A greedy algorithm for optimal heating in powder-bed-based additive manufacturing

Robert Forslund,Anders Snis,Stig Larsson

doi:10.1186/s13362-021-00110-x

Robert Forslund, Anders Snis + Show 1 more

Open Access

https://doi.org/10.1186/s13362-021-00110-x

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Abstract

Powder-bed-based additive manufacturing involves melting of a powder bed using a moving laser or electron beam as a heat source. In this paper, we formulate an optimization scheme that aims to control this type of melting. The goal consists of tracking maximum temperatures on lines that run along the beam path. Time-dependent beam parameters (more specifically, beam power, spot size, and speed) act as control functions. The scheme is greedy in the sense that it exploits local properties of the melt pool in order to divide a large optimization problem into several small ones. As illustrated by numerical examples, the scheme can resolve heat conduction issues such as concentrated heat accumulation at turning points and non-uniform melt depths.

Highlights

1 Introduction Powder bed fusion (PBF) is a type of additive manufacturing (AM) where metal powder is melted by a laser or electron beam in a layer-wise fashion to enable the production of geometrically complex parts [1]
The scheme is efficient because it exploits that the melt pool is local to the beam and shows good capabilities despite several simplifications
The scheme should be useful for studying problematic areas of the melting process where particular care needs to be put into the choice of beam parameters

Summary

Introduction

Powder bed fusion (PBF) is a type of additive manufacturing (AM) where metal powder is melted by a laser or electron beam in a layer-wise fashion to enable the production of geometrically complex parts [1]. DoE suggests that the beam parameters have the largest impact on the temperature distribution during melting and, on the quality of the completed part For this reason, we choose the beam power, spot size and speed as control variables. With our purely thermal model, the powder-solid interface of this volume is dependent on the maximum temperature and would be easier to explicitly define than the melt pool ω(t). The steps taken above allow us to formulate a simple optimization problem that is efficient in the sense that we, instead of tracking some desired transient melt pool ω(t) in a volume, only track two scalar values umelt and usurf on paths. The choice of weights should represent the relative importance of the objectives; important objectives are weighted more heavily

A first greedy algorithm for solving the scalarized optimization problem

Conclusions