Intelligent control of additively manufactured beads on the basis of target morphologies and thermal field

Abstract

Hybrid deposition with microrolling is a promising arc-based direct energy deposition technique to rapidly build complex parts, whose performance is comparable to that of their wrought counterparts. Complex forming conditions and bead morphologies pose difficulties in controlling the morphologies from a single weld bead to built part profiles, and these difficulties hinder the widespread application of the technique. Here, a model that can automatically generate optimal process parameters on the basis of the infrared image, including thermal information and the point cloud information of the target weld bead, is developed. Results show that the errors in critical parameters, namely, feed speed, travel speed, and rolling force, are below 0.4%, 0.9%, and 2%, respectively, indicating that the proposed technique outperforms the compared methods. Furthermore, validation reveals that the actual depositing bead is similar (deviation below 0.05 mm) to the target bead. The proposed strategy provides an effective foundation for dynamic path planning and can considerably improve printing efficiency and precision.

Graphical abstract

Keywords

wire arc / additive manufacturing / deep learning / thermal field / point cloud / stainless steel

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