Influence of layer thickness on the printability of nickel alloy 718:A systematic process optimization framework

Shoukr, David; Morcos, Peter; Sundermann, Tayler; Dobrowolski, Thomas; Yates, C. I.; Jain, Jayesh R.; Arróyave, Raymundo; Караман, И.; Elwany, Alaa

doi:10.1016/j.addma.2023.103646

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…The experimental results obtained for each case study, as shown in Tables 6-8, are aligned with those obtained in the previous literature [36], thus confirming the robustness of the physical tests carried out and the methodology applied to measure the melt pool dimensions. Furthermore, from the evaluation of the melt pool morphology and analysis of the printability maps derived for each case study, it is possible to observe that the printability region becomes narrower as the layer thickness is increased, emphasizing the criticality of printing high-layer-thickness parts in the L-PBF process [33][34][35].…”

Section: Discussionmentioning

confidence: 97%

“…The layer thickness turns out to be one of the most important parameters to be increased to enhance the low productivity of this process [2,33] and to optimize the application of some printing strategies such as hull bulk [34]. Thus, the possibility to predict the melt pool formation and consequently the identification of the printability window (usually narrower as the thickness increases [35]) for high layer thickness parameters through an ML model results fundamentally in matching the major L-PBF needs. In addition, the effect of the preheating temperature is a very important parameter to be analyzed and investigated [36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

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A Supervised Machine Learning Model for Regression to Predict Melt Pool Formation and Morphology in Laser Powder Bed Fusion

Baldi,

Giorgetti,

Polidoro

et al. 2023

Applied Sciences

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In the additive manufacturing laser powder bed fusion (L-PBF) process, the optimization of the print process parameters and the development of conduction zones in the laser power (P) and scanning speed (V) parameter spaces are critical to meeting production quality, productivity, and volume goals. In this paper, we propose the use of a machine learning approach during the process parameter development to predict the melt pool dimensions as a function of the P/V combination. This approach turns out to be useful in speeding up the identification of the printability map of the material and defining the conduction zone during the development phase. Moreover, a machine learning method allows for an accurate investigation of the most promising configurations in the P-V space, facilitating the optimization and identification of the P-V set with the highest productivity. This approach is validated by an experimental campaign carried out on samples of Inconel 718, and the effects of some additional parameters, such as the layer thickness (in the range of 30 to 90 microns) and the preheating temperature of the building platform, are evaluated. More specifically, the experimental data have been used to train supervised machine learning models for regression using the KNIME Analytics Platform (version 4.7.7). An AutoML (node for regression) tool is used to identify the most appropriate model based on the evaluation of R2 and MAE scores. The gradient boosted tree model also performs best compared to Rosenthal’s analytical model.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

A Supervised Machine Learning Model for Regression to Predict Melt Pool Formation and Morphology in Laser Powder Bed Fusion

Baldi,

Giorgetti,

Polidoro

et al. 2023

Applied Sciences

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Exploring chemistry and additive manufacturing design spaces: a perspective on computationally-guided design of printable alloys

Sheikh,

Vela,

Attari

et al. 2024

Materials Research Letters

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Productivity improvement opportunities for metal powder bed fusion technologies: a systematic literature review

McConnell,

Tanner,

Kourousis

2024

RPJ

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Purpose Productivity is often cited as a key barrier to the adoption of metal laser-based powder bed fusion (ML-PBF) technology for mass production. Newer generations of this technology work to overcome this by introducing more lasers or dramatically different processing techniques. Current generation ML-PBF machines are typically not capable of taking on additional hardware to maximise productivity due to inherent design limitations. Thus, any increases to be found in this generation of machines need to be implemented through design or adjusting how the machine currently processes the material. The purpose of this paper is to identify the most beneficial existing methodologies for the optimisation of productivity in existing ML-PBF equipment so that current users have a framework upon which they can improve their processes. Design/methodology/approach The review method used here is the preferred reporting items for systematic review and meta-analysis (PRISMA). This is complemented by using an artificial intelligence-assisted literature review tool known as Elicit. Scopus, WEEE, Web of Science and Semantic Scholar databases were searched for articles using specific keywords and Boolean operators. Findings The PRIMSA and Elicit processes resulted in 51 papers that met the criteria. Of these, 24 indicated that by using a design of experiment approach, processing parameters could be created that would increase productivity. The other themes identified include scan strategy (11), surface alteration (11), changing of layer heights (17), artificial neural networks (3) and altering of the material (5). Due to the nature of the studies, quantifying the effect of these themes on productivity was not always possible. However, studies citing altering layer heights and processing parameters indicated the greatest quantifiable increase in productivity with values between 10% and 252% cited. The literature, though not always explicit, depicts several avenues for the improvement of productivity for current-generation ML-PBF machines. Originality/value This systematic literature review provides trends and themes that aim to influence and support future research directions for maximising the productivity of the ML-PBF machines.

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Influence of layer thickness on the printability of nickel alloy 718:A systematic process optimization framework

Cited by 4 publications

References 41 publications

A Supervised Machine Learning Model for Regression to Predict Melt Pool Formation and Morphology in Laser Powder Bed Fusion

A Supervised Machine Learning Model for Regression to Predict Melt Pool Formation and Morphology in Laser Powder Bed Fusion

Exploring chemistry and additive manufacturing design spaces: a perspective on computationally-guided design of printable alloys

Productivity improvement opportunities for metal powder bed fusion technologies: a systematic literature review

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