The adoption of laser powder bed fusion (L-PBF) for metals by the industry has been limited despite the significant progress made in the development of the process chain. One of the key obstacles i...

Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing

Despite the recent progress in additive manufacturing (AM) process and technology, challenges in the repeatability and reproducibility of AM parts still hinders the adoption of this technique in many industries. This is particularly difficult when a part is qualified on a particular part on a certain machine using optimised parameters. If a manufacturer wishes to expand production to multiple machines, the ability to translate these optimised parameters to different machines much be understood. In this study, four different metal L-PBF printers were used to produce 316L tensile testing samples using the same processing parameters and metal powder supplied from a single batch from the same supplier. In addition to the analysis of the correlation between the input parameters and the output measures, this study reports that despite the same set process parameters, there is significant variations were found in the mechanical performance and properties of the AM samples produced on the different L-PBF metal additive manufacturing machines. For the range of the input processing parameters and the resulting input volumetric energy density applied of 21–37 J/mm3, values of (4–42)%, (200–716) MPa, and (52–214) GPa were obtained for the elongation, ultimate tensile strength and elastic modulus on additively manufactured 316L samples respectively.

Comparison of the porosity and mechanical performance of 316L stainless steel manufactured on different laser powder bed fusion metal additive manufacturing machines

Mutual comparison of acoustic, pyrometric and thermographic laser powder bed fusion monitoring

Powder bed fusion (PBF)
process is a metal additive manufacturing process, which can build parts with
any complexity from a wide range of metallic materials. Research in the PBF
process predominantly focuses on the impact of a few parameters on the ultimate
properties of the printed part. The lack of a systematic approach to optimizing
the process parameters for a better performance of given material results in a
sub-optimal process limiting the potentialof the application. This process
needs a comprehensive study of all the influential parameters and their impact
on the mechanical and microstructural properties of a fabricated part.
Furthermore, there is a need to develop a quantitative system for mapping the
material properties and process parameters with the ultimate quality of the
fabricated part to achieve improvement in the manufacturing cycle as well as
the quality of the final part produced by the PBF process. To address the
aforementioned challenges, this research proposes a framework to optimize the process
for 316L stainless steel material. This framework characterizes the influence
of process parameters on the microstructure and mechanical properties of the
fabricated part using a series of experiments. These experiments study the
significance of process parameters and their variance as well as study the microstructure
and mechanical properties of fabricated parts by conducting tensile, impact,
hardness, surface roughness, and densification tests, and ultimately obtain the
optimum range of parameters. This would result in a more complete understanding
of the correlation between process parameters and part quality. Furthermore,
the data acquired from the experimentsare employed to develop an intelligent
parameter suggestion multi-layer feedforward (FF) backpropagation (BP)
artificial neural network (ANN). This network estimates the fabrication time
and suggests the parameter setting accordingly to the user/manufacturers
desired characteristics of the end-product. Further, research is in progress to
evaluate the framework for assemblies and complex part designs and incorporate
the results in the network for achieving process repeatability and consistency.

A Framework for Optimizing Process Parameters in Powder Bed Fusion (PBF) Process Using Artificial Neural Network (ANN)

Inspection of drilled holes in aerospace Carbon Fiber Reinforced Polymers (CFRPs) is crucial to avoid failure of attachments and disintegration of aircraft structures. Vision-based systems can provide an efficient tool for rapid inspection of holes directly on production lines. However, automatic detection of damages in digital images is a challenging task due to the dark, textured, and semi-specular surfaces of CFRPs. This paper proposes a novel and fully autonomous system for accurate detection and segmentation of damages and cracks around drilled holes in CFRP composites. The proposed system comprises three modules: (1) An automated multi-light imaging end-effector is designed to sequentially illuminate the inspected hole from four different directions and capture images for each case. The four images are then fused to suppress the background and enhance the visibility of damages. (2) A series of image processing steps are proposed to automatically segment the hole profile, damage area, and crack lines in the fused images. The segmented images serve as labeled outputs (masks) in the following deep learning model. (3) A deep Fully Convolutional Network (FCN) with the U-Net architecture is designed and trained for pixel-wise semantic segmentation of hole images. Once trained, the U-Net model can reliably detect the hole profile, damages, and cracks directly from ‘raw’ multi-light images without any image processing required. Experimental tests show that the U-Net model can provide real-time and fully automated evaluation of the delamination factor with a maximum error of 5.4%.

Automated vision-based inspection of drilled CFRP composites using multi-light imaging and deep learning

This paper addresses the problem of classification when target data are subject to feature uncertainties. A robust approach based on Sobol sensitivity analysis is proposed to improve the robustness of support vector machine (SVM) models. SVM is a supervised machine learning method for pattern recognition whose performance depends on the definition of its hyperparameters and the quality of data. The proposed approach analyzes the impact of the uncertainties on the predictive performance of SVM based on Sobol’ sensitivity analysis. Afterwards, a new parameter is introduced to improve the robustness of SVM to the impact of uncertainties. The efficiency of this approach is evaluated by applying it to six real-world datasets. The results are then discussed and analyzed.

Handling the impact of feature uncertainties on SVM: A robust approach based on Sobol sensitivity analysis

The Laser Powder Bed Fusion (L-PBF) process is being adopted in different industrial fields. However, L-PBF currently lacks process reproducibility and quality. Hence, quality monitoring techniques need to be adopted in order to reduce the process variability and to ensure a high-quality process. Accordingly, this work proposes a quality monitoring approach based on machine learning which links the optical signal of a layer to the density of the final part. The approach consists of selecting relevant statistical features from optical data and validating these features by assessing their ability in predicting the different density classes of different products. Afterwards, the approach is compared to a new deep learning framework that allows predicting a part density from the corresponding raw optical signals. This comparison allows assessing the relevance of the identified statistical features. The proposed monitoring approach is applied on cubical specimens produced with different process parameters, and the results are then discussed and analyzed.

Optical process monitoring for Laser-Powder Bed Fusion (L-PBF)

Identification of the key manufacturing parameters impacting the prediction accuracy of support vector machine (SVM) model for quality assessment

Characterization of laser powder bed fusion (L-PBF) process quality: A novel approach based on statistical features extraction and support vector machine

Wahb Zouhri

Papers

Handling the impact of feature uncertainties on SVM: A robust approach based on Sobol sensitivity analysis

Optical process monitoring for Laser-Powder Bed Fusion (L-PBF)

Identification of the key manufacturing parameters impacting the prediction accuracy of support vector machine (SVM) model for quality assessment

Identification of the key manufacturing parameters impacting the prediction accuracy of support vector machine (SVM) model for quality assessment

Characterization of laser powder bed fusion (L-PBF) process quality: A novel approach based on statistical features extraction and support vector machine