The Experts below are selected from a list of 46836 Experts worldwide ranked by ideXlab platform
Magda Sadowski - One of the best experts on this subject based on the ideXlab platform.
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Temperature distribution and melt geometry in laser and electron-beam melting processes - A comparison among common materials
Additive Manufacturing, 2015Co-Authors: John Romano, Jafar Razmi, Leila Ladani, Magda SadowskiAbstract:Due to the relative youth of metallic powder bed additive manufacturing technologies and difficulties with monitoring the process in situ, there is little consensus in the user community on how to optimize user variable parameters to ensure the highest quality and most cost effective Build. Temperature distribution is the critical factor that dictates melting, microstructure and eventually the final part quality. Monitoring or measuring the Temperature during the process is extremely difficult due to the ultra-high speeds and microscale size of the laser or electron beam. Therefore, other tools such as finite element modeling can be utilized to optimize these processes and predict the behavior of the system for different materials. This research presents transient, dynamic finite element model of the Build process for both laser and electron beam melting techniques. The model includes melting and solidification of the powder as well as different thermal aspects such as conduction and radiation. Diffusivity of the powder is modeled and phase change is modeled such that latent heat of fusion is considered. Melt pool geometry and Temperature distribution was obtained for different heat sources and different materials such as Ti6Al4V, Stainless Steel 316, and 7075 Aluminum powders. It was determined that heat accumulation is most consolidated within titanium powder beds, with steel being the second most consolidated, and aluminum powder beds having the most heat dissipation. As a result, titanium was seen to exhibit the highest local Temperatures and largest melt pools, followed by steel and aluminum in decreasing order. Naturally, laser models showed smaller melt pool sizes and depths due to lower power. The beam speed and power used for Ti were found inadequate for creating a sustained and continuous melting of Al and Steel. Therefore, adjustments were made to these parameters and presented in this research.
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Temperature distribution and melt geometry in laser and electron-beam melting processes – A comparison among common materials
Additive Manufacturing, 2015Co-Authors: John Romano, Jafar Razmi, Leila Ladani, Magda SadowskiAbstract:Abstract Due to the relative youth of metallic powder bed additive manufacturing technologies and difficulties with monitoring the process in situ, there is little consensus in the user community on how to optimize user variable parameters to ensure the highest quality and most cost effective Build. Temperature distribution is the critical factor that dictates melting, microstructure and eventually the final part quality. Monitoring or measuring the Temperature during the process is extremely difficult due to the ultra-high speeds and microscale size of the laser or electron beam. Therefore, other tools such as finite element modeling can be utilized to optimize these processes and predict the behavior of the system for different materials. This research presents transient, dynamic finite element model of the Build process for both laser and electron beam melting techniques. The model includes melting and solidification of the powder as well as different thermal aspects such as conduction and radiation. Diffusivity of the powder is modeled and phase change is modeled such that latent heat of fusion is considered. Melt pool geometry and Temperature distribution was obtained for different heat sources and different materials such as Ti6Al4V, Stainless Steel 316, and 7075 Aluminum powders. It was determined that heat accumulation is most consolidated within titanium powder beds, with steel being the second most consolidated, and aluminum powder beds having the most heat dissipation. As a result, titanium was seen to exhibit the highest local Temperatures and largest melt pools, followed by steel and aluminum in decreasing order. Naturally, laser models showed smaller melt pool sizes and depths due to lower power. The beam speed and power used for Ti were found inadequate for creating a sustained and continuous melting of Al and Steel. Therefore, adjustments were made to these parameters and presented in this research.
John Romano - One of the best experts on this subject based on the ideXlab platform.
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Temperature distribution and melt geometry in laser and electron-beam melting processes - A comparison among common materials
Additive Manufacturing, 2015Co-Authors: John Romano, Jafar Razmi, Leila Ladani, Magda SadowskiAbstract:Due to the relative youth of metallic powder bed additive manufacturing technologies and difficulties with monitoring the process in situ, there is little consensus in the user community on how to optimize user variable parameters to ensure the highest quality and most cost effective Build. Temperature distribution is the critical factor that dictates melting, microstructure and eventually the final part quality. Monitoring or measuring the Temperature during the process is extremely difficult due to the ultra-high speeds and microscale size of the laser or electron beam. Therefore, other tools such as finite element modeling can be utilized to optimize these processes and predict the behavior of the system for different materials. This research presents transient, dynamic finite element model of the Build process for both laser and electron beam melting techniques. The model includes melting and solidification of the powder as well as different thermal aspects such as conduction and radiation. Diffusivity of the powder is modeled and phase change is modeled such that latent heat of fusion is considered. Melt pool geometry and Temperature distribution was obtained for different heat sources and different materials such as Ti6Al4V, Stainless Steel 316, and 7075 Aluminum powders. It was determined that heat accumulation is most consolidated within titanium powder beds, with steel being the second most consolidated, and aluminum powder beds having the most heat dissipation. As a result, titanium was seen to exhibit the highest local Temperatures and largest melt pools, followed by steel and aluminum in decreasing order. Naturally, laser models showed smaller melt pool sizes and depths due to lower power. The beam speed and power used for Ti were found inadequate for creating a sustained and continuous melting of Al and Steel. Therefore, adjustments were made to these parameters and presented in this research.
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Temperature distribution and melt geometry in laser and electron-beam melting processes – A comparison among common materials
Additive Manufacturing, 2015Co-Authors: John Romano, Jafar Razmi, Leila Ladani, Magda SadowskiAbstract:Abstract Due to the relative youth of metallic powder bed additive manufacturing technologies and difficulties with monitoring the process in situ, there is little consensus in the user community on how to optimize user variable parameters to ensure the highest quality and most cost effective Build. Temperature distribution is the critical factor that dictates melting, microstructure and eventually the final part quality. Monitoring or measuring the Temperature during the process is extremely difficult due to the ultra-high speeds and microscale size of the laser or electron beam. Therefore, other tools such as finite element modeling can be utilized to optimize these processes and predict the behavior of the system for different materials. This research presents transient, dynamic finite element model of the Build process for both laser and electron beam melting techniques. The model includes melting and solidification of the powder as well as different thermal aspects such as conduction and radiation. Diffusivity of the powder is modeled and phase change is modeled such that latent heat of fusion is considered. Melt pool geometry and Temperature distribution was obtained for different heat sources and different materials such as Ti6Al4V, Stainless Steel 316, and 7075 Aluminum powders. It was determined that heat accumulation is most consolidated within titanium powder beds, with steel being the second most consolidated, and aluminum powder beds having the most heat dissipation. As a result, titanium was seen to exhibit the highest local Temperatures and largest melt pools, followed by steel and aluminum in decreasing order. Naturally, laser models showed smaller melt pool sizes and depths due to lower power. The beam speed and power used for Ti were found inadequate for creating a sustained and continuous melting of Al and Steel. Therefore, adjustments were made to these parameters and presented in this research.
Filipe Natalio - One of the best experts on this subject based on the ideXlab platform.
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Estimating Temperatures of heated Lower Palaeolithic flint artefacts.
Nature human behaviour, 2020Co-Authors: Aviad Agam, Ido Azuri, Iddo Pinkas, Avi Gopher, Filipe NatalioAbstract:Production of stone artefacts using pyro-technology is known from the Middle and Upper Palaeolithic of Europe and the Levant, and the Middle Stone Age in Africa. However, determination of Temperatures to which flint artefacts were exposed is impeded by the chemical and structural variability of flint. Here we combine Raman spectroscopy and machine learning to Build Temperature-estimation models to infer the degree of pyro-technological control effected by inhabitants of the late Lower Palaeolithic (Acheulo-Yabrudian) site of Qesem Cave, Israel. Temperature estimation shows that blades were heated at lower median Temperatures (259 °C) compared to flakes (413 °C), whereas heat-induced structural flint damage (for example, pot-lids and microcracks) appears at 447 °C. These results are consistent with a differential behaviour for selective tool production that can be viewed as part of a plethora of innovative and adaptive behaviours of Levantine hominins >300,000 years ago.
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Estimating Temperatures of heated Lower Palaeolithic flint artefacts
Nature Human Behaviour, 2020Co-Authors: Aviad Agam, Ido Azuri, Iddo Pinkas, Avi Gopher, Filipe NatalioAbstract:Controlled used of fire is one of the most outstanding achievements attributed to humankind. Artificial intelligence estimates the heating Temperatures of flint tools fabricated by hominins over 300,000 years ago at Qesem Cave, providing insightful views into both advanced behaviours and the cognitive evolution of our species. Production of stone artefacts using pyro-technology is known from the Middle and Upper Palaeolithic of Europe and the Levant, and the Middle Stone Age in Africa. However, determination of Temperatures to which flint artefacts were exposed is impeded by the chemical and structural variability of flint. Here we combine Raman spectroscopy and machine learning to Build Temperature-estimation models to infer the degree of pyro-technological control effected by inhabitants of the late Lower Palaeolithic (Acheulo-Yabrudian) site of Qesem Cave, Israel. Temperature estimation shows that blades were heated at lower median Temperatures (259 °C) compared to flakes (413 °C), whereas heat-induced structural flint damage (for example, pot-lids and microcracks) appears at 447 °C. These results are consistent with a differential behaviour for selective tool production that can be viewed as part of a plethora of innovative and adaptive behaviours of Levantine hominins >300,000 years ago.
Jafar Razmi - One of the best experts on this subject based on the ideXlab platform.
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Temperature distribution and melt geometry in laser and electron-beam melting processes - A comparison among common materials
Additive Manufacturing, 2015Co-Authors: John Romano, Jafar Razmi, Leila Ladani, Magda SadowskiAbstract:Due to the relative youth of metallic powder bed additive manufacturing technologies and difficulties with monitoring the process in situ, there is little consensus in the user community on how to optimize user variable parameters to ensure the highest quality and most cost effective Build. Temperature distribution is the critical factor that dictates melting, microstructure and eventually the final part quality. Monitoring or measuring the Temperature during the process is extremely difficult due to the ultra-high speeds and microscale size of the laser or electron beam. Therefore, other tools such as finite element modeling can be utilized to optimize these processes and predict the behavior of the system for different materials. This research presents transient, dynamic finite element model of the Build process for both laser and electron beam melting techniques. The model includes melting and solidification of the powder as well as different thermal aspects such as conduction and radiation. Diffusivity of the powder is modeled and phase change is modeled such that latent heat of fusion is considered. Melt pool geometry and Temperature distribution was obtained for different heat sources and different materials such as Ti6Al4V, Stainless Steel 316, and 7075 Aluminum powders. It was determined that heat accumulation is most consolidated within titanium powder beds, with steel being the second most consolidated, and aluminum powder beds having the most heat dissipation. As a result, titanium was seen to exhibit the highest local Temperatures and largest melt pools, followed by steel and aluminum in decreasing order. Naturally, laser models showed smaller melt pool sizes and depths due to lower power. The beam speed and power used for Ti were found inadequate for creating a sustained and continuous melting of Al and Steel. Therefore, adjustments were made to these parameters and presented in this research.
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Temperature distribution and melt geometry in laser and electron-beam melting processes – A comparison among common materials
Additive Manufacturing, 2015Co-Authors: John Romano, Jafar Razmi, Leila Ladani, Magda SadowskiAbstract:Abstract Due to the relative youth of metallic powder bed additive manufacturing technologies and difficulties with monitoring the process in situ, there is little consensus in the user community on how to optimize user variable parameters to ensure the highest quality and most cost effective Build. Temperature distribution is the critical factor that dictates melting, microstructure and eventually the final part quality. Monitoring or measuring the Temperature during the process is extremely difficult due to the ultra-high speeds and microscale size of the laser or electron beam. Therefore, other tools such as finite element modeling can be utilized to optimize these processes and predict the behavior of the system for different materials. This research presents transient, dynamic finite element model of the Build process for both laser and electron beam melting techniques. The model includes melting and solidification of the powder as well as different thermal aspects such as conduction and radiation. Diffusivity of the powder is modeled and phase change is modeled such that latent heat of fusion is considered. Melt pool geometry and Temperature distribution was obtained for different heat sources and different materials such as Ti6Al4V, Stainless Steel 316, and 7075 Aluminum powders. It was determined that heat accumulation is most consolidated within titanium powder beds, with steel being the second most consolidated, and aluminum powder beds having the most heat dissipation. As a result, titanium was seen to exhibit the highest local Temperatures and largest melt pools, followed by steel and aluminum in decreasing order. Naturally, laser models showed smaller melt pool sizes and depths due to lower power. The beam speed and power used for Ti were found inadequate for creating a sustained and continuous melting of Al and Steel. Therefore, adjustments were made to these parameters and presented in this research.
Leila Ladani - One of the best experts on this subject based on the ideXlab platform.
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Temperature distribution and melt geometry in laser and electron-beam melting processes - A comparison among common materials
Additive Manufacturing, 2015Co-Authors: John Romano, Jafar Razmi, Leila Ladani, Magda SadowskiAbstract:Due to the relative youth of metallic powder bed additive manufacturing technologies and difficulties with monitoring the process in situ, there is little consensus in the user community on how to optimize user variable parameters to ensure the highest quality and most cost effective Build. Temperature distribution is the critical factor that dictates melting, microstructure and eventually the final part quality. Monitoring or measuring the Temperature during the process is extremely difficult due to the ultra-high speeds and microscale size of the laser or electron beam. Therefore, other tools such as finite element modeling can be utilized to optimize these processes and predict the behavior of the system for different materials. This research presents transient, dynamic finite element model of the Build process for both laser and electron beam melting techniques. The model includes melting and solidification of the powder as well as different thermal aspects such as conduction and radiation. Diffusivity of the powder is modeled and phase change is modeled such that latent heat of fusion is considered. Melt pool geometry and Temperature distribution was obtained for different heat sources and different materials such as Ti6Al4V, Stainless Steel 316, and 7075 Aluminum powders. It was determined that heat accumulation is most consolidated within titanium powder beds, with steel being the second most consolidated, and aluminum powder beds having the most heat dissipation. As a result, titanium was seen to exhibit the highest local Temperatures and largest melt pools, followed by steel and aluminum in decreasing order. Naturally, laser models showed smaller melt pool sizes and depths due to lower power. The beam speed and power used for Ti were found inadequate for creating a sustained and continuous melting of Al and Steel. Therefore, adjustments were made to these parameters and presented in this research.
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Temperature distribution and melt geometry in laser and electron-beam melting processes – A comparison among common materials
Additive Manufacturing, 2015Co-Authors: John Romano, Jafar Razmi, Leila Ladani, Magda SadowskiAbstract:Abstract Due to the relative youth of metallic powder bed additive manufacturing technologies and difficulties with monitoring the process in situ, there is little consensus in the user community on how to optimize user variable parameters to ensure the highest quality and most cost effective Build. Temperature distribution is the critical factor that dictates melting, microstructure and eventually the final part quality. Monitoring or measuring the Temperature during the process is extremely difficult due to the ultra-high speeds and microscale size of the laser or electron beam. Therefore, other tools such as finite element modeling can be utilized to optimize these processes and predict the behavior of the system for different materials. This research presents transient, dynamic finite element model of the Build process for both laser and electron beam melting techniques. The model includes melting and solidification of the powder as well as different thermal aspects such as conduction and radiation. Diffusivity of the powder is modeled and phase change is modeled such that latent heat of fusion is considered. Melt pool geometry and Temperature distribution was obtained for different heat sources and different materials such as Ti6Al4V, Stainless Steel 316, and 7075 Aluminum powders. It was determined that heat accumulation is most consolidated within titanium powder beds, with steel being the second most consolidated, and aluminum powder beds having the most heat dissipation. As a result, titanium was seen to exhibit the highest local Temperatures and largest melt pools, followed by steel and aluminum in decreasing order. Naturally, laser models showed smaller melt pool sizes and depths due to lower power. The beam speed and power used for Ti were found inadequate for creating a sustained and continuous melting of Al and Steel. Therefore, adjustments were made to these parameters and presented in this research.
