, Mach. Learn. Appl., Vol. 6, Pages 100099,2021.
  • 27. U.M.R. Paturi, D.G. Vanga, R.B. Duggem, N. Kotkunde, N.S. Reddy, and S. Dutta, “Estimation of Surface Roughness of Direct Metal Laser Sintered Alsi10Mg using Artificial Neural Networks and Response Surface Methodology”, Mater. Manuf. Process.,Vol. 38, Issue 14, Pages 1798-1808,2023.
  • 28. Z. Zhan, N. Ao, Y. Hu, and C. Liu, Defect‐Induced Fatigue Scattering and Assessment of Additively Manufactured 300M-AerMet100 Steel: An Investigation Based on Experiments and Machine Learning, Eng. Fract. Mech., Vol. 264, Pages 108352, 2022.
  • 29. M. Marrey, E. Malekipour, H. El-Mounayri, and E.J. Faierson, A Framework for Optimizing Process Parameters in Powder Bed Fusion (PBF) Process Using Artificial Neural Network (ANN), Proc. Manuf., Vol. 34, Pages 505-515,2019.
  • 30. G.F.V. Voort, Metallography: Principles & Practice, ASM International, United States of America, 1999.
  • 31. K. Upreti, M. Verma, M. Agrawal, J. Garg, R. Kaushik, C. Agrawa, D. Singh, R. Narayanasamy, “Prediction of Mechanical Strength by using an Artificial Neural Network and Random Forest Algorithm”, J. Nanomaterials, Vol. 1, Pages 7791582,2021.
  • 32. U. Atici, “Prediction of the Strength of Mineral Admixture Concrete using Multivariable Regression Analysis and an Artificial Neural Network”, Expert Sys. Appl., Vol. 38, Issue 8, Pages 9609-9618,2011.
  • 33. J. Al-Azzeh, Z. Alqadi, and M. Abuzalata, “Performance Analysis of Artificial Neural Networks used for Color Image Recognition and Retrieving”, Inter. J. Comput. Sci. Mobile Comput., Vol. 8, Issue 2, Pages 20-33,2019.
  • 34. H.V.T. Mai, T.A. Nguyen, H.B. Ly, and V.Q. Tran, “Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast‐Furnace Slag and Fly Ash”, Adv. Mater. Sci. Eng., Vol. 1, Pages 5540853,2021.
  • 35. X. Zhou, P. Lu, Z. Zheng, D. Tolliver, and A. Keramati, “Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings using Random Forest Algorithm Compared with Decision Tree”, Reliability Eng. Sys. Safety, Vol. 200, Pages 106931, 2020.
  • 36. Z.N. Nemer, “Oil and Gas Production Forecasting Using Decision Trees, Random Forst, and XGBoost”, J. Al-Qadisiyah Comput. Sci. Math.,Vol. 16, Issue 1, Pages 9-20,2024.
  • 37. M.G. Abdolrasol, S.S. Hussain, T.S. Ustun, M.R. Sarker, MA. Hannan, R. Mohamed, A. Milad, Artificial Neural Networks Based Optimization Techniques: a Review, Electronics, Vol. 10, Issue 21, Pages 2689,2021.
  • 38. S.B. Torrisi, M.R. Carbone, B.A. Rohr, J.H. Montoya, Y. Ha, J. Yano, and L. Hung,” Random Forest Machine Learning Models For Interpretable X-Ray Absorption Near-Edge Structure Spectrum-Property Relationships”, npj Comput. Mater., Vol. 6, İssue 1,Pages 109,2020.
  • 39. T. Guillod, P. Papamanolis, and J.W. Kolar, “Artificial neural network (ANN) based Fast and Accurate Inductor Modeling and Design”, IEEE Open J Power Electron., Vol. 1, Pages 284-299,2020.
  • 40. J.R.S. Iruela, L.G.B. Ruiz, M.C. Pegalajar, and M.I. Capel, “A Parallel Solution with GPU Technology to Predict Energy Consumption in Spatially Distributed Buildings using Evolutionary Optimization and Artificial Neural Networks”, Energy Conv. Manag., Vol. 207, Pages 112535,2020.
  • 41. S. Menard, “Coefficients of Determination for Multiple Logistic Regression Analysis”, American Stat., Vol. 54, Issue 1, Pages 17-24, 2000.
  • 42. T.O. Hodson, “Root Mean Square Error (RMSE) or Mean Absolute Error (MAE): when to use them or not”, Geosci. Model Dev., Vol. 15, Pages 5481-5487,2022. 43. P.E. Dennison and D.A. Roberts, “Endmember Selection for Multiple Endmember Spectral Mixture Analysis using Endmember Average RMSE,” Remote Sens Environment, Vol. 87, Issue 2-3, Pages 123-135,2003.
  • 44. D. Chicco, M.J. Warrens, and G. Jurman, “The Coefficient of Determination R-Squared is more Informative than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation”,Peerj Comput. Sci., Vol. 5, Issue 7, Pages 623,2021.
  • 45. S. Marola, D. M. Gianluca Fiore, M.G. Poletti, M. Lombardi, P. Fino, and L. Battezzati, “A Comparison of Selective Laser Melting with bulk Rapid Solidification of AlSi10Mg alloy”, J Alloys Compounds, Vol.742, Pages 271-279,2018.
  • 46. K.G. Prashanth, S. Scudino, H.J. Klauss, K.B. Surreddi, L. Lober, Z. Wang, A.K. Chaubey, U. Kühn, and J. Eckert,” Microstructure and Mechanical Properties of Al-12Si Produced by Selective Laser Melting: Effect of Heat Treatment”, Mater. Sci. Eng. A, Vol. 590, Pages 153-160, 2014.
  • 47. L. Zhou, A. Mehta, E. Schulz, B. McWilliams, K. Cho, and Y. Sohn, “Microstructure, Precipitates and Hardness of Selectively Laser MELTED ALSI10MG Alloy before and after Heat Treatment”, Mater Char.,Vol. 143, Pages 5-17,2018.
  • 48. J. Murray and A. McAlister, The Al-Si (Aluminum-Silicon) System, J. Phase Equilib., Vol. 5, Pages 74-84,1984.
  • 49. M. Moustafa, F. Samuel, and H. Doty, “Effect of Solution Heat Treatment and Additives on the Microstructure of Al-Si (A413. 1) Automotive Alloys”, J. Mater. Sci., Vol. 38, Pages 4507-4522,2003.
  • 50. A.H. Demirci, “Malzeme Bilgisi ve Malzeme Muayenesi: Seçilmiş Temel Kavramlar ve Endüstriyel Uygulamalar (Materials Information and Materials Inspection: Selected Basic Concepts and Industrial Applications)”, Alfa Publishing, İstanbul (in Turkish), 2004.
  • 51. N. Suroor, A. Jaiswal, and N. Sachdeva, “Stack Ensemble Oriented Parkinson Disease Prediction Using Machine Learning Approaches Utilizing GridSearchCV-Based Hyper Parameter Tuning”, Critical Review. Biomedical Eng., Vol. 50, Issue 5, Pages 39-58, 2022.

PREDICTION OF HARDNESS VALUES OF AGED SELECTIVE LASER MELTED AlSi10Mg ALLOY DATA WITH MACHINE LEARNING METHODS

Yıl 2025, Cilt: 9 Sayı: 1, 53 - 62, 30.04.2025

Murat İnce , Hatice Varol Özkavak

https://doi.org/10.46519/ij3dptdi.1610116

Öz

Aluminum manufactured with the Selective Laser Melting (SLM) method has been the subject of many research due to the benefits it provides, especially when used in the automotive and aviation industries. Therefore, it is important to examine and improve the mechanical properties of Al parts produced by the SLM method. Many experiments are needed to examine and improve the mechanical properties of SLM Al materials. This situation causes losses in terms of both time and cost. In this study, aims to estimate the hardness values of SLM AlSi10Mg materials that have been aged. For this purpose, aging processes were applied to SLM AlSi10Mg materials at different times and temperatures, and different machine learning methods were used to predict the hardness values using the hardness values obtained because of the process. Random Forest Regression (RFR) algorithm and Artificial Neural Network (ANN) were used in the study. As a result of the study, it was determined that the hardness values estimated by the ANN (R2 0.9276) method were close to the real hardness values. This is proof that it is possible to predict hardness values using the machine learning method.

Anahtar Kelimeler

SLM AlSi10Mg Aging Artificial Neural Networks Machine Learning Regression

Kaynakça

  • 1. Gibson I., Rosen D.W., Stucker B., “Additive Manufacturing Technologies - Rapid Prototyping to Direct Digital Manufacturing”, Pages 1-625, USA,2015.
  • 2. J.H. Martin, B.D. Yahata, J.M. Hundley, J.A. Mayer, T.A. Schaedler, and T.M. Pollock, “3D Printing Of High-Strength Aluminium Alloys”, Nature, Vol. 549, Pages 365-369. 2017,
  • 3. A. Hadadzadeh, B.S. Amirkhiz, S. Shakerin, J. Kelly, J. Li, and M. Mohammadi, “Microstructural Investigation and Mechanical Behavior of a Two-Material Component Fabricated through Selective Laser Melting of AlSi10Mg on an Al-Cu-Ni-Fe-Mg Cast Alloy Substrate”, Addit. Manuf.,Vol. 31, Pages 100937,2020.
  • 5. A.G. Demir and C.A. Biffi, “Micro Laser Metal Wire Deposition Of Thin-Walled Al Alloy Components: Process And Material Characterization”, J. Manuf. Process., Vol. 37, Pages 362–369,2019.
  • 5. Q. Yan, B. Song, and Y. Shi, “Comparative Study Of Performance Comparison Of Alsi10mg Alloy Prepared By Selective Laser Melting And Casting”, J. Mater. Sci. Technol., Vol. 41, Pages 199–208,2020.
  • 6. Y. Cao, X. Lin, Q.Z. Wang, S.Q. Shi, L. Ma, N. Kang, and W.D. Huang, “Microstructure Evolution And Mechanical Properties at High Temperature of Selective Laser Melted AlSi10Mg”, J. Mater. Sci. Technol., Vol. 62, Pages 162-172, 2021,
  • 7. N.O. Larrosa, W. Wang, N. Read, M.H. Loretto, C. Evans, J. Carr, U. Tradowsky, M.M. Attallah, and P.J. Withers, “Linking Microstructure and Processing Defects to Mechanical Properties of Selectively Laser Melted AlSi10Mg Alloy”, Theor. Appl. Fract. Mech., Vol. 98, Pages 123-133, 2018.
  • 8. L. Zhuo, Z. Wang, H. Zhang, E. Yin, Y. Wang, T. Xu, and C. Li, “Effect of Post-Process Heat Treatment on Microstructure and Properties of Selective Laser Melted AlSi10Mg Alloy”, Mater. Lett., Vol. 234, Pages 196-200, 2019.
  • 9. J. Bi, Z. Lei, Y. Chen, X. Chen, N. Lu, Z. Tian, and X. Qin,” An Additively Manufactured Al-14.1 Mg-0.47 Si-0.31 Sc-0.17 Zr Alloy with High Specific Strength, Good Thermal Stability and Excellent Corrosion Resistance”, J. Mater. Sci. Technol., Vol. 67, Pages23-35, 2021,
  • 10. A. Heinz, A. Haszler, C. Keidel, S. Moldenhauer, R. Benedictus, and W.S. Miller, “Recent Development in alumiNIUM ALloys for Aerospace Applications”, Mater. Sci. Eng. A, Vol. 280, Pages 102-107,2000.
  • 11. F. Calignano, “Design Optimization of Supports for Overhanging Structures in Aluminum and Titanium Alloys by Selective Laser Melting”, Mater. Des., Vol. 64, Pages 203-213,2014.
  • 12. L. Thijs, K. Kempen, J.P. Kruth, and J. Van Humbeeck, “”Fine-structured Aluminium Products with Controllable Texture by Selective Laser Melting of Pre-alloyed AlSi10Mg Powder”, Acta Mater., Vol. 61, Pages 1809-1819, 2013.
  • 13. N.T. Aboulkhair, M. Simonelli, L. Parry, I. Ashcroft, C. Tuck, and R. Hague, “Prog. 3D Printing of Aluminium Alloys: Additive Manufacturing of Aluminium Alloys using Selective Laser Melting”, Mater. Sci., Vol. 106, Pages 100578, 2019.
  • 14. E. Brandl, U. Heckenberger, V. Holzinger, and D. Buchbinder, “Additive Manufactured AlSi10Mg Samples using Selective Laser Melting (SLM): Microstructure, High Cycle Fatigue, and Fracture Behavior”, Mater. Des., Vol. 34, Pages 159-169,2012.
  • 15. K. Kempen, L. Thijs, J. Van Humbeeck, and J.P. Kruth, “Processing AlSi10Mg by Selective Laser Melting: parameter optimisation and material characterisation”, Mater. Sci. Technol., Vol. 31, Pages 917-923,2015.
  • 16. N. Takata, H. Kodaira, K. Sekizawa, A. Suzuki, and M. Kobashi, “Change in Microstructure of Selectively Laser Melted AlSi10Mg Alloy with Heat Treatments”, Mater. Sci. Eng. A., Vol. 704, Pages 218-228,2017.
  • 17. R. Casati, M. Hamidi Nasab, M. Coduri, V. Tirelli, and M. Vedani, “Effects of Platform Pre-Heating and Thermal-Treatment Strategies on Properties of AlSi10Mg Alloy Processed by Selective Laser Melting”, Met., Basel, Vol. 8, Pages 954,2018.
  • 18. I. Rosenthal, R. Shneck, and A. Stern, “Heat Treatment Effect on the Mechanical Properties and Fracture Mechanism in Alsi10Mg Fabricated by Additive Manufacturing Selective Laser Melting Process”, Mater. Sci. Eng. A., Vol. 729, Pages 310-322,2018.
  • 19. Q. Han and Y. Jiao,” Effect of Heat Treatment and Laser Surface Remelting on Alsi10Mg Alloy Fabricated by Selective Laser Melting”, Int. J. Adv. Manuf. Technol., Vol. 102, Pages 3315-3324,2019.
  • 20. L.F. Wang, J. Sun, X.L. Yu, Y. Shi, X.G. Zhu, L.Y. Cheng, H.H. Liang, B. Yan, and L.J. Guo, “Enhancement in Mechanical Properties of Selectively Laser-Melted Alsi10mg Aluminum Alloys by T6-Like Heat Treatment”, Mater. Sci. Eng. A.,Vol. 734, Pages 299-310,2018.
  • 21. W. Li, S. Li, J. Liu, A. Zhang, Y. Zhou, Q. Wei, C. Yan, and Y. Shi, “Effect of Heat Treatment on Alsi10Mg Alloy Fabricated by Selective Laser Melting: Microstructure Evolution, Mechanical Properties And Fracture Mechanism”, Mater. Sci. Eng. A., Vol. 663, Pages 116-125,2016.
  • 22. X. Li, X. Wang, M. Saunders, A. Suvorova, L. Zhang, Y. Liu, M. Fang, Z. Huang, and T.B. Sercombe, “A Selective Laser Melting and Solution Heat Treatment Refined Al–12si Alloy with a Controllable Ultrafine Eutectic Microstructure and 25% Tensile Ductility”, Acta Mater.,Vol. 95, Pages 74-82,2015.
  • 23. E. Sjölander and S. Seifeddine, The Heat Treatment of Al–Si–Cu–Mg Casting Alloys, J. Mater. Process. Technol., Vol. 210, Pages 1249-1259,2010.
  • 24. M. Moustafa, F. Samuel, and H. Doty, “Effect of Solution Heat Treatment and Additives on the Microstructure of Al-Si (A413. 1) Automotive Alloys”, J. Mater. Sci., Vol. 38, Pages 4507-4522,2003.
  • 25. A.H. Maamoun, M. Elbestawi, G.K. Dosbaeva, and S.C. Veldhuis, “Thermal Post-Processing of Alsi10Mg Parts Produced by Selective Laser Melting using Recycled Powder”, Addit. Manuf., Vol. 21, Pages 234-247,2018.
  • 26. U.M.R. Paturi, S. Cheruku, V.P.K. Pasunuri, S. Salike, N.S. Reddy, and S. Cheruku, “Machine Learning and Statistical Approach in Modeling and Optimization of Surface Roughness in Wire Electrical Discharge Machining”, Mach. Learn. Appl., Vol. 6, Pages 100099,2021.
  • 27. U.M.R. Paturi, D.G. Vanga, R.B. Duggem, N. Kotkunde, N.S. Reddy, and S. Dutta, “Estimation of Surface Roughness of Direct Metal Laser Sintered Alsi10Mg using Artificial Neural Networks and Response Surface Methodology”, Mater. Manuf. Process.,Vol. 38, Issue 14, Pages 1798-1808,2023.
  • 28. Z. Zhan, N. Ao, Y. Hu, and C. Liu, Defect‐Induced Fatigue Scattering and Assessment of Additively Manufactured 300M-AerMet100 Steel: An Investigation Based on Experiments and Machine Learning, Eng. Fract. Mech., Vol. 264, Pages 108352, 2022.
  • 29. M. Marrey, E. Malekipour, H. El-Mounayri, and E.J. Faierson, A Framework for Optimizing Process Parameters in Powder Bed Fusion (PBF) Process Using Artificial Neural Network (ANN), Proc. Manuf., Vol. 34, Pages 505-515,2019.
  • 30. G.F.V. Voort, Metallography: Principles & Practice, ASM International, United States of America, 1999.
  • 31. K. Upreti, M. Verma, M. Agrawal, J. Garg, R. Kaushik, C. Agrawa, D. Singh, R. Narayanasamy, “Prediction of Mechanical Strength by using an Artificial Neural Network and Random Forest Algorithm”, J. Nanomaterials, Vol. 1, Pages 7791582,2021.
  • 32. U. Atici, “Prediction of the Strength of Mineral Admixture Concrete using Multivariable Regression Analysis and an Artificial Neural Network”, Expert Sys. Appl., Vol. 38, Issue 8, Pages 9609-9618,2011.
  • 33. J. Al-Azzeh, Z. Alqadi, and M. Abuzalata, “Performance Analysis of Artificial Neural Networks used for Color Image Recognition and Retrieving”, Inter. J. Comput. Sci. Mobile Comput., Vol. 8, Issue 2, Pages 20-33,2019.
  • 34. H.V.T. Mai, T.A. Nguyen, H.B. Ly, and V.Q. Tran, “Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast‐Furnace Slag and Fly Ash”, Adv. Mater. Sci. Eng., Vol. 1, Pages 5540853,2021.
  • 35. X. Zhou, P. Lu, Z. Zheng, D. Tolliver, and A. Keramati, “Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings using Random Forest Algorithm Compared with Decision Tree”, Reliability Eng. Sys. Safety, Vol. 200, Pages 106931, 2020.
  • 36. Z.N. Nemer, “Oil and Gas Production Forecasting Using Decision Trees, Random Forst, and XGBoost”, J. Al-Qadisiyah Comput. Sci. Math.,Vol. 16, Issue 1, Pages 9-20,2024.
  • 37. M.G. Abdolrasol, S.S. Hussain, T.S. Ustun, M.R. Sarker, MA. Hannan, R. Mohamed, A. Milad, Artificial Neural Networks Based Optimization Techniques: a Review, Electronics, Vol. 10, Issue 21, Pages 2689,2021.
  • 38. S.B. Torrisi, M.R. Carbone, B.A. Rohr, J.H. Montoya, Y. Ha, J. Yano, and L. Hung,” Random Forest Machine Learning Models For Interpretable X-Ray Absorption Near-Edge Structure Spectrum-Property Relationships”, npj Comput. Mater., Vol. 6, İssue 1,Pages 109,2020.
  • 39. T. Guillod, P. Papamanolis, and J.W. Kolar, “Artificial neural network (ANN) based Fast and Accurate Inductor Modeling and Design”, IEEE Open J Power Electron., Vol. 1, Pages 284-299,2020.
  • 40. J.R.S. Iruela, L.G.B. Ruiz, M.C. Pegalajar, and M.I. Capel, “A Parallel Solution with GPU Technology to Predict Energy Consumption in Spatially Distributed Buildings using Evolutionary Optimization and Artificial Neural Networks”, Energy Conv. Manag., Vol. 207, Pages 112535,2020.
  • 41. S. Menard, “Coefficients of Determination for Multiple Logistic Regression Analysis”, American Stat., Vol. 54, Issue 1, Pages 17-24, 2000.
  • 42. T.O. Hodson, “Root Mean Square Error (RMSE) or Mean Absolute Error (MAE): when to use them or not”, Geosci. Model Dev., Vol. 15, Pages 5481-5487,2022. 43. P.E. Dennison and D.A. Roberts, “Endmember Selection for Multiple Endmember Spectral Mixture Analysis using Endmember Average RMSE,” Remote Sens Environment, Vol. 87, Issue 2-3, Pages 123-135,2003.
  • 44. D. Chicco, M.J. Warrens, and G. Jurman, “The Coefficient of Determination R-Squared is more Informative than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation”,Peerj Comput. Sci., Vol. 5, Issue 7, Pages 623,2021.
  • 45. S. Marola, D. M. Gianluca Fiore, M.G. Poletti, M. Lombardi, P. Fino, and L. Battezzati, “A Comparison of Selective Laser Melting with bulk Rapid Solidification of AlSi10Mg alloy”, J Alloys Compounds, Vol.742, Pages 271-279,2018.
  • 46. K.G. Prashanth, S. Scudino, H.J. Klauss, K.B. Surreddi, L. Lober, Z. Wang, A.K. Chaubey, U. Kühn, and J. Eckert,” Microstructure and Mechanical Properties of Al-12Si Produced by Selective Laser Melting: Effect of Heat Treatment”, Mater. Sci. Eng. A, Vol. 590, Pages 153-160, 2014.
  • 47. L. Zhou, A. Mehta, E. Schulz, B. McWilliams, K. Cho, and Y. Sohn, “Microstructure, Precipitates and Hardness of Selectively Laser MELTED ALSI10MG Alloy before and after Heat Treatment”, Mater Char.,Vol. 143, Pages 5-17,2018.
  • 48. J. Murray and A. McAlister, The Al-Si (Aluminum-Silicon) System, J. Phase Equilib., Vol. 5, Pages 74-84,1984.
  • 49. M. Moustafa, F. Samuel, and H. Doty, “Effect of Solution Heat Treatment and Additives on the Microstructure of Al-Si (A413. 1) Automotive Alloys”, J. Mater. Sci., Vol. 38, Pages 4507-4522,2003.
  • 50. A.H. Demirci, “Malzeme Bilgisi ve Malzeme Muayenesi: Seçilmiş Temel Kavramlar ve Endüstriyel Uygulamalar (Materials Information and Materials Inspection: Selected Basic Concepts and Industrial Applications)”, Alfa Publishing, İstanbul (in Turkish), 2004.
  • 51. N. Suroor, A. Jaiswal, and N. Sachdeva, “Stack Ensemble Oriented Parkinson Disease Prediction Using Machine Learning Approaches Utilizing GridSearchCV-Based Hyper Parameter Tuning”, Critical Review. Biomedical Eng., Vol. 50, Issue 5, Pages 39-58, 2022.
Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Murat İnce 0000-0001-5566-5008

Hatice Varol Özkavak 0000-0002-0314-0119

Yayımlanma Tarihi 30 Nisan 2025
Gönderilme Tarihi 3 Ocak 2025
Kabul Tarihi 12 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 9 Sayı: 1

Kaynak Göster

APA İnce, M., & Varol Özkavak, H. (2025). PREDICTION OF HARDNESS VALUES OF AGED SELECTIVE LASER MELTED AlSi10Mg ALLOY DATA WITH MACHINE LEARNING METHODS. International Journal of 3D Printing Technologies and Digital Industry, 9(1), 53-62. https://doi.org/10.46519/ij3dptdi.1610116
AMA İnce M, Varol Özkavak H. PREDICTION OF HARDNESS VALUES OF AGED SELECTIVE LASER MELTED AlSi10Mg ALLOY DATA WITH MACHINE LEARNING METHODS. IJ3DPTDI. Nisan 2025;9(1):53-62. doi:10.46519/ij3dptdi.1610116
Chicago İnce, Murat, ve Hatice Varol Özkavak. “PREDICTION OF HARDNESS VALUES OF AGED SELECTIVE LASER MELTED AlSi10Mg ALLOY DATA WITH MACHINE LEARNING METHODS”. International Journal of 3D Printing Technologies and Digital Industry 9, sy. 1 (Nisan 2025): 53-62. https://doi.org/10.46519/ij3dptdi.1610116.
EndNote İnce M, Varol Özkavak H (01 Nisan 2025) PREDICTION OF HARDNESS VALUES OF AGED SELECTIVE LASER MELTED AlSi10Mg ALLOY DATA WITH MACHINE LEARNING METHODS. International Journal of 3D Printing Technologies and Digital Industry 9 1 53–62.
IEEE M. İnce ve H. Varol Özkavak, “PREDICTION OF HARDNESS VALUES OF AGED SELECTIVE LASER MELTED AlSi10Mg ALLOY DATA WITH MACHINE LEARNING METHODS”, IJ3DPTDI, c. 9, sy. 1, ss. 53–62, 2025, doi: 10.46519/ij3dptdi.1610116.
ISNAD İnce, Murat - Varol Özkavak, Hatice. “PREDICTION OF HARDNESS VALUES OF AGED SELECTIVE LASER MELTED AlSi10Mg ALLOY DATA WITH MACHINE LEARNING METHODS”. International Journal of 3D Printing Technologies and Digital Industry 9/1 (Nisan 2025), 53-62. https://doi.org/10.46519/ij3dptdi.1610116.
JAMA İnce M, Varol Özkavak H. PREDICTION OF HARDNESS VALUES OF AGED SELECTIVE LASER MELTED AlSi10Mg ALLOY DATA WITH MACHINE LEARNING METHODS. IJ3DPTDI. 2025;9:53–62.
MLA İnce, Murat ve Hatice Varol Özkavak. “PREDICTION OF HARDNESS VALUES OF AGED SELECTIVE LASER MELTED AlSi10Mg ALLOY DATA WITH MACHINE LEARNING METHODS”. International Journal of 3D Printing Technologies and Digital Industry, c. 9, sy. 1, 2025, ss. 53-62, doi:10.46519/ij3dptdi.1610116.
Vancouver İnce M, Varol Özkavak H. PREDICTION OF HARDNESS VALUES OF AGED SELECTIVE LASER MELTED AlSi10Mg ALLOY DATA WITH MACHINE LEARNING METHODS. IJ3DPTDI. 2025;9(1):53-62.
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