Automation of Property Acquisition of Single Track Depositions Manufactured through Direct Energy Deposition

Maria Beatriz Silva,Jorge Gil,Abílio De Jesus,João Manuel R S Tavares,Ana Reis,Maria F Vaz

doi:10.3390/app12052755

Abstract

Metallic additive manufacturing processes have been significantly developed since their inception with modern systems capable of manufacturing components for structural applications. However, successful processing through these methods requires extensive experimentation before optimised parameters can be found. In laser-based processes, such as direct energy deposition, it is common for single track beads to be deposited and subjected to analysis, yielding information on how the input parameters influence characteristics such as the output’s adhesion to the substrate. These characteristics are often determined using specialised software, from images obtained by cross-section cutting the line beads. The proposed approach was based on a Python algorithm, using the scikit-image library and optical microscopy imaging from produced 18Ni300 Maraging steel on H13 tool steel, and it computes the relevant properties of DED-produced line beads, such as the track height, width, penetration, wettability angles, cross-section areas above and below the substrate and dilution proportion. 18Ni300 Maraging steel depositions were optimised with a laser power of 1550 W, feeding rate of 12 gmin−1, scanning speed of 12 mm s−1, shielding gas flow rate of 25 Lmin−1 and carrier gas flow rate of 4 Lmin−1 for a laser spot diameter of 2.1mm. Out of the cross-sectioned beads, their respective height, width and penetration were calculated with 2.71%, 4.01% and 9.35% errors; the dilution proportion was computed with 14.15% error, the area above the substrate with 5.27% error and the area below the substrate with 17.93% error. The average computational time for the processing of one image was 12.7s. The developed approach was purely segmentational and could potentially benefit from machine-learning implementations.

Highlights

Metallic additive manufacturing (MAM) is a family of technological processes where metallic components are manufactured by the successive deposition of cross-sections of the final part until it is built [1]
This classification encompasses a considerable variety of processes, varying in complexity, productivity and cost; the present study focuses on direct energy deposition (DED), a process where the feedstock, which may be powdered (DED) or a wire metallic compound, is melted by a heat source as it is being inserted into the melting pool [2]
DED technologies may be compared to selective laser melting (SLM), another wellestablished MAM process, which produces parts through the successive melting of evenly spread metallic powder layers: at each cross-section, a thin coat before the heat source melts the powder as it is diverted through a system of mirrors to follow the cross-section of the built part

Summary

Introduction

Metallic additive manufacturing (MAM) is a family of technological processes where metallic components are manufactured by the successive deposition of cross-sections (or layers) of the final part until it is built [1]. This classification encompasses a considerable variety of processes, varying in complexity, productivity and cost; the present study focuses on direct energy deposition (DED), a process where the feedstock, which may be powdered (DED) or a wire (wDED) metallic compound, is melted by a heat source as it is being inserted into the melting pool [2]. While DED features increased productivity at the cost of decreased part accuracy and surface finishing [4], often being named a near-net shape process [5], SLM is capable of producing more complex geometries, as it has better dimensional fidelity and is capable of producing support structures [6]

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Conclusion