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Presentation on 2024 MEA Manufacturing R&D project, the vision of which is to lead research, development and demonstration of high thruput quality control methodology, with the aim of facilitating the industry-wide scaling up of fuel cell and electrolyzer MEAs and stacks.
In this project we successfully demonstrated the capabilities of the Reactive Spray Deposition Technology (RSDT) to fabricate large-scale CCMs for advanced PEMWEs that have one-order of magnitude lower PGM loading in their catalyst layers, and performance comparable with the commercial state-of-the-art CCMs. The RSDT is a unique methodology that combines the catalyst synthesis and CCM fabrication in one step and reduces dramatically the time for CCM manufacturing. As fabricated large-scale CCMs with geometric area of 680 cm2 demonstrated excellent activity and durability performance, and the novel duo-recombination layer design paves the way for solving the safety concerns related to PEMWEs. In addition, excellent activity and durability performance has been demonstrated with RSDT fabricated CCMs with thinner membranes and duo RL design. This is a novel approach for further performance improvement of the MEAs for PEMWEs that has been successfully demonstrated for the first time in this project. The integration of the in-situ laser diagnostics system along with the in-line optical quality control system within the RSDT that has been achieved and demonstrated in this project, is an example for possibility of designing and building advanced manufacturing technologies that can meet the requirements of the future manufacturing. Therefore, the RSDT offers a precise real-time monitoring and control of the particles size, composition, loading, porosity, thickness, and defects in the catalysts’ layers, which render this technology as the best candidate for manufacturing of cost effective CCMs for PEMWEs. By using RSDT we successfully met all project’s milestones, Go/No-Go decision, objectives, goals, and deliverables.
Results presented here demonstrate the effect of several prebond surface contaminants (hydrocarbon, machining fluid, latex, silicone, peel ply residue, release film) on bond quality, as measured by fracture toughness and failure modes of carbon fiber reinforced epoxy substrates bonded in secondary and co-bond configurations with paste and film adhesives. Additionally, the capability of various prebond surface property measurement tools to detect contaminants and potentially predict subsequent bond performance of three different adhesives is also shown. Surface measurement methods included water contact angle, Dyne solution wettability, optically stimulated electron emission spectroscopy, surface free energy, inverse gas chromatography, and Fourier transform infrared spectroscopy with chemometrics analysis. Information will also be provided on the effectiveness of mechanical and energetic surface treatments to recover a bondable surface after contamination. The benefits and drawbacks of the various surface analysis tools to detect contaminants and evaluate prebond surfaces after surface treatment were assessed as well as their ability to correlate to bond performance. Surface analysis tools were also evaluated for their potential use as in-line quality control of adhesive bonding parameters in the manufacturing environment.
In high-performance polymer matrix composite assemblies, adhesive bonding is generally superior to mechanical fastening in structural performance and manufacturing efficiency. However, adhesive bonds are susceptible to minute levels of contamination accumulated during assembly that can lead to unpredictable, weak bonds. Current methods of measuring bond strength are all destructive mechanical tests. To overcome these challenges, redundant load paths (e.g., mechanical fasteners) are often implemented in secondary-bonded, primary-structures, which can greatly reduce structural performance. This study investigated reformulated aerospace epoxy matrix resins with stoichiometric offset to inhibit cure of the matrix resin prior to assembly. Inhibited resins can reflow and mix across the joint interface, which eliminates the material discontinuity and forms a homogenous joint with reliable fracture properties. The goal of this study was to develop and demonstrate secondary composite assemblies that are mechanically and microscopically indistinguishable from a co-cured composite joint. This article describes the development of latent epoxy resins, the fabrication of test articles, and the mechanical properties measured from experimental joints compared with conventional, co-cured laminates. Methods of in-line quality control using and infrared spectroscopy and post-assembly forensics are also described. The final mode-II fracture toughness measured from precracked AERoBOND specimens was similar to that measured from co-cured laminates indicating that later cure epoxy materials could be a suitable replacement for secondary bonding.
Quality monitoring is a critical aspect for manufacturing systems. Ideally the monitoring would be done in-line, be non-contact, non-destructive, and fast. This would enable reduced scrap and higher throughput. This poster presents an optical transmission method for evaluating and mapping coatings. With the method shown in the poster we can visualize optical variations on the macro and micro scales. This allows us to see the overall trend in loading in both the cross web and down web directions. Furthermore, we can visualize defects such dewetting spots, streaks, clumps, and pinholes where there is a lack of coating. The optical transmission signal has been found to be proportional to the IrOx loading signal using XRF measurements. Therefore, an optical transmission setup can be installed in-line and allow for a fast, non-contact method for mapping loading variations and defects.
This project will focus on diagnostics and quality control understandings of continuously produced FFI membranes [both Anion Exchange Membrane (AEM) and Proton Exchange Membrane (PEM)] and continuously produced FFI electrodes. Optimization of methods and novel quality control techniques will be developed as needed.
Hydrogen peroxide is currently produced at central chemical plants via the anthraquinone oxidation process. This process produces environmental pollutants that are costly to remediate, requires hazardous long distance shipping of highly concentrated peroxide (50% or 70%), and necessitates extra handling costs related to storage and dilution. Peroxygen Systems, Inc. (PSi) is developing breakthrough technology for on-site hydrogen peroxide production. PSi’s on-site on-demand electrolyzer can reduce the cost of producing hydrogen peroxide by 50%, while also completely eliminating the cost and safety issues associated with shipping and handling of high concentration hydrogen peroxide. The challenge for PSi is scaling. To support the next step toward commercialization (customer pilot tests), scaling the prototype into larger single cells and 20-40 cell stacks is required. In addition to internal hardware and flow-field design efforts at PSi, NREL will address three critical problems for this scale-up effort: (1) demonstrating a large scale roll-to-roll (R2R) process to coat uniform electrode materials for 100 cm2 and 500 cm2 stack testing, (2) demonstrating an in-line diagnostic to achieve better electrode quality control, and (3) performing in situ cell/stack testing to better understand and optimize the performance of the flow field design.
Real-time measurements of food deformation are important for quality control in drying, yet they pose significant challenges. In this study, we developed an interference-based optical fiber strain sensor to enable in-line, continuous, below-the-surface strain measurements in drying of soft food samples. Compared to a strain resolution of 8 × 10 -4 at zero strain and 7.2 × 10 -3 at 0.20 strain reported in our previously published work, the present study achieves markedly improved resolutions of 1.3 × 10 -4 at zero strain and 7.8 × 10 -4 at 0.25 strain, which strains are the lower and higher boundaries of the dynamic range, respectively. This nearly order-of-magnitude improvement is attributed to the unique interference-based sensing mechanism, no need for calibration to convert optical signals to strain, and the system-design-enabled immunity to fiber-disk misalignments and light source intensity fluctuations. To demonstrate the in-line process monitoring, deformation measurements of fresh banana slices and sugar cookie doughs were carried out in a benchtop oven and an industrial-scale hot-air pilot dryer, respectively. In both dryers, strain measurements were continuously measured during the whole drying process at various depths and radii below the sample surfaces, with the strains up to 25%. Computer vision was used only in the benchtop drying to confirm the faithfulness of the fiber sensor measurements and cannot provide below-the-surface measurements. The measured spatiotemporal deformation allowed us to confirm the shell-hardening effect and to determine the speed and location of large deformation changes in the whole drying process, the latter of which is related to the sample cracking. To the best of the authors’ knowledge, this study is the first to report on an interferometry-based fiber sensor to measure food deformation. This sensor and the sensing mechanism have high potential for real-time process monitoring and control to prevent over-drying or cracking during drying processes.
Effective quality control (QC) for manufacturing proton exchange membrane water electrolysis (PEMWE) components is critical to enabling widespread adoption of the technology for hydrogen generation. This study investigates X-ray radiography as a novel, high-throughput, potentially in-line QC technique for detecting defects and assessing material property distributions in titanium-based porous transport layers (PTLs) which constitute a crucial component of low temperature PEMWE stacks. We obtain radiographs of a set of fifteen PTLs and model their absorbance of the broadband radiation as a second-order polynomial to account for the non-monoenergetic radiation source used in this study. The resulting model serves as a basis for predicting the areal density and porosity distributions of the PTLs. We find radiography successful in detecting multiple instances of defects, including holes/depressions, cracks, and excess material on the surface or in the pores of the material, demonstrating its potential as a robust in-line QC tool for PTL manufacturing.
Direct ink write (DIW) printing of reactive resins presents a unique challenge due to the time-dependent nature of the rheological and chemical properties of the ink. As a result, careful print optimization or process control is important to obtain consistent, high quality prints. The present invention uses a flow-through characterization cell for in situ chemical monitoring of a resin ink during DIW printing. Additionally, in-line extrusion force monitoring can be combined with off-line post inspection using machine vision. By combining in-line spectroscopy and force monitoring, it is possible to follow reaction kinetics (for example, curing of a reactive resin) and viscosity changes during printing, which can be used for a closed-loop process control. Additionally, the capability of machine vision to automatically identify and quantify print artifacts can be incorporated on the printing line to enable real-time, AI-assisted quality control of the printed products. Together, these techniques can form the building blocks of an optimized process control strategy when complex reactive ink must be used to produce printed hardware.
REBa 2 Cu 3 O 7-δ (REBCO, RE = rare earth) tapes with high critical current can be very impactful in high magnetic field applications at low temperatures and power applications at high temperatures. A pilot-scale Advanced Metal Organic Chemical Vapor Deposition (MOCVD) method was used to fabricate 50-m-long, 4+μm-thick REBCO tape in a single pass. Critical currents 3.3x that of commercial HTS tapes were achieved at 20 K, 12 T in these 50-m-long tapes. An in-line 2D X-ray Diffraction (XRD) system has been used to assess the quality of the long tapes in real-time, during manufacturing. The key peaks of REBCO, REO, and BZO phases were identified and utilized for tape quality analysis. Furthermore, a 20-m tape made by Advanced MOCVD was tested over its entire length by reel-to-reel (R2R) scanning Hall-probe microscopy (SHPM) at 65 K, 0.25 T, 2 T, and 4 T. 4-mm-wide strands of Advanced MOCVD tapes showed mean critical currents at 65 K of 530 A, 200 A, and 104 A at 0.25 T, 2 T, and 4 T respectively. The combined use of in-line and offline characterization techniques provides a reliable approach for assessing long REBCO tapes during manufacturing, serving as an effective feedback source for quality control in scaled-up REBCO tape deposition processes. This advancement contributes to the production of longer and more uniform high-performance REBCO tapes for large-scale, high-field superconducting applications
The Defense Logistics Agency is successfully incorporating Intelligent Processing Equipment (IPE) into each of its Manufacturing Technology thrust areas. Several IPE applications are addressed in the manufacturing of two 'soldier support' items: combat rations and military apparel. In combat rations, in-line sensors for food processing are being developed or modified from other industries. In addition, many process controls are being automated to achieve better quality and to gain higher use (soldier) acceptance. IPE applications in military apparel include: in-process quality controls for identification of sewing defects, use of robots in the manufacture of shirt collars, and automated handling of garments for pressing.
Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.
Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.
The problems associated with blending of the AMK additive with Jet A, and the base fuel effects on AMK properties are addressed. The results from the evaluation of some of the quality control techniques for AMK are presented. The principal conclusions of this investigation are: significant compositional differences for base fuel (Jet A) within the ASTM specification DI655; higher aromatic content of the base fuel was found to be beneficial for the polymer dissolution at ambient (20 C) temperature; using static mixer technology, the antimisting additive (FM-9) is in-line blended with Jet A, producing AMK which has adequate fire-protection properties 15 to 20 minutes after blending; degradability of freshly blended and equilibrated AMK indicated that maximum degradability is reached after adequate fire protection is obtained; the results of AMK degradability as measured by filter ratio, confirmed previous RAE data that power requirements to decade freshly blended AMK are significantly higher than equilibrated AMK; blending of the additive by using FM-9 concentrate in Jet A produces equilibrated AMK almost instantly; nephelometry offers a simple continuous monitoring capability and is used as a real time quality control device for AMK; and trajectory (jet thurst) and pressure drop tests are useful laboratory techniques for evaluating AMK quality.
Process control has been proven to be the most reliable means of safeguarding the quality of adhesive bonds according to Federal Aviation Administration (FAA). A method for implementing process control for reduction in risk in a bonded joint fabrication process is demonstrated in this study using a selected bonding system. The stepwise method included risk analysis to identify defects with the highest impact and likelihood to occur, evaluation of various pre-bond surface analysis tools to monitor for the selected defects, and demonstration of the benefits of in-process monitoring utilizing threshold limits determined from bond performance tests. The bonded system selected for investigation was an aerospace carbon fiber epoxy composite substrate surface prepared with random orbital sanding using 180 grit aluminum oxide sand paper. A series of portable, pre-bond surface analysis tools were investigated for their ability to be used for in-line bond process control. Results and threshold limits are presented from roughness, ballistic water contact angle (WCA), color, gloss, and Fourier transform infrared spectroscopy (FTIR) surface analysis tools. Results demonstrated how in-process inspection methods can be used to ensure quality of a surface preparation for a selected bonding system. A framework is provided for implementation of bond process control for robust bonding.
This project, funded by the U.S. Department of Energy (DOE) – Office of Fossil Energy under Award Number DE-FE0032177, aimed to assess the feasibility of an integrated Laser-Induced Breakdown Spectroscopy (LIBS) system with advanced machine learning (ML) models for real-time characterization and potential control of hydrogen gasifiers running on waste materials as feedstocks. This was a multidisciplinary effort that encompassed the acquisition and standardized analysis of individual and blended feedstocks—comprising biomass, coal waste, and plastic waste, followed by the development of a dynamic LIBS bench system for material sample analysis and development of predictive ML models. Comprehensive laboratory testing enabled the creation of a robust elemental dataset that served as the foundation for ML model training. Techniques such as Random Forest, Gradient Boosting, Support Vector Regression, and Neural Networks were employed to predict key feedstock properties, including higher heating value (HHV), moisture content, thermal conductivity, and ash composition with high accuracy. The results were validated against experimental data and demonstrated strong potential for real-time application in gasifier control systems. The project concluded with a study on the integration of the LIBS+ML approach for gasifier control and a techno-economic analysis of the implementation of the approach into hydrogen (H 2 ) gasification systems. Dissemination of results was carried out at a DOE meeting. This work establishes a scalable framework for automated, in-line feedstock quality assessment, offering significant implications for process optimization and emissions reduction in hydrogen production.
Carbon nanotubes (CNTs) have been studied extensively since their discovery and demonstrated at the nanoscale superior mechanical, electrical and thermal properties in comparison to micro and macro scale properties of conventional engineering materials. This combination of properties suggests their potential to enhance multi-functionality of composites in regions of primary structures on aerospace vehicles where lightweight materials with improved thermal and electrical conductivity are desirable. In this study, hybrid multifunctional polymer matrix composites were fabricated by interleaving layers of CNT sheets into Hexcel® IM7/8552 prepreg, a well-characterized toughened epoxy carbon fiber reinforced polymer (CFRP) composite. The resin content of these interleaved CNT sheets, as well as ply stacking location were varied to determine the effects on the electrical, thermal, and mechanical performance of the composites. The direct-current electrical conductivity of the hybrid CNT composites was characterized by in-line and Montgomery four-probe methods. For [0](sub 20) laminates containing a single layer of CNT sheet between each ply of IM7/8552, in-plane electrical conductivity of the hybrid laminate increased significantly, while in-plane thermal conductivity increased only slightly in comparison to the control IM7/8552 laminates. Photo-microscopy and short beam shear (SBS) strength tests were used to characterize the consolidation quality of the fabricated laminates. Hybrid panels fabricated without any pretreatment of the CNT sheets resulted in a SBS strength reduction of 70 percent. Aligning the tubes and pre-infusing the CNT sheets with resin significantly improved the SBS strength of the hybrid composite To determine the cause of this performance reduction, Mode I and Mode II fracture toughness of the CNT sheet to CFRP interface was characterized by double cantilever beam (DCB) and end notch flexure (ENF) testing, respectively. Results are compared to the control IM7/8552 laminate.