Trajectory Optimization for Spatial Microstructure Control in Electron Beam Metal Additive Manufacturing

TL;DR

This paper introduces a trajectory optimization method using augmented Lagrangian differential dynamic programming to control microstructure and hardness in electron beam additive manufacturing, demonstrating precise spatial microstructure regulation.

Contribution

It develops a novel optimal control framework for spatial microstructure control in metal AM, integrating thermal and microstructural models with experimental data.

Findings

Optimized power trajectories achieve targeted hardness distributions.

GPU-accelerated AL-DDP efficiently computes control inputs.

Experimental results confirm precise microstructure control.

Abstract

Metal additive manufacturing (AM) opens the possibility for spatial control of as-fabricated microstructure and properties. However, since the solid state diffusional transformations that drive microstructure outcomes are governed by nonlinear ODEs in terms of temperature, which is itself governed by PDEs over the entire part domain, solving for the system inputs needed to achieve desired microstructure distributions has proven difficult. In this work, we present a trajectory optimization approach for spatial control of microstructure in metal AM, which we demonstrate by controlling the hardness of a low-alloy steel in electron beam powder bed fusion (EB-PBF). To this end, we present models for thermal and microstructural dynamics. Next, we use experimental data to identify the parameters of the microstructure transformation dynamics. We then pose spatial microstructure control as a finite-horizon optimal control problem. The optimal power field trajectory is computed using an augmented Lagrangian differential dynamic programming (AL-DDP) method with GPU acceleration. The resulting time-varying power fields are then realized on an EB-PBF machine through an approximation scheme. Measurements of the resultant hardness shows that the optimized power field trajectory is able to closely produce the desired hardness distribution.