Search NASA⌕ Search

SEARCH · Search NASA

Results for “in situ processing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An innovative digital image correlation technique for in-situ process monitoring of composite structures in large scale additive manufacturing

As additive manufacturing (AM) continues to develop and become a standardized manufacturing method, there will be a continued need to provide in-situ monitoring during the manufacturing of polymer composite printed components. Thermal residual stress is a primary cause of failures such as interlayer disbonds or delamination, micro cracking, and dimensional instability, which can occur during or after the build. Here, we report a novel digital image correlation (DIC) adaptation to monitor thermal residual stresses during the entire print process for large-scale AM. In this work, DIC has been investigated (a) by the natural speckle produced by the polymer surface for correlation, (b) to monitor AM build, and (c) to evaluate the effect of thermal residual stress on warpage of the printed component. The natural speckle pattern of the AM material resulted in a respectable 3.57% error compared to the traditional painted speckle pattern of 3.05% error. DIC measured a 190% increase in vertical displacement at the edge of the wall compared to the center, indicating warpage during AM. This work is a step towards a non-intrusive residual stress measuring technique using DIC for large-scale AM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In-Situ Process Monitoring Evaluation and Demonstration using Advanced Characterization with Laser Powder Bed Systems

Oak Ridge National Laboratory’s (ORNL) Manufacturing Demonstration Facility (MDF) worked with EOS Group to evaluate the current in-situ sensor capabilities of an EOS M290 Laser Powder Bed Fusion machine. The M290 was fitted with a 1 Mega-Pixel (MP) grayscale visible-light camera and a 5 MP temporally integrated (TI) near-infrared (NIR) camera. One print from stainless steel (SS) 316 and two from Inconel 625 (IN625) were performed where data including in-situ imaging and a machine log file were captured. These data were subsequently analyzed using a Dynamic Multi-Scale Segmentation Convolutional Neural Network (DMSCNN) trained on user defined classes and correlated to as-printed flaws, in the form of porosity, discovered in X-Ray Computed Tomography (XCT). In Phase I, two indications were detected in-situ and spatially correlated to stochastic lack-of-fusion flaws discovered using XCT. In Phase II, using these links from in-situ signatures to XCT flaw populations, a second neural network (NN) was trained to create a Voxelized Property Prediction Model (VPPM) to predict porosity percentages within the part using only features garnered from the in-situ data from two IN625 complex geometries. The VPPM was able to accurately predict porosity values for IN625 parts with an R 2 value of 0.764.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In-Situ Process Monitoring Evaluation and Demonstration using Advanced Characterization with Laser Powder Bed Systems

Oak Ridge National Laboratory’s (ORNL) Manufacturing Demonstration Facility (MDF) worked with EOS Group to evaluate the current in-situ sensor capabilities of an EOS M290 Laser Powder Bed Fusion machine. The M290 was fitted with a 1 Mega-Pixel (MP) grayscale visible-light camera and a 5 MP temporally integrated (TI) near-infrared (NIR) camera. One print from stainless steel (SS) 316 and two from Inconel 625 (IN625) were performed where data including in-situ imaging and a machine log file were captured. These data were subsequently analyzed using a Dynamic Multi-Scale Segmentation Convolutional Neural Network (DMSCNN) trained on user defined classes and correlated to as-printed flaws, in the form of porosity, discovered in X-Ray Computed Tomography (XCT). In Phase I, two indications were detected in-situ and spatially correlated to stochastic lack-of-fusion flaws discovered using XCT. In Phase II, using these links from in-situ signatures to XCT flaw populations, a second neural network (NN) was trained to create a Voxelized Property Prediction Model (VPPM) to predict porosity percentages within the part using only features garnered from the in-situ data from two IN625 complex geometries. The VPPM was able to accurately predict porosity values for IN625 parts with an R 2 value of 0.764.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Role of NDE and In-Situ Process Monitoring in Managing Risk of AM Space Hardware

The recently published NASA-STD-6030 defines the Additive Manufacturing (AM) Requirements for Spaceflight Systems. Key aspects of the certification approach include the development of a qualified material process (QMP) and material characterization determined by part classification. Nondestructive evaluation (NDE) of the full surface and volume is required for all part classifications except those with negligible risk. NASA is exploring the use of in-process monitoring data to improve risk posture and supplement post-build inspection for complex parts. Currently, the most challenging obstacle to overcome is linking the indications in the monitoring data to the physics of the process and the final material state of the finished part. NASA is undertaking studies to understand and quantify this relationship for various monitoring methods. The desired goal is to develop a protocol to establish this correlation for any monitoring method. Once this correlation is known, the critical defect size can be linked to a representative indication in the monitoring data, and the capability of the monitoring system can be tested using the 90/95 probability of detection requirement for NDE methods. This would enable the use of in-process monitoring as a defect screening activity for AM part certification. Many high-criticality components built with AM have high complexity and therefore limited inspectability, so using in-process monitoring can help address this certification gap. The use of adaptive, closed-loop monitoring systems that alter the locked process will require a new approach to the QMP.

advanced manufacturing↗
NASA NTRSDate not supplied

Role of NDE and In-Situ Process Monitoring in Managing Risk of AM Space Hardware

The recently published NASA-STD-6030 defines the Additive Manufacturing (AM) Requirements for Spaceflight Systems. Key aspects of the certification approach include the development of a qualified material process (QMP) and material characterization determined by part classification. Nondestructive evaluation (NDE) of the full surface and volume is required for all part classifications except those with negligible risk. NASA is exploring the use of in-process monitoring data to improve risk posture and supplement post-build inspection for complex parts. Currently, the most challenging obstacle to overcome is linking the indications in the monitoring data to the physics of the process and the final material state of the finished part. NASA is undertaking studies to understand and quantify this relationship for various monitoring methods. The desired goal is to develop a protocol to establish this correlation for any monitoring method. Once this correlation is known, the critical defect size can be linked to a representative indication in the monitoring data, and the capability of the monitoring system can be tested using the 90/95 probability of detection requirement for NDE methods. This would enable the use of in-process monitoring as a defect screening activity for AM part certification. Many high-criticality components built with AM have high complexity and therefore limited inspectability, so using in-process monitoring can help address this certification gap. The use of adaptive, closed-loop monitoring systems that alter the locked process will require a new approach to the QMP.

additive manufacturing↗
DOE OSTI2023

Review of in situ process monitoring for metal hybrid directed energy deposition

Hybrid manufacturing, or the combination of additive and subtractive manufacturing within a single build volume is transforming the way products are being fabricated. Additive manufacturing confers unprecedented freedom of design and reduced material usage while enabling serial customization. Subtractive manufacturing provides superior surface finish and improved dimensional accuracies. Interwoven, these two digital manufacturing paradigms are enabling the rapid manufacturing of complex, highly accurate, and customized geometries in a diversity of high-performance alloys. In situ monitoring, heavily relied upon in either additive or subtractive, becomes even more crucial with the interplay of the two processes in a single combined build sequence. Moreover, challenges that do not exist in either process alone can now have a dramatic impact on final part quality: (1) a large amount of heat is generated during additive manufacturing deposition, which is primarily dissipated into the machine tooling and impacts accuracy by deforming the material and causing misaligned machining; (2) microstructure, mechanical properties, and residual stresses are the result of the complex thermal histories generated by additive manufacturing and can impact subsequent cutting performance. This comprehensive review considers previous research in monitoring and providing closed-loop control in both additive and subtractive manufacturing separately and then considers the implications of the effectiveness of these monitoring techniques when additive and subtractive processes are integrated together.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accelerating Scientific Workflows on HPC Platforms with In Situ Processing

Scientific workflows drive most modern large-scale science breakthroughs by allowing scientists to define their computations as a set of jobs executed in a given order based on their data dependencies. Workflow management systems (WMSs) have become key to automating scientific workflows-executing computational jobs and orchestrating data transfers between those jobs running on complex high-performance computing (HPC) platforms. Traditionally, WMSs use files to communicate between jobs: a job writes out files that are read by other jobs. However, HPC machines face a growing gap between their storage and compute capabilities. To address that concern, the scientific community has adopted a new approach called in situ, which bypasses costly parallel filesystem I/O operations with faster in-memory or in-network communications. When using in situ approaches, communication and computations can be interleaved. In this work, we leverage the Decaf in situ dataflow framework to accelerate task-based scientific workflows managed by the Pegasus WMS, by replacing file communications with faster MPI messaging. We propose a new execution engine that uses Decaf to manage communications within a sub-workflow (i.e., set of jobs) to optimize inter-job communications. We consider two workflows in this study: (i) a synthetic workflow that benchmarks and compares file- and MPI-based communication; and (ii) a realistic bioinformatics workflow that computes mu-tational overlaps in the human genome. Experiments show that in situ communication can improve the bioinformatics workflow execution time by 22% to 30% compared with file communication. Our results motivate further opportunities and challenges for bridging traditional WMSs with in situ frameworks.

Decaf↗
DOE OSTI2022

Observation of spatter-induced stochastic lack-of-fusion in laser powder bed fusion using in situ process monitoring

Material produced via additive manufacturing (AM) continues to exhibit variable mechanical properties despite apparent optimization of processing parameters, inhibiting qualification efforts and limiting use in critical applications. Stochastic lack-of-fusion flaws may help explain this variability, but the origin of these seemingly random defects has to this point remained unclear. In this work, we show that spatter particles, material ejected from the laser melt pool, are directly responsible for generating stochastic lack-of-fusion in laser-based powder bed fusion components through the application of spatial statistics. Herein a statistically significant, causal relationship between spatter particles and stochastic lack-of-fusion is established, and the spatial and morphological relationships between spatter and internal flaws are investigated. The occurrence of spatter-induced lack-of-fusion in relation to the inert gas flow and laser trajectory direction is also investigated, and recommendations for mitigating the occurrence of spatter are evaluated.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Precision Polishing of Ablator Capsules via in situ Process Monitoring and Machine Learning–Based Optimization

In inertial confinement fusion (ICF) experiments seeking output gains of unity and beyond, the quality of the ablator capsule is paramount for minimizing the hydrodynamic mix that quenches the central hot spot. Defects in the form of foreign particles or missing mass on the surface and within the wall of the capsule are primary offenders. High-density carbon capsules made for ICF experiments at the National Ignition Facility are precision polished to achieve surface smoothness on the order of a few nanometers as well as to minimize isolated defects in the form of pits. Given the critical role of this process, we are developing smart manufacturing techniques with the goal of elevating the efficiency of this process. Our approach is to use MEMS (micro-electromechanical systems)–based sensors to capture the fine vibration signals generated during the polishing process and combine them with synchronized visual feedback as needed. Beyond using these sensors for process monitoring, we use specific deep learning methods to analyze the data and extract correlations with both the process parameters and the final performance of the polishing run. Here, in this work, we describe the multiple fronts we have explored in this regard and the results we have gotten so far. This approach promises to have the potential to ultimately provide real-time feedback that can be used to ensure the progress of the run as well as a means for faster optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Precision Polishing of Spheres Via In-Situ Process Monitoring and Machine-Learning-Based Optimization

Precision polishing for high surface finish is a vital step in the making of many high-tech components for optical and semiconductor modules as well as for wide ranging experimental samples for HED science that is at the core of LLNL’s mission. In particular, the fabrication of extremely smooth and round spheres comprised of low atomic number materials (low-Z such as high-density carbon or HDC, beryllium, hydrocarbon polymers, etc.) for Inertial Confinement Fusion (ICF) is entirely reliant on specialized polishing processes to achieve the surface smoothness.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Precision Polishing of Spheres Via In-Situ Process Monitoring and Machine-Learning-Based Optimization

In inertial confinement fusion (ICF) experiments seeking output gains of unity and beyond, the quality of the ablator capsule is paramount for minimizing hydrodynamic mix that quenches the central hot spot. Defects in the form of foreign particles or missing mass on the surface and within the wall of the capsule are primary offenders. High density carbon capsules made for ICF experiments on the National Ignition Facility (NIF) are precision polished to achieve the surface smoothness in the order of a few nm as well as to minimize isolated defects in the form of pits. Given the critical role of this process, we are developing smart manufacturing techniques with goal of elevating the efficiency of this process. Our approach is to use MEMS-based sensors to capture the fine vibrational signals generated during the polishing process and combine it with synchronized visual feedback as needed. Beyond using these sensors for process monitoring, we use specific deep learning methods to analyze the data and extract correlations with both the process parameters and the final performance of the polishing run. Here, we describe the multiple fronts that we have explored in this regard and the results we have gotten so far. This approach promises to have the potential to ultimately provide real-time feedback that can be used for ensuring the progress of the run as well as a means for faster optimization.

36 MATERIALS SCIENCE↗