No abstract

No abstract

No abstract

The powder bed fusion additive manufacturing process has received widespread interest because of its capability to manufacture components with a complicated design and better surface finish compared to other additive techniques. Process optimization to obtain high quality parts is still a concern, which is impeding the full-scale production of materials. Therefore, it is of paramount importance to identify the best combination of process parameters that produces parts with the least defects and best features. This work focuses on gaining useful information about several features of the bead area, such as contact angle, porosity, voids, melt pool size and keyhole that were achieved using several combinations of laser power and scan speed to produce single scan lines. These features are identified and quantified using process learning, which is then used to conduct a comprehensive statistical analysis that allows to estimate the effect of the process parameters, such as laser power and scan speed on the output features. Both single and multi-response analyses are applied to analyze the response parameters, such as contact angle, porosity and melt pool size individually as well as in a collective manner. Laser power has been observed to have a more influential effect on all the features. A multi-response analysis showed that 150 W of laser power and 200 mm/s produced a bead with the best possible features.

Powder bed metal additive manufacturing process using laser or electron beam heat source is gaining increasing popularity due to its ability to create complex shaped metallic components. The process is a complex multi-physics process where multiple phases of material exist and laser interacts through multiple physical mechanisms with the surface of these materials and phases. The power absorption depends on optical and thermos-physical properties of the surface and laser type and wavelength. Most of the work conducted in the past have modeled the laser using a moving heat source. These studies typically assume a certain absorption without actual calculation of this power absorption. This study focuses on modeling the process in a more comprehensive manner including the laser physics and evaluating how this physics affects the temperature distribution and build outcome. The results are compared with the conventional techniques where simple Gaussian distribution was used for the power source. The temperature profile obtained with this study was lower than the Gaussian beam.