Parand Akbari
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View article: Leveraging Machine Learning and Clinical Data to Predict Response to Intralesional Corticosteroids in Keloid Patients
Leveraging Machine Learning and Clinical Data to Predict Response to Intralesional Corticosteroids in Keloid Patients Open
Background Intralesional corticosteroid injections (ILCS) are a common treatment for keloid lesions; however, many patients exhibit resistance, and some experience worsening of their keloids following treatment. Objective To develop a mach…
View article: Machine learning predictions of spatter behavior in LPBF additive manufacturing
Machine learning predictions of spatter behavior in LPBF additive manufacturing Open
View article: Machine learning prediction of mechanical properties in metal additive manufacturing
Machine learning prediction of mechanical properties in metal additive manufacturing Open
Predicting mechanical properties in metal additive manufacturing (MAM) is essential for ensuring the performance and reliability of printed parts, as well as their suitability for specific applications. However, conducting experiments to e…
View article: Mechpronet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing
Mechpronet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing Open
View article: Surrogate modeling of melt pool temperature field using deep learning
Surrogate modeling of melt pool temperature field using deep learning Open
Powder-based additive manufacturing has transformed the manufacturing industry over the last decade. In the Laser Powder Bed Fusion (L-PBF) process, a specific part is built in an iterative manner in which two-dimensional cross-sections ar…
View article: MechProNet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing
MechProNet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing Open
Predicting mechanical properties in metal additive manufacturing (MAM) is essential for ensuring the performance and reliability of printed parts, as well as their suitability for specific applications. However, conducting experiments to e…
View article: Surrogate Modeling of Melt Pool Thermal Field using Deep Learning
Surrogate Modeling of Melt Pool Thermal Field using Deep Learning Open
Powder-based additive manufacturing has transformed the manufacturing industry over the last decade. In Laser Powder Bed Fusion, a specific part is built in an iterative manner in which two-dimensional cross-sections are formed on top of e…
View article: MeltpoolNet: Melt pool characteristic prediction in Metal Additive Manufacturing using machine learning
MeltpoolNet: Melt pool characteristic prediction in Metal Additive Manufacturing using machine learning Open
Characterizing melt pool shape and geometry is essential in Metal Additive Manufacturing (MAM) to control the printing process, and avoid defects. Predicting melt pool flaws based on process parameters and powder material is difficult due …
View article: MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning
MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning Open
Characterizing meltpool shape and geometry is essential in metal Additive Manufacturing (MAM) to control the printing process and avoid defects. Predicting meltpool flaws based on process parameters and powder material is difficult due to …
View article: Surrogate Modeling of Melt Pool Thermal Field Using Deep Learning
Surrogate Modeling of Melt Pool Thermal Field Using Deep Learning Open