Concurrent geometry- and material-based process identification and optimization for robotic CMT-based wire arc additive manufacturing

Abstract

Additive Manufacturing (AM) is a novel manufacturing method where a component is fabricated by accumulating material layer-by-layer therefore facilitating customized near-net-shape components while maximizing design freedom. A metal AM technology suited for large-scale component fabrication that has been emerging in recent years and is commonly referred to as Wire and Arc AM (WAAM) uses Gas Metal Arc Welding (GMAW) technology to deposit material and large-scale robotic serial manipulator systems (reach >1 m cubed). One of the requirements for applying GMAW welding technology to AM is to identify and optimize deposition parameters in order to achieve a desired deposition quality measured in terms of geometrical, mechanical and metallurgical consistency. In this work, deposition process parameters qualitatively and quantitatively influencing the geometrical, mechanical and metallurgical consistency are identified and statistically validated. A deposition parameter combination is found that optimizes the quality of single-track wall benchmark components made from low-carbon steel. Moreover, correlations are found between accumulated heat during fabrication and the geometrical variations of the benchmark components due to bead slumping. Additionally, correlations between microstructure variations and geometrical variations are found. Finally, based on the presented analyses, in-situ temperature monitoring methods are proposed in order to achieve optimal component quality.

Keywords

• Process identification
• Process response optimization
• WAAM
• CMT
• Robotic additive manufacturing