BABIČ, Matej ;KOVAČIČ, Miha ;FRAGASSA, Cristiano ;ŠTURM, Roman . Selective Laser Melting: A Novel Method for Surface Roughness Analysis. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 70, n.7-8, p. 313-324, july 2024. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/selective-laser-melting-a-novel-method-for-surface-roughness-analysis/>. Date accessed: 01 sep. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2024.1009.
Babič, M., Kovačič, M., Fragassa, C., & Šturm, R. (2024). Selective Laser Melting: A Novel Method for Surface Roughness Analysis. Strojniški vestnik - Journal of Mechanical Engineering, 70(7-8), 313-324. doi:http://dx.doi.org/10.5545/sv-jme.2024.1009
@article{sv-jmesv-jme.2024.1009,
author = {Matej Babič and Miha Kovačič and Cristiano Fragassa and Roman Šturm},
title = {Selective Laser Melting: A Novel Method for Surface Roughness Analysis},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {70},
number = {7-8},
year = {2024},
keywords = {additive manufacturing; selective laser melting; surface roughness; fractal geometry; network theory; genetic programming; },
abstract = {The present study introduces a novel approach to analyse the surface roughness of metal parts made by 3D selective laser melting (SLM). This technology, known for its ability to efficiently produce functional prototypes and limited-run series, is particularly effective when surface conditions directly meet usage requir ements. Thus, the suitability of surfaces is a critical factor, emphasizing the importance of new methods for predicting their quality. Here fractal geometry and network theory are integrated to delve into the complexities of SLM-produced surfaces, while machine learning and pattern recognition concepts are employed to evaluate the surface roughness. Specifically, genetic programming, artificial neural networks, support vector machine, random forest, k-nearest neighbors are compared in terms of accuracy demonstrating that only the first method provided valid estimation due to the presence of very little training data. Experimental work with EOS Maraging Steel MS1 and an EOS M 290 3D printer validates the method’s practicality and effectiveness. Then, the research offers a fresh perspective in surface analysis and has significant implications for quality control in additive manufacturing, potentially enhancing the precision and efficiency of 3D metal printing.},
issn = {0039-2480}, pages = {313-324}, doi = {10.5545/sv-jme.2024.1009},
url = {https://www.sv-jme.eu/sl/article/selective-laser-melting-a-novel-method-for-surface-roughness-analysis/}
}
Babič, M.,Kovačič, M.,Fragassa, C.,Šturm, R. 2024 July 70. Selective Laser Melting: A Novel Method for Surface Roughness Analysis. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 70:7-8
%A Babič, Matej %A Kovačič, Miha %A Fragassa, Cristiano %A Šturm, Roman %D 2024 %T Selective Laser Melting: A Novel Method for Surface Roughness Analysis %B 2024 %9 additive manufacturing; selective laser melting; surface roughness; fractal geometry; network theory; genetic programming; %! Selective Laser Melting: A Novel Method for Surface Roughness Analysis %K additive manufacturing; selective laser melting; surface roughness; fractal geometry; network theory; genetic programming; %X The present study introduces a novel approach to analyse the surface roughness of metal parts made by 3D selective laser melting (SLM). This technology, known for its ability to efficiently produce functional prototypes and limited-run series, is particularly effective when surface conditions directly meet usage requir ements. Thus, the suitability of surfaces is a critical factor, emphasizing the importance of new methods for predicting their quality. Here fractal geometry and network theory are integrated to delve into the complexities of SLM-produced surfaces, while machine learning and pattern recognition concepts are employed to evaluate the surface roughness. Specifically, genetic programming, artificial neural networks, support vector machine, random forest, k-nearest neighbors are compared in terms of accuracy demonstrating that only the first method provided valid estimation due to the presence of very little training data. Experimental work with EOS Maraging Steel MS1 and an EOS M 290 3D printer validates the method’s practicality and effectiveness. Then, the research offers a fresh perspective in surface analysis and has significant implications for quality control in additive manufacturing, potentially enhancing the precision and efficiency of 3D metal printing. %U https://www.sv-jme.eu/sl/article/selective-laser-melting-a-novel-method-for-surface-roughness-analysis/ %0 Journal Article %R 10.5545/sv-jme.2024.1009 %& 313 %P 12 %J Strojniški vestnik - Journal of Mechanical Engineering %V 70 %N 7-8 %@ 0039-2480 %8 2024-07-04 %7 2024-07-04
Babič, Matej, Miha Kovačič, Cristiano Fragassa, & Roman Šturm. "Selective Laser Melting: A Novel Method for Surface Roughness Analysis." Strojniški vestnik - Journal of Mechanical Engineering [Online], 70.7-8 (2024): 313-324. Web. 01 Sep. 2024
TY - JOUR AU - Babič, Matej AU - Kovačič, Miha AU - Fragassa, Cristiano AU - Šturm, Roman PY - 2024 TI - Selective Laser Melting: A Novel Method for Surface Roughness Analysis JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2024.1009 KW - additive manufacturing; selective laser melting; surface roughness; fractal geometry; network theory; genetic programming; N2 - The present study introduces a novel approach to analyse the surface roughness of metal parts made by 3D selective laser melting (SLM). This technology, known for its ability to efficiently produce functional prototypes and limited-run series, is particularly effective when surface conditions directly meet usage requir ements. Thus, the suitability of surfaces is a critical factor, emphasizing the importance of new methods for predicting their quality. Here fractal geometry and network theory are integrated to delve into the complexities of SLM-produced surfaces, while machine learning and pattern recognition concepts are employed to evaluate the surface roughness. Specifically, genetic programming, artificial neural networks, support vector machine, random forest, k-nearest neighbors are compared in terms of accuracy demonstrating that only the first method provided valid estimation due to the presence of very little training data. Experimental work with EOS Maraging Steel MS1 and an EOS M 290 3D printer validates the method’s practicality and effectiveness. Then, the research offers a fresh perspective in surface analysis and has significant implications for quality control in additive manufacturing, potentially enhancing the precision and efficiency of 3D metal printing. UR - https://www.sv-jme.eu/sl/article/selective-laser-melting-a-novel-method-for-surface-roughness-analysis/
@article{{sv-jme}{sv-jme.2024.1009},
author = {Babič, M., Kovačič, M., Fragassa, C., Šturm, R.},
title = {Selective Laser Melting: A Novel Method for Surface Roughness Analysis},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {70},
number = {7-8},
year = {2024},
doi = {10.5545/sv-jme.2024.1009},
url = {https://www.sv-jme.eu/sl/article/selective-laser-melting-a-novel-method-for-surface-roughness-analysis/}
}
TY - JOUR AU - Babič, Matej AU - Kovačič, Miha AU - Fragassa, Cristiano AU - Šturm, Roman PY - 2024/07/04 TI - Selective Laser Melting: A Novel Method for Surface Roughness Analysis JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 70, No 7-8 (2024): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2024.1009 KW - additive manufacturing, selective laser melting, surface roughness, fractal geometry, network theory, genetic programming, N2 - The present study introduces a novel approach to analyse the surface roughness of metal parts made by 3D selective laser melting (SLM). This technology, known for its ability to efficiently produce functional prototypes and limited-run series, is particularly effective when surface conditions directly meet usage requir ements. Thus, the suitability of surfaces is a critical factor, emphasizing the importance of new methods for predicting their quality. Here fractal geometry and network theory are integrated to delve into the complexities of SLM-produced surfaces, while machine learning and pattern recognition concepts are employed to evaluate the surface roughness. Specifically, genetic programming, artificial neural networks, support vector machine, random forest, k-nearest neighbors are compared in terms of accuracy demonstrating that only the first method provided valid estimation due to the presence of very little training data. Experimental work with EOS Maraging Steel MS1 and an EOS M 290 3D printer validates the method’s practicality and effectiveness. Then, the research offers a fresh perspective in surface analysis and has significant implications for quality control in additive manufacturing, potentially enhancing the precision and efficiency of 3D metal printing. UR - https://www.sv-jme.eu/sl/article/selective-laser-melting-a-novel-method-for-surface-roughness-analysis/
Babič, Matej, Kovačič, Miha, Fragassa, Cristiano, AND Šturm, Roman. "Selective Laser Melting: A Novel Method for Surface Roughness Analysis" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 70 Number 7-8 (04 July 2024)
Strojniški vestnik - Journal of Mechanical Engineering 70(2024)7-8, 313-324
© The Authors 2024. CC BY 4.0 Int.
The present study introduces a novel approach to analyse the surface roughness of metal parts made by 3D selective laser melting (SLM). This technology, known for its ability to efficiently produce functional prototypes and limited-run series, is particularly effective when surface conditions directly meet usage requir ements. Thus, the suitability of surfaces is a critical factor, emphasizing the importance of new methods for predicting their quality. Here fractal geometry and network theory are integrated to delve into the complexities of SLM-produced surfaces, while machine learning and pattern recognition concepts are employed to evaluate the surface roughness. Specifically, genetic programming, artificial neural networks, support vector machine, random forest, k-nearest neighbors are compared in terms of accuracy demonstrating that only the first method provided valid estimation due to the presence of very little training data. Experimental work with EOS Maraging Steel MS1 and an EOS M 290 3D printer validates the method’s practicality and effectiveness. Then, the research offers a fresh perspective in surface analysis and has significant implications for quality control in additive manufacturing, potentially enhancing the precision and efficiency of 3D metal printing.