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DEVELOPMENT OF A PHYSICS-INFORMED MELT POOL MODEL FOR POROSITY PREDICTION IN ADDITIVE MANUFACTURING

Yıl 2025, Cilt: 9 Sayı: 3, 488 - 502, 28.12.2025
https://doi.org/10.46519/ij3dptdi.1789827

Öz

Powder Bed Fusion–Laser Beam (PBF-LB) has emerged as a leading additive manufacturing technique for producing complex metallic components; however, its susceptibility to process-induced defects, particularly porosity, continues to limit its widespread application. In this study, a physics-informed computational framework was developed to predict porosity formation in Ti-6Al-4V parts by explicitly resolving transient thermal fields, melt pool dynamics, and layer-wise liquid fractions with temperature-dependent material properties. A dedicated graphical user interface was implemented, providing flexibility in defining the critical processing variables in PBF-LB. Model validation was performed using experimentally reported datasets from the literature. Benchmarking against melt pool geometries demonstrated that the algorithm successfully reproduced the depth and width evolution under different laser powers (100–195 W) and scan speeds (500–750 mm/s). Further comparisons with porosity data revealed strong quantitative consistency: for example, a numerical prediction of 0.19% porosity closely matched Archimedes (0.115%) and µ-CT (0.070%) results, while micrograph-based measurements indicated a higher value (0.204%). Across all investigated specimens, the algorithm reliably reflected experimentally observed porosity trends, including near fully dense conditions (<0.01%). The results demonstrate that the proposed framework provides an efficient and adaptable tool for predicting porosity in PBF-LB prior to fabrication.

Kaynakça

  • 1. Ero, O., Taherkhani, K., Toyserkani, E., “Optical tomography and machine learning for in-situ defects detection in laser powder bed fusion: A self-organizing map and U-Net based approach”, Additive Manufacturing, Vol. 78, Page 103894, 2023.
  • 2. Wang, S., Ning, J., Zhu, L., Yang, Z., Yan, W., Dun, Y., Xue, P., “Role of porosity defects in metal 3D printing: Formation mechanisms, impacts on properties and mitigation strategies”, Materials Today, Vol. 59, Pages 133-160, 2022.
  • 3. Gui, Y., Aoyagi, K., Bian, H., Chiba, A., “Detection, classification and prediction of internal defects from surface morphology data of metal parts fabricated by powder bed fusion type additive manufacturing using an electron beam”, Additive Manufacturing, Vol. 54, Page 102736, 2022.
  • 4. Guillen, D., Wahlquist, S., Ali, A., “Critical review of LPBF metal print defects detection: Roles of selective sensing technology”, Applied Sciences, Vol. 14, Issue 15, Page 6718, 2024.
  • 5. Gui, Y., Aoyagi, K., Chiba, A., “Development of macro-defect-free PBF-EB-processed Ti–6Al–4V alloys with superior plasticity using PREP-synthesized powder and machine learning-assisted process optimization”, Materials Science and Engineering: A, Vol. 864, Page 144595, 2023.
  • 6. Haiati, S., Dotchev, K., Lowther, M., “Utilizing powder bed fusion additive manufacturing technology to fabricate parts with controlled porosity and permeability characteristics for filtration applications”, International Journal of Precision Engineering and Manufacturing-Green Technology, Vol. 12, Issue 1, Pages 135-149, 2025.
  • 7. Pimenov, D.Y., Berti, L.F., Pintaude, G., Peres, G.X., Chaurasia, Y., Khanna, N., Giasin, K., “Influence of selective laser melting process parameters on the surface integrity of difficult-to-cut alloys: Comprehensive review and future prospects”, The International Journal of Advanced Manufacturing Technology, Vol. 127, Issue 3, Pages 1071-1102, 2023.
  • 8. Pi, Q., Li, R., Han, B., Yang, K., Hu, Y., Shi, Y., Qi, H., “Predicting the porosity of as-built additive manufactured samples based on machine learning method for small datasets”, Optics & Laser Technology, Vol. 177, Page 111203, 2024.
  • 9. Mohamed, A.M.F., Careri, F., Khan, R.H.U., Attallah, M.M., Stella, L., “A novel porosity prediction framework based on reinforcement learning for process parameter optimization in additive manufacturing”, Scripta Materialia, Vol. 255, Page 116377, 2025.
  • 10. Staszewska, A., Patil, D.P., Dixith, A.C., Neamtu, R., Lados, D.A., “A machine learning methodology for porosity classification and process map prediction in laser powder bed fusion”, Progress in Additive Manufacturing, Vol. 9, Issue 6, Pages 1901-1911, 2024.
  • 11. Westphal, E., Seitz, H., “A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks”, Additive Manufacturing, Vol. 41, Page 101965, 2021.
  • 12. Mohammed, A.S., Almutahhar, M., Sattar, K., Alhajeri, A., Nazir, A., Ali, U., “Deep learning based porosity prediction for additively manufactured laser powder-bed fusion parts”, Journal of Materials Research and Technology, Vol. 27, Pages 7330-7335, 2023.
  • 13. Li, B., Zhang, W., Xuan, F., “Machine-learning prediction of selective laser melting additively manufactured part density by feature-dimension-ascended Bayesian network model for process optimisation”, The International Journal of Advanced Manufacturing Technology, Vol. 121, No. 5, Pages 4023-4038, 2022.
  • 14. Kumar, S., Gopi, T., Harikeerthana, N., Gupta, M.K., Gaur, V., Krolczyk, G.M., Wu, C.S., “Machine learning techniques in additive manufacturing: A state of the art review on design, processes and production control”, Journal of Intelligent Manufacturing, Vol. 34, Issue 1, Pages 21-55, 2023.
  • 15. Smoqi, Z., Gaikwad, A., Bevans, B., Kobir, M.H., Craig, J., Abul-Haj, A., Peralta, A., Rao, P., “Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning”, Journal of Materials Processing Technology, Vol. 304, Page 117550, 2022.
  • 16. Park, J.M., Choi, M., Um, J., “Convolutional LSTM based melt-pool prediction from images of laser tool path strategy in laser powder bed fusion for additive manufacturing”, The International Journal of Advanced Manufacturing Technology, Vol. 130, Issue 3, Pages 1871-1886, 2024.
  • 17. Carter III, F.M., Porter, C., Kozjek, D., Shimoyoshi, K., Fujishima, M., Irino, N., Cao, J., “Machine learning guided adaptive laser power control in selective laser melting for pore reduction”, CIRP Annals, Vol. 73, Issue 1, Pages 149-152, 2024.
  • 18. Zhong, Q., Wei, K., Lu, Z., Yue, X., Ouyang, T., Zeng, X., “High power laser powder bed fusion of Inconel 718 alloy: Effect of laser focus shift on formability, microstructure and mechanical properties”, Journal of Materials Processing Technology, Vol. 311, Page 117824, 2023.
  • 19. Yıldız, A.K., Mollamahmutoğlu, M., Yılmaz, O., “Computational evaluation of the effect of build orientation on thermal behavior and in-situ martensite decomposition for laser powder-bed fusion (LPBF) process”, Gazi University Journal of Science, Vol. 36, Issue 2, Pages 870-880, 2023.
  • 20. Slama, M.B., Chatti, S., Kolsi, L., “Effect of processing parameters on porosity defects during SLM: A DOE-FEM approach”, Welding in the World, Vol. 67, Issue 9, Pages 2201-2213, 2023.
  • 21. Zhang, Z.-D., Shahabad, S.I., Dibia, C.F., Bonakdar, A., Toyserkani, E., “3-Dimensional heat transfer modeling for laser powder bed fusion additive manufacturing using parallel computing and adaptive mesh”, Optics & Laser Technology, Vol. 158, Page 108839, 2023.
  • 22. Cook, P.S., Ritchie, D.J., “Determining the laser absorptivity of Ti-6Al-4V during laser powder bed fusion by calibrated melt pool simulation”, Optics & Laser Technology, Vol. 162, Page 109247, 2023.
  • 23. Liu, B., Fang, G., Lei, L., Yan, X., “Predicting the porosity defects in selective laser melting (SLM) by molten pool geometry”, International Journal of Mechanical Sciences, Vol. 228, Page 107478, 2022.
  • 24. Majeed, M., Vural, M., Raja, S., Shaikh, M.B.N., “Finite element analysis of thermal behavior in maraging steel during SLM process”, Optik, Vol. 208, Page 164128, 2020.
  • 25. Ansari Dezfoli, A.R., Lo, Y.-L., Raza, M.M., “Prediction of epitaxial grain growth in single-track laser melting of IN718 using integrated finite element and cellular automaton approach”, Materials, Vol. 14, Issue 18, Page 5202, 2021.
  • 26. Domine, A., Verdy, C., Penaud, C., Vitu, L., Fenineche, N., Dembinski, L., “Selective laser melting (SLM) of pure copper using 515-nm green laser: From single track analysis to mechanical and electrical characterization”, The International Journal of Advanced Manufacturing Technology, Pages 1-12, 2023.
  • 27. Yildiz, A. K., Mollamahmutoglu, M., Yilmaz, O., “Computational evaluation of temperature-dependent microstructural transformations of Ti6Al4V for laser powder bed fusion process”, Journal of Materials Engineering and Performance, Vol. 31, Issue 9, Pages 7191-7203, 2022.
  • 28. Dilip, J.J.S., Zhang, S., Teng, C., Zeng, K., Robinson, C., Pal, D., Stucker, B., “Influence of processing parameters on the evolution of melt pool, porosity, and microstructures in Ti-6Al-4V alloy parts fabricated by selective laser melting”, Progress in Additive Manufacturing, Vol. 2, Issue 3, Pages 157-167, 2017.
  • 29. Zalameda, J. N., Hocker, S. J., Tayon, W. A., Fody, J. M., Richter, B. M., “Comparison of in-situ near infrared melt pool imagery to optical microscopy measurements”, In Thermosense: Thermal Infrared Applications XLIV, Vol. 12109, Page 1210902, 2022.
  • 30. Promoppatum, P., Srinivasan, R., Quek, S.S., Msolli, S., Shukla, S., Johan, N.S., van der Veen, S., Jhon, M.H., “Quantification and prediction of lack-of-fusion porosity in the high porosity regime during laser powder bed fusion of Ti-6Al-4V”, Journal of Materials Processing Technology, Vol. 300, Page 117426, 2022.
  • 31. Derimow, N., Madrigal Camacho, M., Kafka, O.L., Benzing, J.T., Garboczi, E.J., Clark, S.J., Fezzaa, K., Mathaudhu, S., Hrabe, N., “Investigation of melt pool dynamics and solidification microstructures of laser melted Ti-6Al-4V powder using X-ray synchrotron imaging”, Journal of Alloys and Metallurgical Systems, Vol. 6, Page 100070, 2024.
  • 32. Shen, T., Zhang, W., Li, B., “Machine learning-enabled predictions of as-built relative density and high-cycle fatigue life of Ti6Al4V alloy additively manufactured by laser powder bed fusion”, Materials Today Communications, Vol. 37, Page 107286, 2023.

DEVELOPMENT OF A PHYSICS-INFORMED MELT POOL MODEL FOR POROSITY PREDICTION IN ADDITIVE MANUFACTURING

Yıl 2025, Cilt: 9 Sayı: 3, 488 - 502, 28.12.2025
https://doi.org/10.46519/ij3dptdi.1789827

Öz

Powder Bed Fusion–Laser Beam (PBF-LB) has emerged as a leading additive manufacturing technique for producing complex metallic components; however, its susceptibility to process-induced defects, particularly porosity, continues to limit its widespread application. In this study, a physics-informed computational framework was developed to predict porosity formation in Ti-6Al-4V parts by explicitly resolving transient thermal fields, melt pool dynamics, and layer-wise liquid fractions with temperature-dependent material properties. A dedicated graphical user interface was implemented, providing flexibility in defining the critical processing variables in PBF-LB. Model validation was performed using experimentally reported datasets from the literature. Benchmarking against melt pool geometries demonstrated that the algorithm successfully reproduced the depth and width evolution under different laser powers (100–195 W) and scan speeds (500–750 mm/s). Further comparisons with porosity data revealed strong quantitative consistency: for example, a numerical prediction of 0.19% porosity closely matched Archimedes (0.115%) and µ-CT (0.070%) results, while micrograph-based measurements indicated a higher value (0.204%). Across all investigated specimens, the algorithm reliably reflected experimentally observed porosity trends, including near fully dense conditions (<0.01%). The results demonstrate that the proposed framework provides an efficient and adaptable tool for predicting porosity in PBF-LB prior to fabrication.

Kaynakça

  • 1. Ero, O., Taherkhani, K., Toyserkani, E., “Optical tomography and machine learning for in-situ defects detection in laser powder bed fusion: A self-organizing map and U-Net based approach”, Additive Manufacturing, Vol. 78, Page 103894, 2023.
  • 2. Wang, S., Ning, J., Zhu, L., Yang, Z., Yan, W., Dun, Y., Xue, P., “Role of porosity defects in metal 3D printing: Formation mechanisms, impacts on properties and mitigation strategies”, Materials Today, Vol. 59, Pages 133-160, 2022.
  • 3. Gui, Y., Aoyagi, K., Bian, H., Chiba, A., “Detection, classification and prediction of internal defects from surface morphology data of metal parts fabricated by powder bed fusion type additive manufacturing using an electron beam”, Additive Manufacturing, Vol. 54, Page 102736, 2022.
  • 4. Guillen, D., Wahlquist, S., Ali, A., “Critical review of LPBF metal print defects detection: Roles of selective sensing technology”, Applied Sciences, Vol. 14, Issue 15, Page 6718, 2024.
  • 5. Gui, Y., Aoyagi, K., Chiba, A., “Development of macro-defect-free PBF-EB-processed Ti–6Al–4V alloys with superior plasticity using PREP-synthesized powder and machine learning-assisted process optimization”, Materials Science and Engineering: A, Vol. 864, Page 144595, 2023.
  • 6. Haiati, S., Dotchev, K., Lowther, M., “Utilizing powder bed fusion additive manufacturing technology to fabricate parts with controlled porosity and permeability characteristics for filtration applications”, International Journal of Precision Engineering and Manufacturing-Green Technology, Vol. 12, Issue 1, Pages 135-149, 2025.
  • 7. Pimenov, D.Y., Berti, L.F., Pintaude, G., Peres, G.X., Chaurasia, Y., Khanna, N., Giasin, K., “Influence of selective laser melting process parameters on the surface integrity of difficult-to-cut alloys: Comprehensive review and future prospects”, The International Journal of Advanced Manufacturing Technology, Vol. 127, Issue 3, Pages 1071-1102, 2023.
  • 8. Pi, Q., Li, R., Han, B., Yang, K., Hu, Y., Shi, Y., Qi, H., “Predicting the porosity of as-built additive manufactured samples based on machine learning method for small datasets”, Optics & Laser Technology, Vol. 177, Page 111203, 2024.
  • 9. Mohamed, A.M.F., Careri, F., Khan, R.H.U., Attallah, M.M., Stella, L., “A novel porosity prediction framework based on reinforcement learning for process parameter optimization in additive manufacturing”, Scripta Materialia, Vol. 255, Page 116377, 2025.
  • 10. Staszewska, A., Patil, D.P., Dixith, A.C., Neamtu, R., Lados, D.A., “A machine learning methodology for porosity classification and process map prediction in laser powder bed fusion”, Progress in Additive Manufacturing, Vol. 9, Issue 6, Pages 1901-1911, 2024.
  • 11. Westphal, E., Seitz, H., “A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks”, Additive Manufacturing, Vol. 41, Page 101965, 2021.
  • 12. Mohammed, A.S., Almutahhar, M., Sattar, K., Alhajeri, A., Nazir, A., Ali, U., “Deep learning based porosity prediction for additively manufactured laser powder-bed fusion parts”, Journal of Materials Research and Technology, Vol. 27, Pages 7330-7335, 2023.
  • 13. Li, B., Zhang, W., Xuan, F., “Machine-learning prediction of selective laser melting additively manufactured part density by feature-dimension-ascended Bayesian network model for process optimisation”, The International Journal of Advanced Manufacturing Technology, Vol. 121, No. 5, Pages 4023-4038, 2022.
  • 14. Kumar, S., Gopi, T., Harikeerthana, N., Gupta, M.K., Gaur, V., Krolczyk, G.M., Wu, C.S., “Machine learning techniques in additive manufacturing: A state of the art review on design, processes and production control”, Journal of Intelligent Manufacturing, Vol. 34, Issue 1, Pages 21-55, 2023.
  • 15. Smoqi, Z., Gaikwad, A., Bevans, B., Kobir, M.H., Craig, J., Abul-Haj, A., Peralta, A., Rao, P., “Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning”, Journal of Materials Processing Technology, Vol. 304, Page 117550, 2022.
  • 16. Park, J.M., Choi, M., Um, J., “Convolutional LSTM based melt-pool prediction from images of laser tool path strategy in laser powder bed fusion for additive manufacturing”, The International Journal of Advanced Manufacturing Technology, Vol. 130, Issue 3, Pages 1871-1886, 2024.
  • 17. Carter III, F.M., Porter, C., Kozjek, D., Shimoyoshi, K., Fujishima, M., Irino, N., Cao, J., “Machine learning guided adaptive laser power control in selective laser melting for pore reduction”, CIRP Annals, Vol. 73, Issue 1, Pages 149-152, 2024.
  • 18. Zhong, Q., Wei, K., Lu, Z., Yue, X., Ouyang, T., Zeng, X., “High power laser powder bed fusion of Inconel 718 alloy: Effect of laser focus shift on formability, microstructure and mechanical properties”, Journal of Materials Processing Technology, Vol. 311, Page 117824, 2023.
  • 19. Yıldız, A.K., Mollamahmutoğlu, M., Yılmaz, O., “Computational evaluation of the effect of build orientation on thermal behavior and in-situ martensite decomposition for laser powder-bed fusion (LPBF) process”, Gazi University Journal of Science, Vol. 36, Issue 2, Pages 870-880, 2023.
  • 20. Slama, M.B., Chatti, S., Kolsi, L., “Effect of processing parameters on porosity defects during SLM: A DOE-FEM approach”, Welding in the World, Vol. 67, Issue 9, Pages 2201-2213, 2023.
  • 21. Zhang, Z.-D., Shahabad, S.I., Dibia, C.F., Bonakdar, A., Toyserkani, E., “3-Dimensional heat transfer modeling for laser powder bed fusion additive manufacturing using parallel computing and adaptive mesh”, Optics & Laser Technology, Vol. 158, Page 108839, 2023.
  • 22. Cook, P.S., Ritchie, D.J., “Determining the laser absorptivity of Ti-6Al-4V during laser powder bed fusion by calibrated melt pool simulation”, Optics & Laser Technology, Vol. 162, Page 109247, 2023.
  • 23. Liu, B., Fang, G., Lei, L., Yan, X., “Predicting the porosity defects in selective laser melting (SLM) by molten pool geometry”, International Journal of Mechanical Sciences, Vol. 228, Page 107478, 2022.
  • 24. Majeed, M., Vural, M., Raja, S., Shaikh, M.B.N., “Finite element analysis of thermal behavior in maraging steel during SLM process”, Optik, Vol. 208, Page 164128, 2020.
  • 25. Ansari Dezfoli, A.R., Lo, Y.-L., Raza, M.M., “Prediction of epitaxial grain growth in single-track laser melting of IN718 using integrated finite element and cellular automaton approach”, Materials, Vol. 14, Issue 18, Page 5202, 2021.
  • 26. Domine, A., Verdy, C., Penaud, C., Vitu, L., Fenineche, N., Dembinski, L., “Selective laser melting (SLM) of pure copper using 515-nm green laser: From single track analysis to mechanical and electrical characterization”, The International Journal of Advanced Manufacturing Technology, Pages 1-12, 2023.
  • 27. Yildiz, A. K., Mollamahmutoglu, M., Yilmaz, O., “Computational evaluation of temperature-dependent microstructural transformations of Ti6Al4V for laser powder bed fusion process”, Journal of Materials Engineering and Performance, Vol. 31, Issue 9, Pages 7191-7203, 2022.
  • 28. Dilip, J.J.S., Zhang, S., Teng, C., Zeng, K., Robinson, C., Pal, D., Stucker, B., “Influence of processing parameters on the evolution of melt pool, porosity, and microstructures in Ti-6Al-4V alloy parts fabricated by selective laser melting”, Progress in Additive Manufacturing, Vol. 2, Issue 3, Pages 157-167, 2017.
  • 29. Zalameda, J. N., Hocker, S. J., Tayon, W. A., Fody, J. M., Richter, B. M., “Comparison of in-situ near infrared melt pool imagery to optical microscopy measurements”, In Thermosense: Thermal Infrared Applications XLIV, Vol. 12109, Page 1210902, 2022.
  • 30. Promoppatum, P., Srinivasan, R., Quek, S.S., Msolli, S., Shukla, S., Johan, N.S., van der Veen, S., Jhon, M.H., “Quantification and prediction of lack-of-fusion porosity in the high porosity regime during laser powder bed fusion of Ti-6Al-4V”, Journal of Materials Processing Technology, Vol. 300, Page 117426, 2022.
  • 31. Derimow, N., Madrigal Camacho, M., Kafka, O.L., Benzing, J.T., Garboczi, E.J., Clark, S.J., Fezzaa, K., Mathaudhu, S., Hrabe, N., “Investigation of melt pool dynamics and solidification microstructures of laser melted Ti-6Al-4V powder using X-ray synchrotron imaging”, Journal of Alloys and Metallurgical Systems, Vol. 6, Page 100070, 2024.
  • 32. Shen, T., Zhang, W., Li, B., “Machine learning-enabled predictions of as-built relative density and high-cycle fatigue life of Ti6Al4V alloy additively manufactured by laser powder bed fusion”, Materials Today Communications, Vol. 37, Page 107286, 2023.

Yıl 2025, Cilt: 9 Sayı: 3, 488 - 502, 28.12.2025
https://doi.org/10.46519/ij3dptdi.1789827

Öz

Kaynakça

  • 1. Ero, O., Taherkhani, K., Toyserkani, E., “Optical tomography and machine learning for in-situ defects detection in laser powder bed fusion: A self-organizing map and U-Net based approach”, Additive Manufacturing, Vol. 78, Page 103894, 2023.
  • 2. Wang, S., Ning, J., Zhu, L., Yang, Z., Yan, W., Dun, Y., Xue, P., “Role of porosity defects in metal 3D printing: Formation mechanisms, impacts on properties and mitigation strategies”, Materials Today, Vol. 59, Pages 133-160, 2022.
  • 3. Gui, Y., Aoyagi, K., Bian, H., Chiba, A., “Detection, classification and prediction of internal defects from surface morphology data of metal parts fabricated by powder bed fusion type additive manufacturing using an electron beam”, Additive Manufacturing, Vol. 54, Page 102736, 2022.
  • 4. Guillen, D., Wahlquist, S., Ali, A., “Critical review of LPBF metal print defects detection: Roles of selective sensing technology”, Applied Sciences, Vol. 14, Issue 15, Page 6718, 2024.
  • 5. Gui, Y., Aoyagi, K., Chiba, A., “Development of macro-defect-free PBF-EB-processed Ti–6Al–4V alloys with superior plasticity using PREP-synthesized powder and machine learning-assisted process optimization”, Materials Science and Engineering: A, Vol. 864, Page 144595, 2023.
  • 6. Haiati, S., Dotchev, K., Lowther, M., “Utilizing powder bed fusion additive manufacturing technology to fabricate parts with controlled porosity and permeability characteristics for filtration applications”, International Journal of Precision Engineering and Manufacturing-Green Technology, Vol. 12, Issue 1, Pages 135-149, 2025.
  • 7. Pimenov, D.Y., Berti, L.F., Pintaude, G., Peres, G.X., Chaurasia, Y., Khanna, N., Giasin, K., “Influence of selective laser melting process parameters on the surface integrity of difficult-to-cut alloys: Comprehensive review and future prospects”, The International Journal of Advanced Manufacturing Technology, Vol. 127, Issue 3, Pages 1071-1102, 2023.
  • 8. Pi, Q., Li, R., Han, B., Yang, K., Hu, Y., Shi, Y., Qi, H., “Predicting the porosity of as-built additive manufactured samples based on machine learning method for small datasets”, Optics & Laser Technology, Vol. 177, Page 111203, 2024.
  • 9. Mohamed, A.M.F., Careri, F., Khan, R.H.U., Attallah, M.M., Stella, L., “A novel porosity prediction framework based on reinforcement learning for process parameter optimization in additive manufacturing”, Scripta Materialia, Vol. 255, Page 116377, 2025.
  • 10. Staszewska, A., Patil, D.P., Dixith, A.C., Neamtu, R., Lados, D.A., “A machine learning methodology for porosity classification and process map prediction in laser powder bed fusion”, Progress in Additive Manufacturing, Vol. 9, Issue 6, Pages 1901-1911, 2024.
  • 11. Westphal, E., Seitz, H., “A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks”, Additive Manufacturing, Vol. 41, Page 101965, 2021.
  • 12. Mohammed, A.S., Almutahhar, M., Sattar, K., Alhajeri, A., Nazir, A., Ali, U., “Deep learning based porosity prediction for additively manufactured laser powder-bed fusion parts”, Journal of Materials Research and Technology, Vol. 27, Pages 7330-7335, 2023.
  • 13. Li, B., Zhang, W., Xuan, F., “Machine-learning prediction of selective laser melting additively manufactured part density by feature-dimension-ascended Bayesian network model for process optimisation”, The International Journal of Advanced Manufacturing Technology, Vol. 121, No. 5, Pages 4023-4038, 2022.
  • 14. Kumar, S., Gopi, T., Harikeerthana, N., Gupta, M.K., Gaur, V., Krolczyk, G.M., Wu, C.S., “Machine learning techniques in additive manufacturing: A state of the art review on design, processes and production control”, Journal of Intelligent Manufacturing, Vol. 34, Issue 1, Pages 21-55, 2023.
  • 15. Smoqi, Z., Gaikwad, A., Bevans, B., Kobir, M.H., Craig, J., Abul-Haj, A., Peralta, A., Rao, P., “Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning”, Journal of Materials Processing Technology, Vol. 304, Page 117550, 2022.
  • 16. Park, J.M., Choi, M., Um, J., “Convolutional LSTM based melt-pool prediction from images of laser tool path strategy in laser powder bed fusion for additive manufacturing”, The International Journal of Advanced Manufacturing Technology, Vol. 130, Issue 3, Pages 1871-1886, 2024.
  • 17. Carter III, F.M., Porter, C., Kozjek, D., Shimoyoshi, K., Fujishima, M., Irino, N., Cao, J., “Machine learning guided adaptive laser power control in selective laser melting for pore reduction”, CIRP Annals, Vol. 73, Issue 1, Pages 149-152, 2024.
  • 18. Zhong, Q., Wei, K., Lu, Z., Yue, X., Ouyang, T., Zeng, X., “High power laser powder bed fusion of Inconel 718 alloy: Effect of laser focus shift on formability, microstructure and mechanical properties”, Journal of Materials Processing Technology, Vol. 311, Page 117824, 2023.
  • 19. Yıldız, A.K., Mollamahmutoğlu, M., Yılmaz, O., “Computational evaluation of the effect of build orientation on thermal behavior and in-situ martensite decomposition for laser powder-bed fusion (LPBF) process”, Gazi University Journal of Science, Vol. 36, Issue 2, Pages 870-880, 2023.
  • 20. Slama, M.B., Chatti, S., Kolsi, L., “Effect of processing parameters on porosity defects during SLM: A DOE-FEM approach”, Welding in the World, Vol. 67, Issue 9, Pages 2201-2213, 2023.
  • 21. Zhang, Z.-D., Shahabad, S.I., Dibia, C.F., Bonakdar, A., Toyserkani, E., “3-Dimensional heat transfer modeling for laser powder bed fusion additive manufacturing using parallel computing and adaptive mesh”, Optics & Laser Technology, Vol. 158, Page 108839, 2023.
  • 22. Cook, P.S., Ritchie, D.J., “Determining the laser absorptivity of Ti-6Al-4V during laser powder bed fusion by calibrated melt pool simulation”, Optics & Laser Technology, Vol. 162, Page 109247, 2023.
  • 23. Liu, B., Fang, G., Lei, L., Yan, X., “Predicting the porosity defects in selective laser melting (SLM) by molten pool geometry”, International Journal of Mechanical Sciences, Vol. 228, Page 107478, 2022.
  • 24. Majeed, M., Vural, M., Raja, S., Shaikh, M.B.N., “Finite element analysis of thermal behavior in maraging steel during SLM process”, Optik, Vol. 208, Page 164128, 2020.
  • 25. Ansari Dezfoli, A.R., Lo, Y.-L., Raza, M.M., “Prediction of epitaxial grain growth in single-track laser melting of IN718 using integrated finite element and cellular automaton approach”, Materials, Vol. 14, Issue 18, Page 5202, 2021.
  • 26. Domine, A., Verdy, C., Penaud, C., Vitu, L., Fenineche, N., Dembinski, L., “Selective laser melting (SLM) of pure copper using 515-nm green laser: From single track analysis to mechanical and electrical characterization”, The International Journal of Advanced Manufacturing Technology, Pages 1-12, 2023.
  • 27. Yildiz, A. K., Mollamahmutoglu, M., Yilmaz, O., “Computational evaluation of temperature-dependent microstructural transformations of Ti6Al4V for laser powder bed fusion process”, Journal of Materials Engineering and Performance, Vol. 31, Issue 9, Pages 7191-7203, 2022.
  • 28. Dilip, J.J.S., Zhang, S., Teng, C., Zeng, K., Robinson, C., Pal, D., Stucker, B., “Influence of processing parameters on the evolution of melt pool, porosity, and microstructures in Ti-6Al-4V alloy parts fabricated by selective laser melting”, Progress in Additive Manufacturing, Vol. 2, Issue 3, Pages 157-167, 2017.
  • 29. Zalameda, J. N., Hocker, S. J., Tayon, W. A., Fody, J. M., Richter, B. M., “Comparison of in-situ near infrared melt pool imagery to optical microscopy measurements”, In Thermosense: Thermal Infrared Applications XLIV, Vol. 12109, Page 1210902, 2022.
  • 30. Promoppatum, P., Srinivasan, R., Quek, S.S., Msolli, S., Shukla, S., Johan, N.S., van der Veen, S., Jhon, M.H., “Quantification and prediction of lack-of-fusion porosity in the high porosity regime during laser powder bed fusion of Ti-6Al-4V”, Journal of Materials Processing Technology, Vol. 300, Page 117426, 2022.
  • 31. Derimow, N., Madrigal Camacho, M., Kafka, O.L., Benzing, J.T., Garboczi, E.J., Clark, S.J., Fezzaa, K., Mathaudhu, S., Hrabe, N., “Investigation of melt pool dynamics and solidification microstructures of laser melted Ti-6Al-4V powder using X-ray synchrotron imaging”, Journal of Alloys and Metallurgical Systems, Vol. 6, Page 100070, 2024.
  • 32. Shen, T., Zhang, W., Li, B., “Machine learning-enabled predictions of as-built relative density and high-cycle fatigue life of Ti6Al4V alloy additively manufactured by laser powder bed fusion”, Materials Today Communications, Vol. 37, Page 107286, 2023.
Toplam 32 adet kaynakça vardır.

Ayrıntılar

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

İbrahim Ülke 0000-0002-8927-0052

Oğuzhan Yılmaz 0000-0002-2641-2324

Mehmet Mollamahmutoğlu 0000-0002-7202-5034

Gönderilme Tarihi 23 Eylül 2025
Kabul Tarihi 15 Kasım 2025
Yayımlanma Tarihi 28 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 9 Sayı: 3

Kaynak Göster

APA Ülke, İ., Yılmaz, O., & Mollamahmutoğlu, M. (2025). DEVELOPMENT OF A PHYSICS-INFORMED MELT POOL MODEL FOR POROSITY PREDICTION IN ADDITIVE MANUFACTURING. International Journal of 3D Printing Technologies and Digital Industry, 9(3), 488-502. https://doi.org/10.46519/ij3dptdi.1789827
AMA Ülke İ, Yılmaz O, Mollamahmutoğlu M. DEVELOPMENT OF A PHYSICS-INFORMED MELT POOL MODEL FOR POROSITY PREDICTION IN ADDITIVE MANUFACTURING. IJ3DPTDI. Aralık 2025;9(3):488-502. doi:10.46519/ij3dptdi.1789827
Chicago Ülke, İbrahim, Oğuzhan Yılmaz, ve Mehmet Mollamahmutoğlu. “DEVELOPMENT OF A PHYSICS-INFORMED MELT POOL MODEL FOR POROSITY PREDICTION IN ADDITIVE MANUFACTURING”. International Journal of 3D Printing Technologies and Digital Industry 9, sy. 3 (Aralık 2025): 488-502. https://doi.org/10.46519/ij3dptdi.1789827.
EndNote Ülke İ, Yılmaz O, Mollamahmutoğlu M (01 Aralık 2025) DEVELOPMENT OF A PHYSICS-INFORMED MELT POOL MODEL FOR POROSITY PREDICTION IN ADDITIVE MANUFACTURING. International Journal of 3D Printing Technologies and Digital Industry 9 3 488–502.
IEEE İ. Ülke, O. Yılmaz, ve M. Mollamahmutoğlu, “DEVELOPMENT OF A PHYSICS-INFORMED MELT POOL MODEL FOR POROSITY PREDICTION IN ADDITIVE MANUFACTURING”, IJ3DPTDI, c. 9, sy. 3, ss. 488–502, 2025, doi: 10.46519/ij3dptdi.1789827.
ISNAD Ülke, İbrahim vd. “DEVELOPMENT OF A PHYSICS-INFORMED MELT POOL MODEL FOR POROSITY PREDICTION IN ADDITIVE MANUFACTURING”. International Journal of 3D Printing Technologies and Digital Industry 9/3 (Aralık2025), 488-502. https://doi.org/10.46519/ij3dptdi.1789827.
JAMA Ülke İ, Yılmaz O, Mollamahmutoğlu M. DEVELOPMENT OF A PHYSICS-INFORMED MELT POOL MODEL FOR POROSITY PREDICTION IN ADDITIVE MANUFACTURING. IJ3DPTDI. 2025;9:488–502.
MLA Ülke, İbrahim vd. “DEVELOPMENT OF A PHYSICS-INFORMED MELT POOL MODEL FOR POROSITY PREDICTION IN ADDITIVE MANUFACTURING”. International Journal of 3D Printing Technologies and Digital Industry, c. 9, sy. 3, 2025, ss. 488-02, doi:10.46519/ij3dptdi.1789827.
Vancouver Ülke İ, Yılmaz O, Mollamahmutoğlu M. DEVELOPMENT OF A PHYSICS-INFORMED MELT POOL MODEL FOR POROSITY PREDICTION IN ADDITIVE MANUFACTURING. IJ3DPTDI. 2025;9(3):488-502.

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