Enabling additive manufacturing part inspection of digital twins via collaborative virtual reality
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Virtual Inspection of Advanced Manufacturing via Process-Scale Digital Twins (Abbreviated Report)
Inspection and certification comprise the most significant bottlenecks in advanced manufacturing for NNSA applications, often requiring far more time and resources than the fabrication of the parts themselves. Traditional methods, such as manual review and X-ray computed tomography, are not only slow and costly, but also struggle to provide a clear connection between manufacturing instructions and the final performance of critical components. This gap limits both the agility and assurance needed to support the modernization and safety of the United States nuclear stockpile. In response, our Strategic Initiative established a digital twin framework that integrates realtime process monitoring, automated data analysis, and immersive virtual reality collaboration into a unified inspection pipeline. By leveraging data from sensors, machine instructions, and imaging, we created high-fidelity virtual models of manufactured parts that could be rapidly analyzed and certified. This approach was first demonstrated with Direct Ink Write, and then extended to other manufacturing settings, including conventional (or “subtractive”) manufacturing and to predict the end of life performance of parts per the aging and lifetimes programs. The result is a transformational capability: inspection times have been reduced by a factor of 120,000 without loss of accuracy and while simultaneously improving traceability and confidence in part quality. This framework not only streamlines certification for critical applications, but also positions the national security enterprise to respond more flexibly to emerging challenges, supporting agile manufacturing and digital engineering practices across a broad range of mission-relevant domains.
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Myna: Connecting powder bed fusion build data to simulation tools for digital twin applications
Additive manufacturing (AM), as a digital process, can generate a detailed digital thread linking a part’s design and manufacturing to its operational performance. As AM systems advance, an increasing amount of process data is stored in manufacturing databases. In principle, this data can be utilized by simulation-based digital twin approaches, such as real-time process control and asynchronous post-processing guidance. However, few tools currently exist for systematically integrating digital thread data with computational tools. Here, in this study, we propose a software package, called Myna, for connecting data from powder bed fusion processes to simulation tools. The utility of such a platform is demonstrated using build data from the Oak Ridge National Laboratory Manufacturing Demonstration Facility “Peregrine v2023-10” public dataset to automatically configure and run 54 semi-analytical 3DThesis melt pool simulations, 78 numerical Additive FOAM melt pool simulations, and 3 ExaCA microstructure simulations. The simulated, spatially registered microstructures are then compared directly with electron backscatter diffraction characterization of the corresponding as-built part locations. The resulting simulated microstructure showed variation as a function of process parameters, particularly stripe width; however, the experimental data had little variation between the microstructure texture and grain size resulting from different processing conditions. Analysis of the discrepancies suggest that it is possible a two-phase ferritic-austenitic solidification model is needed to accurately predict grain size and texture for certain stainless steel 316L feedstock compositions under powder bed fusion conditions, providing direction for future research. As illustrated here, due to the number and complexity of the simulations involved in AM process-structure–property predictions, automated methods to connect process data and simulations will remain necessary tools for testing hypotheses and implementing digital twin applications.
Knapp, Gerald L. [Oak Ridge National Laboratory (O↗
Digital light processing of porous LLZTO scaffolds for Li-garnet solid-state batteries
Li 7 La 3 Zr 2 O 12 (LLZO)-based solid-state electrolytes (SEs) are promising materials for next-generation solid-state batteries. In this work, digital light processing (DLP), an emerging additive manufacturing technology, is employed to produce porous Ta-doped LLZO (LLZTO) scaffolds. The self-standing scaffolds are 100 μm thick and have 40% porosity. The scaffolds demonstrate symmetric cell cycling stability exceeding 1,500 h at 0.1 mA/cm2 current density, with a capacity of 0.1 mAh/cm 2 (1 h for each half cycle). At higher current densities, reversible soft shorts frequently happen, while immediate hard shorts are prevented due to Li dendrite growth being hindered by the tortuous pore network. In addition to the cycling stability, the phase stability of LLZTO is investigated during the post-printing thermal process for printing resin removal. We discovered that the LLZTO partially decomposes into Li 2 Zr 2 O 7 and other impurity phases from 400°C to 800°C, but the pure LLZTO phase is restored upon the completion of resin removal beyond 800°C.
Li-metal anode↗
Developing Digital Twins of Additively Manufactured Components
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Engineering, Materials science, Chemistry↗
Developing Digital Twins of Additively Manufactured Components
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Engineering, Materials science, Chemistry↗
Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective
We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass and renewable feedstocks, in general, is to produce new molecules and products with unmatched properties that have no analog in traditional refineries. Discovering such performance-advantaged molecules and the paths and processes to make them rapidly and systematically can transform manufacturing practices. Furthermore, we discuss retrosynthetic approaches, text mining, natural language processing, and modern machine learning methods to enable digitalization. Laboratory and multiscale computation automation via active learning are crucial to complement existing literature and expedite discovery and valuable data collection without a human in the loop. Such data can help process simulation and optimization select the most promising processes and molecules according to economic, environmental, and societal metrics. We propose the close integration between bench and process scale models and data to exploit the low dimensionality of the data and transform the manufacturing for renewable feedstocks.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Digital Twins- An Imitation of Life
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manufacturing↗
Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS)
Opportunities exist for realizing transformative advances in productivity and reductions in energy footprint through ubiquitous sensing in manufacturing environments. Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS) is a 21-month (4 academic semesters, plus one summer) experience for graduate students that focuses on scaling the knowledge, understanding and leadership skills in the cyber manufacturing area. Masters students (8/year, 32 total) complete 2-year projects on industrially-driven project topics, rotating to internships in summer semester to work on scoping and implementation at project partners. Students complete academic training in embedded systems, process modeling, data science, and cloud-based systems design. Their projects are targeted toward sensor retrofit, process monitoring, root cause analysis, and sensor fusion.
Advanced Manufacturing↗
A Case Study: Using Delmia at Kennedy Space Center to Support NASA's Constellation Program
The presentation examines the use of Delmia (Digital Enterprise Lean Manufacturing Interactive Application) for digital simulation in NASA's Constellation Program. Topics include an overview of the Kennedy Space Center (KSC) Design Visualization Group tasks, NASA's Constellation Program, Ares 1 ground processing preliminary design review, and challenges and how Delmia is used at KSC, Challenges include dealing with large data sets, creating and maintaining KSC's infrastructure, gathering customer requirements and meeting objectives, creating life-like simulations, and providing quick turn-around on varied products,
Kickbusch, Tracey↗
An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants
The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. Here, in this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.
59 BASIC BIOLOGICAL SCIENCES↗
A Digital Twin Approach to Study Additive Manufacturing Processing Using Embedded Optical Fiber Sensors and Numerical Modeling
One of the major challenges for metal-powder-based additive manufacturing is measuring and mitigating residual strain induced during the manufacturing processes. This article reports distributed fiber optic sensors embedded in Inconel alloy components as experimental means to validate numerical models of additive manufacturing process. Electroplating was used to deposit a metal protective jacket onto standard telecom single-mode fibers for strain measurements, Fiber sensors were embedded in an Inconel alloy substrate using the laser engineered net shaping (LENS) process. Here by using a Rayleigh-scattering optical frequency domain reflectometer (OFDR), temperature changes, and residual strain in the metal substrate were monitored with 5 mm spatial resolution during the LENS process. Using finite element analysis, temperature and strain profiles induced by the LENS deposition processes were also numerically studied. Discrepancies between the simulated temperature and strain profiles and those measured directly were less than 10%. Results presented in this article demonstrates a digital twin approach to fuse modeling results with distributed fiber sensor measurement data to study additive manufacturing process toward design and fabrication process optimization.
36 MATERIALS SCIENCE↗
Low-cost sensors and analytics for a digital factory
ORNL Manufacturing Demonstration Facility and Perisense worked on a low-cost solution for machining process monitoring to enable a digital factory. Enabling machine connectivity for digital factors is expensive for many small to medium manufacturers. The hypothesis for the research project is that a lowcost sensor suite, which can be retrofitted to a legacy machine tool or a new machine tool, can effectively provide real-time, in-situ sensing for manufacturing processes, which can enable improvements in process performance and efficiency. The Perisense sensor node was installed on the Haas TM1 machine with a current sensor and an accelerometer. Cutting tests were performed at different process parameters and sensor data was recorded. Results show a correlation of the current sensor to the spindle load. The accelerometer, mounted on the machine base, showed a correlation to the spindle speed as well as the spindle load. The sensor data can be used to monitor the machining process and provide insights on the machine spindle load utilization.
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3-D Printed Electro-Magnetic Objects for THz Communications
Advanced materials and manufacturing techniques will pave the path towards high quality electro-magnetic circuits that operate up through THz and into optics regime. Digital additive manufacturing (AM) techniques offer game-changing potential for large scale manufacturing of Radio-frequency (RF) circuits and system in custom 2D/3D geometries. The technical collaboration will explore the design flexibility and product customization capabilities of additive manufacturing techniques to process magnetic materials in custom 3D geometries with tailored dielectric properties and frequency response characteristics. Transformation of an RF circuit design idea into a 3D printed system using additive manufacturing techniques will bring together the elements of advanced materials, additive integration, and low-cost manufacturing for future advanced wireless communication systems.
36 MATERIALS SCIENCE↗
Operational resilience of additively manufactured parts to stealthy cyberphysical attacks using geometric and process digital twins
Cyberphysical attacks on the digital backbone of Additive Manufacturing (AM) can compromise the printed part’s functionality. They can alter features in the digital geometry to introduce geometric defects (e.g., missing fillets) or alter process parameters to create local defects (e.g., voids). Addressing the downtime, waste, and quality deterioration associated with existing solutions requires operational resilience, i.e., rapid elimination or disruption of defect formation (to retain part function) without production stoppage or part disposal (to retain yield). This need is unmet due to the inherently unpredictable nature of attack-induced alterations, lack of access to the original geometric model for identification of altered geometric features, and in-process imposition of unknown process dynamics via attack-driven alteration of real-time-uncontrolled (or exogenous) parameters. This work establishes the above-mentioned operational resilience for the first time by creating two Digital Twins (DT). The Geometric DT (Geo-DT) is based on a unique physical-field-driven soft sensor and topology optimization method. The Process Digital Twin (Pro-DT) combines local defect quantification with a novel Reinforcement Learning formulation and training method. The importance of these methodological advances and the scalability of our approach are examined on a real AM testbed. It is shown that Geo-DT can correct geometric defects without access to the original digital geometry or explicit knowledge of attack-altered geometric features. Further, Pro-DT can accelerate real-time disruption of local defects despite attack-driven imposition of unknown process dynamics. We discuss how our framework goes beyond the contemporary focus on pre-attack security and in-attack detection towards resilience for AM and beyond.
Additive Manufacturing↗
On the additive manufacturing (3D printing) of viscoelastic materials and flow behavior: From composites to food manufacturing
Additive manufacturing (AM) or more commonly known as 3D-printing has gained much ground in industrial manufacturing because of its breakthrough in rapid prototyping and processes. To date, the 3D-printing market has grown significantly and is poised for growth in line with digitization of manufacturing. Furthermore, the main objective of this review article focuses on the viscoelastic behavior (viscous solution) of 3D printing materials at ambient conditions, which are not essentially under melt-flow or higher temperatures. The initial section reviews the basic concepts on viscosity and viscoelastic phenomenon, with emphasis on the preferred conditions a viscous material should have prior to 3D-printing. Several examples of structures formed from viscoelastic materials through 3D-printing are also detailed, as well as the various curing techniques for different printed objects or models. While there is much interest on industrial applications, the last part surveys various food formulations used for 3D-printing.
36 MATERIALS SCIENCE↗
Digital fabrication of a small diameter polymer optical waveguide
A novel polymer optical waveguide and method of manufacturing is presented herein. A digitally manufactured process is described which utilizes a micro-dispensed UV optical adhesive as the contour guiding cladding, a fused deposition modeling technology for creating a core, and a subtractive laser process to finish the two ends of the optical interconnect. The optical waveguide can be printed directly on a circuit board in some embodiments. Alternatively, using a slightly modified process including a step to bond the optical fiber to the substrate, the optical interconnect can be manufactured on a flexible substrate.
Bhethanabotla, Venkat Rama↗