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Calta, Nicholas P.

Publications and source records attributed to Calta, Nicholas P..

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Height-resolved emission spectroscopy and high-speed imaging of the TiAl6V4 vapor plume under laser powder bed fusion conditions

Optical emission spectroscopy is increasingly used as an in situ monitoring technique during laser powder bed fusion (LPBF) because plume emission holds elemental information not found in other in situ sensing techniques. This work explores the shape, stability, and temperature of the emission plume above the melt pool of Ti6Al4 V undergoing laser scans under LPBF-like processing conditions, using high-speed video and height-resolved spectroscopy to study the details of plume dynamics. Optical emission spectroscopy is conducted in the 480 nm to 525 nm region, where Ti emission is strong, with 0.3 mm vertical resolution above the baseplate. The Boltzmann plot method is used to determine temperature at each probed elevation, which indicates that the hottest location of the plume is occasionally elevated 0.3 mm to 0.6 mm above the scanning surface. The results show that the plume shape, stability, temperature, and spectra are highly dependent on the processing conditions. We highlight some of the complexities of optical emission spectroscopy and discuss potential challenges for implementing optical emission spectroscopy across an entire build.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In situ x-ray imaging to understand subsurface behavior during continuous wave laser drilling

A limited understanding regarding the underlying dynamics and mechanisms of material removal during continuous wave laser drilling has presented significant challenges in achieving precision and process control. Here, to address this, we employed high-fidelity, in situ synchrotron x-ray imaging to reveal previously unknown material behaviors during continuous wave laser drilling with power modulation. Our findings highlight that high-aspect ratio drill holes are achieved when the laser modulation frequency falls within the range of 8–12 kHz, provided that the laser average power and modulation amplitude levels meet the specified limits. Under these conditions, we identified a material removal mechanism driven by incremental accumulation of recoil pressure that gradually pushes material upward from deep within the substrate to the surface. This mechanism manifested as a low-frequency fluctuation in the vapor depression depth, resulting in periodic instances of material ejection. Furthermore, our study underscores that rapid expansion of the melt pool and the widening of the drill hole opening can impede effective material removal by redirecting energy from material ejection to increasing the melt pool size. This investigation contributes essential insights into the subsurface dynamics involved in the drilling of high-aspect ratio holes, furthering our fundamental understanding of this intricate process.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Direct mechanistic connection between acoustic signals and melt pool morphology during laser powder bed fusion

Various nondestructive diagnostic techniques have been proposed for in situ process monitoring of laser powder bed fusion (LPBF), including melt pool pyrometry, whole-layer optical imaging, acoustic emission, atomic emission spectroscopy, high speed melt pool imaging, and thermionic emission. Correlations between these in situ monitoring signals and defect formation have been demonstrated with acoustic signals having been shown to predict pore formation with especially high confidence in recent machine learning studies. Here, in this work, time-resolved acoustic data are collected in both the conduction and keyhole welding regimes of LPBF-processed Ti-6Al-4V alloy. A non-dimensionalized Strouhal number analysis, used in whistle aeroacoustics, is applied to demonstrate that the acoustic signals recorded in the keyhole regimes can be directly associated with the vapor depression morphology. This mechanistic understanding developed from whistle aeroacoustics shows that acoustic monitoring during the LPBF process can provide a direct probe into the vapor depression dynamics and defect occurrence, especially in the keyhole regimes relevant to printing and defect formation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

42 ENGINEERING↗
DOE OSTI2024

An Electrical Resistance Diagnostic for Conductivity Monitoring in Laser Powder Bed Fusion

With the growing interest in metal additive manufacturing using laser powder bed fusion (LPBF), there is a need for advanced in-situ nondestructive evaluation (NDE) methods that can dynamically monitor manufacturing process-related variations, that can be used as a feedback mechanism to further improve the manufacturing process, leading to parts with improved microstructural properties and mechanical properties. Current NDE techniques either lack sensitivity beyond build layer, are costly or time-consuming, or are not compatible for in-situ integration. In this research, we develop an electrical resistance diagnostic for in-situ monitoring of powder fused regions during laser powder bed fusion printing. The technique relies on injecting current into the build plate and detecting voltage differences from conductive variations during printing using a simple, cheap four-point electrode array directly connected to the build plate. A computational model will be utilized to determine sensitivities of the approach, and preliminary experiments will be performed during the printing process to test the overall approach.

36 MATERIALS SCIENCE↗
DOE OSTI2024

System and method for in situ inspection of defects in additively manufactured parts using high speed melt pool pyrometry

A system and method is disclosed for detecting anomalies in an additively manufactured part. An energy source generates a signal forming an optical beam for creating a melt pool in a layer of feedstock material being selectively fused to make a part in an additive manufacturing operation. A sensor is configured to receive a signal reflected from the melt pool. The reflected signal forms a thermal signal indicative of a temperature of the feedstock material at a known location on a layer of the feedstock material while the feedstock material is being fused at the known location. A controller receives and analyzes data relating to the received signal to determine if an anomaly exists at the known location.

Forien, Jean-Baptiste↗
DOE OSTI2023

Uncovering acoustic signatures of pore formation in laser powder bed fusion

Abstract We present a machine learning workflow to discover signatures in acoustic measurements that can be utilized to create a low-dimensional model to accurately predict the location of keyhole pores formed during additive manufacturing processes. Acoustic measurements were sampled at 100 kHz during single-layer laser powder bed fusion (LPBF) experiments, and spatio-temporal registration of pore locations was obtained from post-build radiography. Power spectral density (PSD) estimates of the acoustic data were then decomposed using non-negative matrix factorization with custom $$\varvec{k}$$ k -means clustering (NMF $$\varvec{k}$$ k ) to learn the underlying spectral patterns associated with pore formation. NMF $$\varvec{k}$$ k returned a library of basis signals and matching coefficients to blindly construct a feature space based on the PSD estimates in an optimized fashion. Moreover, the NMF $$\varvec{k}$$ k decomposition led to the development of computationally inexpensive machine learning models which are capable of quickly and accurately identifying pore formation with classification accuracy of supervised and unsupervised label learning greater than 95% and 90%, respectively. The intrinsic data compression of NMF k , the relatively light computational cost of the machine learning workflow, and the high classification accuracy makes the proposed workflow an attractive candidate for edge computing toward in-situ keyhole pore prediction in LPBF.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography

Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Denoising Autoencoder for Improved Kikuchi Pattern Quality and Indexing in Electron Backscatter Diffraction

The rapid collection and indexing of electron diffraction patterns as produced via electron backscatter diffraction (EBSD) has enabled crystallographic orientation and structural determination, as well as additional property-determining strain and dislocation density information with increasing speed, resolution, and efficiency. Pattern indexing quality is reliant on the noise of the collected electron diffraction patterns, which is often convoluted by sample preparation and data collection parameters. EBSD acquisition is sensitive to many factors and thus can result in low confidence index (CI), poor image quality (IQ), and improper minimization of fit, which can result in noisy datasets and misrepresent the microstructure. In an attempt to enable both higher speed EBSD data collection and enable greater orientation fit accuracy with noisy datasets, an image denoising autoencoder was implemented to improve pattern quality. Here, we show that EBSD data processed through the autoencoder results in a higher CI, IQ, and a more accurate degree of fit. In addition, using denoised datasets in HR-EBSD cross correlative strain analysis can result in reduced phantom strain from erroneous calculations due to the increased indexing accuracy and improved correspondence between collected and simulated patterns.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Applying protective coatings to optical fibers

Apparatus, systems, and methods that provide coats on a glass optical fiber including of an inner layer and an outer layer. The method includes two steps. First, a conductive polymer coating is applied to the optical fiber as it is being produced. Second, a protective coating is applied to that conductive polymer coating. The conductive polymer coating is applied immediately after the fiber is drawn from preform to fiber.

Calta, Nicholas P.↗
DOE OSTI2022

Detecting missing struts in metallic micro-lattices using high speed melt pool thermal monitoring

Metal lattices are an important class of cellular materials that offer great advantages by providing high-strength and lightweight structures as compared to bulk materials. Progress in additive manufacturing techniques has led to increased complexity in design and shape of produced objects and is greatly beneficial for the development of metallic lattice structures. However additive manufacturing of lattices suffers from unpredictable defect creation that can compromise its mechanical integrity. Although post-build inspection techniques can provide quality assurance of the process, accurate assessment can be technically challenging, time consuming and costly. In this work, we investigate the use of high-speed measurements of thermal emission from the melt pool to identify defective individual struts formed with a missing bottom half in an otherwise fully built lattice structure produced with laser powder bed fusion. Surprisingly, results indicate lower photodiode signal, suggesting colder melt pool surface temperature, when printing struts with missing bottom half as compared to nominal struts. Additional thermographic imaging and multi-physics simulations reveal that the low photodiode signal is accompanied by presence of hot spatters carrying heat away from detection and continuous avalanche of powder on the melt pool. Based on these observations, a method was developed to identify defective individual struts with missing bottom half in full built lattices. This prediction approach provides valuable insights about part quality which are important for process qualification and illustrates the utility of melt pool thermal emission monitoring for identifying specific defects introduced by laser powder bed fusion.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Sensor fusion of pyrometry and acoustic measurements for localized keyhole pore identification in laser powder bed fusion

We report in-situ process monitoring as an aid to part qualification for laser powder-bed fusion (L-PBF) technology is a topic of increasing interest to the additive manufacturing community. In this work, airborne acoustic and inline pyrometry measurements were recorded simultaneously with the laser position. X-ray radiography imaging was used to spatio-temporally register keyhole pore locations to the pyrometry and acoustic signals, enabling binary labeling of data partitions based on pore formation. These labeled partitions were subsequently featurized using a highly comparative time-series analysis toolbox. Acoustic data was found to be much more effective than the pyrometry data for keyhole pore identification. However, when the information contained in both sensing modalities was combined in a sensor fusion strategy, the error rates of the top performing models were significantly reduced.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Two-color high speed thermal imaging system for laser-based additive manufacturing process monitoring

Monitoring melt pool temperature in laser powder bed fusion by providing a build laser that produces a laser beam that is directed onto the melt pool and produces an incandescence that emanates from the melt pool, receiving the incandescence and producing a first image having a first spectral band and a second image having a second spectral band, and determining the ratio of said first image having a first spectral band and said second image having a second spectral band to monitor the melt pool temperature.

Calta, Nicholas P.↗
DOE OSTI2022

A laser powder bed fusion system for operando synchrotron x-ray imaging and correlative diagnostic experiments at the Stanford Synchrotron Radiation Lightsource

Laser powder bed fusion (LPBF) is a highly dynamic multi-physics process used for the additive manufacturing (AM) of metal components. Improving process understanding and validating predictive computational models require high-fidelity diagnostics capable of capturing data in challenging environments. Synchrotron x-ray techniques play a vital role in the validation process as they are the only in situ diagnostic capable of imaging sub-surface melt pool dynamics and microstructure evolution during LPBF-AM. In this article, a laboratory scale system designed to mimic LPBF process conditions while operating at a synchrotron facility is described. The system is implemented with process accurate atmospheric conditions, including an air knife for active vapor plume removal. Significantly, the chamber also incorporates a diagnostic sensor suite that monitors emitted optical, acoustic, and electronic signals during laser processing with coincident x-ray imaging. The addition of the sensor suite enables validation of these industrially compatible single point sensors by detecting pore formation and spatter events and directly correlating the events with changes in the detected signal. Experiments in the Ti–6Al–4V alloy performed at the Stanford Synchrotron Radiation Lightsource using the system are detailed with sufficient sampling rates to probe melt pool dynamics. X-ray imaging captures melt pool dynamics at frame rates of 20 kHz with a 2 µm pixel resolution, and the coincident diagnostic sensor data are recorded at 470 kHz. Furthermore, this work shows that the current system enables the in situ detection of defects during the LPBF process and permits direct correlation of diagnostic signatures at the exact time of defect formation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A mesoscopic digital twin that bridges length and time scales for control of additively manufactured metal microstructures

We present our recent development of an integrated mesoscale digital twin (DT) framework for relating processing conditions, microstructures, and mechanical responses of additively manufactured (AM) metals. In particular, focusing on the laser powder bed fusion technique, we describe how individual modeling and simulation capabilities are coupled to investigate and control AM microstructural features at multiple length and time scales. We review our prior case studies that demonstrate the integrated modeling schemes, in which high-fidelity melt pool dynamics simulations provide accurate local thermal profiles and histories to subsequent AM microstructure simulations. We also report our new mechanical response modeling results for predicted AM microstructures. In addition, we illustrate how our DT framework has been validated through modeling–experiment integration, as well as how it has been practically utilized to guide and analyze AM experiments. Finally, we share our perspectives on future directions of further development of the DT framework for more efficient, accurate predictions and wider ranges of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Cooling dynamics of two titanium alloys during laser powder bed fusion probed with in situ X-ray imaging and diffraction

Metal parts produced by laser powder bed fusion (LPBF) additive manufacturing exhibit characteristic microstructures comparable to those observed in laser welding. The primary cause of this characteristic microstructure is rapid, localized heating and cooling cycles, which result in extreme thermal gradients where material solidification is followed by fast cooling in the solid state. The final microstructure and mechanical performance are also influenced by pore formation caused by melt pool fluid dynamics. Here, we use high speed, in situ X-ray diffraction to probe the kinetics of cooling and solid-solid phase transitions after laser melting in two aerospace titanium alloys: Ti-6Al-4V, an α + β alloy; and Ti-5Al-5V-5Mo-3Cr, a near-β alloy. We complement these diffraction studies with in situ X-ray imaging to probe melt pool dynamics and pore formation. From these two complementary probes, we quantify pore formation during melting and the subsequent microstructural evolution as the material rapidly cools after solidification. These results are critical for understanding defect formation and residual stress development in different titanium alloys under LPBF conditions and can help inform process models to predict final part performance.

36 MATERIALS SCIENCE↗