Laser Powder Bed Fusion (LPBF) Process
The LPBF parameter space consists of laser power, scan speed, laser spot size, scanning strategy, feedstock, part geometry, and machine conditions. The selection of process parameters determines the resulting microstructure and component properties.
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Smart process mapping of powder bed fusion additively manufactured metallic wicks using surrogate modeling
Published: 06 March 2024
(2024)
Journal of Intelligent Manufacturing
Mohammad Borumand, Saideep Nannapaneni, Gurucharan Madiraddy, Michael P. Sealy, Sima Esfandiarpour Borujeni & Gisuk Hwang
Abstract
Powder bed fusion is an innovative additive manufacturing (AM) technique to achieve metallic wick structures for efficient two-phase thermal management systems. However, a technical challenge lies in the lack of standard process maps as it currently relies on an expensive trial and error approach. In this study, five types of surrogate models for classification analysis (i.e., naïve Bayes, logistic regression, random forest, support vector machine, and Gaussian process classification) were constructed and compared to efficiently unlock the relations between five process parameters (i.e., laser power, scan speed, hatch spacing, spot diameter, and effective laser energy) and wick manufacturability. The models were trained using data from a total of 187 AM wick manufacturability experiments. Using four process parameter (PP) model (five PP model without effective laser energy), the Gaussian process classification (GPC) showed the maximum median prediction accuracy (PA) of 93%, while it further improved to 99.7% using support vector machine (SVM) and five process parameter model. Also, the median PAs of the SVM and GPC remains above 98.5% with only 60% of the total experimental data using five PP model. The sensitivity analysis showed that the hatch spacing was the most sensitive parameter for the wick manufacturability using four PP model, while the effective laser energy is the most sensitive one using five PP model. This study provides insights into the smart selection of optimal process parameters for the desired metallic AM wicks.
Unlock the relations between five process parameters (i.e., laser power, scan speed, hatch spacing, spot diameter, and effective laser energy) and wick manufacturability.
https://link.springer.com/article/10.1007/s10845-024-02330-5
Effects of process parameters on the surface characteristics of laser powder bed fusion printed parts: machine learning predictions with random forest and support vector regression
Naol Dessalegn Dejene, Hirpa G. Lemu & Endalkachew Mosisa Gutema
Open access
The International Journal of Advanced Manufacturing Technology
Volume 133, pages 5611–5625, (2024)
You have full access to this open access article
Abstract
Laser powder bed fusion (L-PBF) fuses metallic powder using a high-energy laser beam, forming parts layer by layer. This technique offers flexibility and design freedom in metal additive manufacturing (MAM). However, achieving the desired surface quality remains challenging and impacts functionality and reliability. L-PBF process parameters significantly influence surface roughness. Identifying the most critical factors among numerous parameters is essential for improving quality. This study examines the effects of key process parameters on the surface roughness of AlSi10Mg, a widely used aluminum alloy in high-tech industries, fabricated by L-PBF. Part orientation, laser power, scanning speed, and layer thickness were identified as crucial parameters via cause-and-effect analysis. To systematically examine their effects, the Taguchi method was employed within the framework of the design of experiment (DoE). Experimental results and statistical analysis revealed that laser power, scanning speed, and layer thickness significantly influence surface roughness parameters: arithmetic mean (Ra) and root mean square (Rq). Main effect plots and energy density analyses confirmed their impact on surface quality. Microscopic investigations identified surface flaws such as spattering, balling, and porosity contributing to poor quality. Given the complex interplay between parameters and surface quality, accurately predicting their effects is challenging. To address this, machine learning models, specifically random forest regression (RFR) and support vector regression (SVR), were used to predict the effects on surface roughness. The RFR model’s R2 values for predicting Ra and Rq are 97% and 85%, while the SVR model’s predictions are 85% and 66%, respectively. Evaluation metrics demonstrated that the RFR model outperformed SVR in predicting surface roughness.
https://link.springer.com/article/10.1007/s00170-024-14087-5?fromPaywallRec=true
Journal of Intelligent Manufacturing
A universal predictor-based machine learning model for optimal process maps in laser powder bed fusion process
Published: 23 August 2022
Volume 34, pages 3341–3363, (2023)
Journal of Intelligent Manufacturing
Zhaochen Gu, Shashank Sharma, Daniel A. Riley, Mangesh V. Pantawane, Sameehan S. Joshi, Song Fu & Narendra B. Dahotre
Abstract
The primary bottlenecks faced by the laser powder bed fusion (LPBF) process is the identification of optimal process parameters to obtain high density (> 99.8%) and a good surface finish (< 10 µm) in the fabricated components. Prediction of optimal process maps with the help of machine learning (ML) models is still challenging due to extensive training data, which proves to be expensive in additive manufacturing. In view of this, the present study employs six different supervised ML algorithms on a comparatively small data set of 33 experiments to predict relative density and surface roughness. It has been observed that input data (predictor) curation can increase the accuracy of the ML models even with a small data set. In the ML prediction model, the mean absolute percentage error (MAPE) was reduced by 30% (relative density) and 21.94% (surface roughness) with volumetric energy density as an input parameter instead of laser power, scanning speed, hatch space, and layer thickness. The choice of non-dimensional energy input as a universal predictor allows for an increase in training size and the translation capability of trained ML models from one machine/material combination to another. The ML model based on increased training data size (198 for relative density and 173 for surface roughness) procured from the material processed/fabricated on different LPBF machines showcased reasonable R2 values of 79.11% and 80.3% for relative density and surface roughness, respectively.
https://link.springer.com/article/10.1007/s10845-022-02004-0?fromPaywallRec=false
Laser Powder Bed Fusion (LPBF) Process
The LPBF parameter space consists of laser power, scan speed, laser spot size, scanning strategy, feedstock, part geometry, and machine conditions. The selection of process parameters determines the resulting microstructure and component properties.
Various libraries of process parameters for a given machine and material have been determined through physical testing by AM suppliers or individual laboratories. An integrated computational materials engineering (ICME) approach reduces the amount of physical testing and informs design engineers about detrimental performance expected for specific process parameters.
Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2019 Symposium (2020)
http://nap.naptionalacademies.org/25620
File name 25620.pdf
Optimization of Laser Powder Bed Fusion Processing
https://www.mdpi.com/2504-4494/3/1/21/pdf
Full Research Article
Open Access
Published: 07 April 2022
Individual process development of single and multi-material laser melting in novel modular laser powder bed fusion system
Jochen Schanz, Nabirul Islam, David Kolb, David K. Harrison, Anjali K. M. De Silva, Dagmar Goll, Gerhard Schneider & Harald Riegel
Progress in Additive Manufacturing volume 7, pages481–493 (2022)
https://link.springer.com/article/10.1007/s40964-022-00276-9
Journal of Manufacturing Processes
Volume 78, June 2022, Pages 231-241
Journal of Manufacturing Processes
Increasing productivity of laser powder bed fusion manufactured Hastelloy X through modification of process parameters
Claudia Schwerz, Fiona Schulz, Elanghovan Natesan,Lars Nyborg
Pub. 30.5.2022
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