Melt pool sensing and size analysis in laser powder-bed metal additive manufacturing

2018 ◽  
Vol 32 ◽  
pp. 744-753 ◽  
Author(s):  
Bo Cheng ◽  
James Lydon ◽  
Kenneth Cooper ◽  
Vernon Cole ◽  
Paul Northrop ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Size Analysis ◽  
Powder Bed ◽  
Metal Additive Manufacturing ◽  
Melt Pool ◽  
Metal Additive

Progress towards a Complete Model of Metal Additive Manufacturing

2021 ◽  
Vol 1016 ◽  
pp. 1031-1038
Author(s):  
Vu Thua Nguyen ◽  
Anthony B. Murphy ◽  
Gary W. Delaney ◽  
Sharen J. Cummins ◽  
Paul W. Cleary ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Computational Techniques ◽  
Powder Bed Fusion ◽  
Complete Model ◽  
Physical Processes ◽  
Powder Bed ◽  
Metal Additive Manufacturing ◽  
Melt Pool ◽  
Process Understanding ◽  
Metal Additive

Metal additive manufacturing based on powder bed fusion processes is increasingly important. However, highly transient physical phenomena that occur in these processes at different length scales are difficult to observe. Challenging and costly experiments are usually needed to obtain data for process understanding and improvement. Computational modelling of powder-bed fusion processes is therefore important from several points of view. These include better process understanding, optimisation of process parameters and component designs, prediction of component properties, qualification of components and to assist process control. Several physical processes have to be treated to develop a complete model, namely the raking of the powder bed surface, the transfer of energy from the laser or electron beam to the metal, the melting and solidification of the powder, the flow of liquid metal in the melt pool, the heat transfer from the melt pool to the surrounding powder and solid metal, the evolution of the microstructure, and the residual stress and deformation of the component. These processes occur at very different scales, and have to be treated using several different computational techniques. In addition, the interdependency of some of the processes has to be accounted for. This paper discusses the rationale for developing a complete model, progress in developing sub-models of the different physical processes, and the framework that is envisaged to combine the sub-models into a predictive model of the additive manufacturing process.

A deep neural network for classification of melt-pool images in metal additive manufacturing

2018 ◽  
Vol 31 (2) ◽  
pp. 375-386 ◽  
Author(s):  
Ohyung Kwon ◽  
Hyung Giun Kim ◽  
Min Ji Ham ◽  
Wonrae Kim ◽  
Gun-Hee Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Deep Neural Network ◽  
Metal Additive Manufacturing ◽  
Melt Pool ◽  
Metal Additive

Measurement of surface morphology of powder bed for metal additive manufacturing

2020 ◽  
Vol 2020 (0) ◽  
pp. S04110
Author(s):  
Kenya YUASA ◽  
Masaharu TAGAMI ◽  
Makiko YONEHARA ◽  
Toshi-Taka IKESHOJI ◽  
Koki TAKESHITA ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Surface Morphology ◽  
Powder Bed ◽  
Metal Additive Manufacturing ◽  
Metal Additive

Effect of Time Spacing and Hatch Spacing on Thermal Material Properties and Melt Pool Geometry in Additive Manufacturing of S316L

2019 ◽  
Author(s):  
Elham Mirkoohi ◽  
Daniel E. Sievers ◽  
Steven Y. Liang
Keyword(s):  
Heat Transfer ◽  
Additive Manufacturing ◽  
Material Properties ◽  
Metal Additive Manufacturing ◽  
Melt Pool ◽  
Experimental Values ◽  
Melt Pool Size ◽  
Transfer Mechanisms ◽  
Hatch Spacing ◽  
Metal Additive

Abstract A physics-based analytical solution is proposed in order to investigate the effect of hatch spacing and time spacing (which is the time delay between two consecutive irradiations) on thermal material properties and melt pool geometry in metal additive manufacturing processes. A three-dimensional moving point heat source approach is used in order to predict the thermal behavior of the material in additive manufacturing process. The thermal material properties are considered to be temperature dependent since the existence of the steep temperature gradient has a substantial influence on the magnitude of the thermal conductivity and specific heat, and as a result, it has an influence on the heat transfer mechanisms. Moreover, the melting/solidification phase change is considered using the modified heat capacity since it has an influence on melt pool geometry. The proposed analytical model also considers the multi-layer aspect of metal additive manufacturing since the thermal interaction of the successive layers has an influence on heat transfer mechanisms. Temperature modeling in metal additive manufacturing is one of the most important predictions since the presence of the temperature gradient inside the build part affect the melt pool size and geometry, thermal stress, residual stress, and part distortion. In this paper, the effect of time spacing and hatch spacing on thermal material properties and melt pool geometry is investigated. Both factors are found statistically significant with regard to their influence on thermal material properties and melt pool geometry. The predicted melt pool size is compared to experimental values from independent reports. Good agreement is achieved between the proposed physics-based analytical model and experimental values.

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