ADDOPT: An Additive Manufacturing Optimal Control Framework Demonstrated in Minimizing Layer-Level Thermal Variance in Electron Beam Powder Bed Fusion

TL;DR

This paper introduces ADDOPT, a novel optimal control framework for additive manufacturing that minimizes thermal variance during the process, leading to improved quality and reduced need for iterative experimentation.

Contribution

It presents a general state-space modeling approach and formulates AM process planning as an optimal control problem, demonstrating globally optimal dynamic process plans for electron beam powder bed fusion.

Findings

Achieved up to 87% reduction in thermal variance in simulations.

Validated process plans experimentally with significant variance reduction.

Expanded the use of optimal control theory in AM process optimization.

Abstract

Additive manufacturing (AM) techniques hold promise but face significant challenges in process planning and optimization. The large temporal and spatial variations in temperature that can occur in layer-wise AM lead to thermal excursions, resulting in property variations and defects. These variations cannot always be fully mitigated by simple static parameter search. To address this challenge, we propose a general approach based on modeling AM processes on the part-scale in state-space and framing AM process planning as a numerical optimal control problem. We demonstrate this approach on the problem of minimizing thermal variation in a given layer in the electron beam powder bed fusion (EB-PBF) AM process, and are able to compute globally optimal dynamic process plans. These optimized process plans are then evaluated in simulation, achieving an 87% and 86% reduction in cumulative variance compared to random spot melting and a uniform power field respectively, and are further validated in experiment. This one-shot feedforward planning approach expands the capabilities of AM technology by minimizing the need for experimentation and iteration to achieve process optimization. Further, this work opens the possibility for the application of optimal control theory to part-scale optimization and control in AM.