In-situ monitoring of Direct Energy Deposition via Structured Light System and its application in remanufacturing industry

Abstract

The Direct Energy Deposition (DED) process utilizes laser energy to melt metal powders and deposit them on the substrate layer to manufacture complex metal parts. This study was applied as a remanufacturing and repair process to fix used parts, which reduced unnecessary waste in the manufacturing industry. However, there could be defects generated during the repair, such as porosity or bumpy morphological defects. Traditionally, the operator would use a design of experiment (DOE) or simulation method to understand the printing parameters’ influence on the printed part. There are several influential factors: laser power, scanning speed, powder feeding rate, and standoff distance. Each DED machine has a different setup in practice, which results in some uncertainties for the printing results. For example, the nozzle diameter and laser type could be varied in different DED machines. Thus, it was hypothesized that a repair could be more effective if the printing process could be monitored in real-time. In this study, a structured light system (SLS) was used to capture the printing process’s layerwise information. The SLS system is capable of performing 3D surface scanning with a high-resolution of 10 µm. To determine how much material needs to be deposited, given the initial scanning of the part and allowing the real-time observation of each layer’s information. Once a defect was found in-situ, the DED machine (hybrid machine) would change the tool and remove the flawed layer. After the repair, the non-destructive approach computed tomography (CT) was applied to examine its interior features. In this research, a DED machine using 316L stainless steel was used to perform the repairing process to demonstrate its effectiveness. The lab-built SLS system was used to capture each layer’s information, and CT data was provided for the quality evaluation. The novel manufacturing approach could improve the DED repair quality, reduce the repair time, and promote repair automation. In the future, it has a great potential to be used in the manufacturing industry to repair used parts and avoid the extra cost involved in buying a new part.