Lessons learned in the design of reference fiducials for layer-wise analysis of test coupons made by laser powder bed fusion

Ferrucci, Massimiliano; Craeghs, Tom; Cornelissen, Sven; Pavan, Michele; Dewulf, Wim; Dönmez, Alkan

doi:10.1016/j.addma.2021.101997

Cited by 3 publications

(3 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With an accurate data alignment also the first aspect can be better studied, by reliably distinguishing relevant and not relevant process anomalies. In order to improve the accuracy of alignment between in-process and post-process gathered data, adequate sample design and alignment procedures are needed, as remarked in [18]. However, research works published so far investigated the correlation using simple geometries, such as cylinders or cubes, with no use of fiducials properly designed for aiding the alignment [19].…”

Section: Introductionmentioning

confidence: 99%

On the use of X-ray computed tomography for the improvement of metal laser powder bed fusion process monitoring

Bonato¹,

Zanini²,

Carmignato³

2023

eJNDT

View full text Add to dashboard Cite

Laser-powder bed fusion (LPBF) is increasingly used as a successful metal additive manufacturing technology in different industrial sectors, such as biomedical and aerospace, especially for the production of parts with high geometrical complexity. However, LPBF is still affected by repeatability issues and the presence of several defects within the fabricated components. This aspect is therefore a hindrance in the spreading of this prospective technology, which in several cases is still not able to meet the stringent quality requirements imposed by such industries. In order to overcome these issues, X-ray computed tomography (CT) acquires a fundamental role in the understanding of critical aspects and for process development and optimization. Also considering the growing interest regarding the development of LPBF process monitoring systems, which seek to identify and possibly correct defects during fabrication, CT is very much needed for the generation of reference data concerning actual geometry and internal defects. Nevertheless, this implies the need for an accurate comparison between datasets obtained by in-process acquisitions and post-process measurements, with the aim of successfully correlating process events to actual defects. However, to enhance the comparison accuracy, deformations occurring during and after manufacturing need to be considered when performing in- and post-process data registration, given that this aspect is greatly affecting the outcome of correlations. This work studies a sample geometry that is specifically developed for enhancing data registration accuracy, along with the related alignment procedure. The methodology has also the advantage to be applicable to any kind of in-process monitoring data, despite this paper uses optical imaging for the acquisition of long-exposure digital images, which proved to be suited for detecting spatter particles. Post-process datasets were obtained through metrological X-ray computed tomography, which provides detailed information regarding internal defects.

show abstract

Section: Introductionmentioning

confidence: 99%

On the use of X-ray computed tomography for the improvement of metal laser powder bed fusion process monitoring

Bonato¹,

Zanini²,

Carmignato³

2023

eJNDT

View full text Add to dashboard Cite

show abstract

“…Another popular method to monitor the process is through inspection of the whole layer images. 22,23 These images are taken before and after each layer's sintering process and can be used to determine the geometry deviation to detect shrinkage 24 and larger instances of incomplete fusion. 25,26 A study by Grasso et al 27 successfully extracted features from layer-wise thermal images via a PCA method and matched them to anomalous geometry.…”

Section: Introductionmentioning

confidence: 99%

“…36,37 Aberrations in expected surface quality or errors in contour shape are easily detected via these methods. 22,23 Abdelrahman et al 25 were able to detect geometric deviations in a variety of lighting conditions by comparing build geometry to commanded geometry. Although these methods are accurate, they detect and classify faults at the end of each layer sinter as opposed to during the process.…”

Section: Introductionmentioning

confidence: 99%

An unsupervised online anomaly detection method for metal additive manufacturing processes via a statistical time-frequency domain algorithm

Chen,

Kopsaftopoulos,

Mishra

2023

Structural Health Monitoring

View full text Add to dashboard Cite

Anomalies often occur in metal additive manufacturing from processing inconsistencies and uncertainty. A robust fault detection system that uses sensor measurements such as melt pool imaging has the potential to improve part quality and save production time by anticipating print failure. Toward this goal, we develop and validate a fault detection technique using melt pool geometry-related measurements from an in situ near-infrared optical camera. This method is unsupervised and is trained on a small dataset, mitigating human error in classifying fault types, and reducing lead times for preparing training datasets. Furthermore, this method uses learned geometry-informed nominal behavior of the melt pool signal to make informed decisions on the process health. There are spatial-temporal characteristics embedded in the melt pool images, caused by the periodicity in the geometry-dependent raster pattern. These characteristics can be captured in the frequency domain using the signal spectrogram, a representation of the frequency content over time. Defects will appear in the spectrogram, disrupting the healthy spectral response. To quantify healthy spectrograms, we use principal component (PC) decomposition to extract the features of these spectrograms as a set of nominal basis vectors. Anomaly detection is then performed by calculating the error between the original and reconstructed spectrogram vector by projection of the spectrogram PCs onto the nominal basis. The reconstruction error for anomalous signals is larger than that from healthy signals, which is then used for fault detection. A one-tailed statistical test is used to determine the fault detection threshold for the reconstruction error signal. This method is tested on three raster patterns and performs better than a comparative time-series thresholding method. We demonstrate that this time-frequency algorithm can detect both temporal faults (which occur at a single time instant) and spatial faults (such as those introduced by an improper sintering), differentiating them from nominal operation.

show abstract

Deformations modelling of metal additively manufactured parts and improved comparison of in-process monitoring and post-process X-ray computed tomography

Bonato,

Zanini,

Carmignato

2023

Additive Manufacturing

View full text Add to dashboard Cite

Lessons learned in the design of reference fiducials for layer-wise analysis of test coupons made by laser powder bed fusion

Cited by 3 publications

References 61 publications

On the use of X-ray computed tomography for the improvement of metal laser powder bed fusion process monitoring

On the use of X-ray computed tomography for the improvement of metal laser powder bed fusion process monitoring

An unsupervised online anomaly detection method for metal additive manufacturing processes via a statistical time-frequency domain algorithm

Deformations modelling of metal additively manufactured parts and improved comparison of in-process monitoring and post-process X-ray computed tomography

Contact Info

Product

Resources

About