Powder Bed Fusion via Machine Learning-Enabled Approaches

Chadha, Utkarsh; Selvaraj, Senthil Kumaran; Abraham, Abel Saji; Khanna, Mayank; Mishra, Anirudh; Sachdeva, Isha; Kashyap, Swati; Dev, Sidharth; Swatish, R. Srii; Joshi, Ayushma; Anand, Simar Kaur; Adefris, Addisalem; Kumar, Ramesh; Kaliappan, Jayakumar; Dhanalakshmi, S.

doi:10.1155/2023/9481790

Cited by 5 publications

(2 citation statements)

References 113 publications

(197 reference statements)

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“…The loss of dimensional correctness and structural integrity is the result of the quick heating and cooling processes. The implementation of quality controls for the process parameters of PBF has been enhanced by the utilization of artificial intelligence and machine-learning techniques, resulting in improved part quality [240,241].…”

Section: Quality Controlmentioning

confidence: 99%

Powder Bed Fusion 3D Printing in Precision Manufacturing for Biomedical Applications: A Comprehensive Review

Joshua,

Raj,

Hameed Sultan

et al. 2024

Materials

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Precision manufacturing requirements are the key to ensuring the quality and reliability of biomedical implants. The powder bed fusion (PBF) technique offers a promising solution, enabling the creation of complex, patient-specific implants with a high degree of precision. This technology is revolutionizing the biomedical industry, paving the way for a new era of personalized medicine. This review explores and details powder bed fusion 3D printing and its application in the biomedical field. It begins with an introduction to the powder bed fusion 3D-printing technology and its various classifications. Later, it analyzes the numerous fields in which powder bed fusion 3D printing has been successfully deployed where precision components are required, including the fabrication of personalized implants and scaffolds for tissue engineering. This review also discusses the potential advantages and limitations for using the powder bed fusion 3D-printing technology in terms of precision, customization, and cost effectiveness. In addition, it highlights the current challenges and prospects of the powder bed fusion 3D-printing technology. This work offers valuable insights for researchers engaged in the field, aiming to contribute to the advancement of the powder bed fusion 3D-printing technology in the context of precision manufacturing for biomedical applications.

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Section: Quality Controlmentioning

confidence: 99%

Powder Bed Fusion 3D Printing in Precision Manufacturing for Biomedical Applications: A Comprehensive Review

Joshua,

Raj,

Hameed Sultan

et al. 2024

Materials

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“…Prior research has addressed process optimization through machine learning [15,16,26,27] and the immediate process mapping method using in-process monitoring [28]. However, condition determination for data collection has not been completely automated.…”

Section: Process Map Constructed Through Selecting Boundary Condition...mentioning

confidence: 99%

Developing auto process mapping technique for powder bed fusion using an electron beam

Aoyagi,

Ono,

Yanagihara

et al. 2023

Prog Addit Manuf

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Powder bed fusion using an electron beam offers promise for manufacturing intricate metal parts. However, process optimization for defect-free parts proves costly and time-consuming. Many studies have investigated process optimization and defect prediction methods, but automating process optimization remains a significant challenge. This study developed and validated software to automatically determine i + 1-th trial conditions based on the results of the i-th trial experiment. Two algorithms were implemented and evaluated:—a dynamic programming approach and a selecting boundary conditions approach. The latter method considerably reduced the time required to determine the next conditions compared to the former approach. Considering a process mapping experiment requiring real-time trial condition determination during the build, we chose the selecting boundary conditions approach. The selecting boundary conditions approach was used to conduct a process mapping experiment to validate the software for constructing a process map using machine learning. The model and hyperparameters were optimized using sequential model-based global optimization with a tree-structured Parzen estimator. The process map underwent four updates using the developed software to determine i + 1-th trial conditions and construct a process map from the results of the i-th trial experiment.

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Influence of post-heat treatment on microstructure, texture, and mechanical properties of 18Ni-300 maraging steel fabricated by using LPBF technique

Karlapudy,

Nancharaiah,

Subba Rao

2023

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Powder Bed Fusion via Machine Learning-Enabled Approaches

Cited by 5 publications

References 113 publications

Powder Bed Fusion 3D Printing in Precision Manufacturing for Biomedical Applications: A Comprehensive Review

Powder Bed Fusion 3D Printing in Precision Manufacturing for Biomedical Applications: A Comprehensive Review

Developing auto process mapping technique for powder bed fusion using an electron beam

Influence of post-heat treatment on microstructure, texture, and mechanical properties of 18Ni-300 maraging steel fabricated by using LPBF technique

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