Are you seeking an automated solution to assess quality, reliability, and safety in your manufacturing operation?
EWI recently worked with materialsIN to develop a real-time quality assurance system for ultrasonically welded, lithium-ion battery tabs. In a new paper, A Fully Automated Quality Assurance Solution for Battery Tab Welds Using Process Monitoring and AI, EWI engineers Alex Kitt and Zach Corey describe how EWI recently combined sensor selection, AI, and intelligent training data generation to create an elegant inspection and verification system.
You are invited to download this paper for FREE by completing the form on this page.
To learn more about quality assurance solutions developed by EWI, contact Alex Kitt at [email protected].
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Integrated computational materials engineering (ICME) uses analytical and numerical tools to create predictive models. Recently, EWI has published a series of papers examining analytical methods that work well in assessing thermal/mechanical responses in specific joining processes.
Some highly complex joining applications, however, require advanced computational tools such as finite element analysis (FEA) to achieve desired accuracy. EWI has successfully used FEA to predict thermal behavior in different joining applications including two specific cases: 1) thermal excursions in battery module electrical interconnects, and 2) residual stress and distortion in arc welding processes.
You can read about this work in Thermal Analysis Using Finite Element Methods, written by EWI Project Engineer Amin Moghaddas. Simply complete the form on this page to download the paper for FREE.
Staying competitive in large-structure production is vital for American industries today. The Accelerating Production of Large Structures and Systems (RAPLSS) survey initiative, sponsored by NIST Manufacturing USA and managed by EWI, aims to streamline this process.
The Need for Large Structure Production 4.0 Technologies
US industries must adopt leading-edge technologies for large structure production 4.0 to stay competitive globally. The RAPLSS roadmap takes a three-fold approach to reach its objectives:
Development of Large Structure 4.0 Technologies: This involves using automation, data analytics, and IoT to optimize large structure production processes.
Next-Generation Manufacturing, Fabrication, & Inspection Processes: Innovations in materials and techniques are crucial for improving production efficiency and reducing costs.
Advanced Training Programs: Preparing the workforce for the future is a priority. The roadmap focuses on creating training programs to equip individuals with skills needed for modern manufacturing.
The RAPLSS initiative aims to identify specific industry gaps and needs to enhance American manufacturing in large structures and systems. By prioritizing innovation, efficiency, and workforce development, it aims to position the U.S. as a leader in this field. However, achieving this vision requires industry-wide participation from experts, manufacturers, and researchers. Please take NIST’s 5-minute survey to share your insights and participate in accelerating production in this critical sector. Questions about this survey? Contact Larry Brown, Senior Project Manager/Technical Advisor, Government Programs at[email protected].
EWI is pleased to welcome Commonwealth Fusion Systems (CFS) to membership. The company designs and builds fusion energy systems to achieve its mission of delivering clean, limitless fusion power to the world.
EWI has recently developed a new technique to obtain improved accuracy in material flow stress for modeling metal forming processes.
The method uses the hydraulic bulge test (HBT) and digital image correlation (DIC) analysis and applies the plastic work equivalency principle to determine an optimal arc length for the for calculation of a biaxial flow curve. It yields a maximum uniform equivalent strain that is twice the maximum uniform strain that could be obtained in the past, thus offering improved accuracy.
EWI associates Amir Asgharzadeh and Laura Zoller have written Advanced DIC Post-processing Technique to Analyze Biaxial Bulge Test to describe this work. You are invited to download this paper FOR FREE by completing the form on this page.
To discuss this technique or other forming tests offered by EWI, contact Laura Zoller at [email protected].
Have you seen the new process known as tele-welding? Have you ever TRIED it? You’ll have your chance when you visit the CWB-EWI booths at FABTECH 2023, September 11-14 in Chicago.
The setup for a tele-welding workstation
Come drive the cutting-edge robotic system that can perform welding, gouging, cutting, and inspection from remote locations. You’ll get the full experience of live video visuals, haptic feedback, and real-time operation as you lay down high-quality gas metal arc welds using EWI’s unique tele-manufacturing technology. The remote execution of your work will take place LIVE in a booth located across the expo hall. EWI associates will be on hand at both booths to guide you through the process and answer your questions.
To watch the control system at work – or operate it yourself – visit FABTECH Exhibit Booth #16033. The remote welding robot will be set up in a separate location several rows away at Booth #37003. We invite you to check out both ends of the operation while you visit the show.
Tele-manufacturing is just one of many innovations that EWI has to share at FABTECH. You can learn about our broad capabilities, services, and innovations when you stop by our exhibit. In addition, EWI associates will be giving ten presentations on recent R&D as part of the AWS Professional Program. For a complete list of speakers, visit ewi.org/ewi-event/fabtech-2023.
Common aerospace materials such as high-nickel alloys (such as René 80) or high-strength aluminum alloys (6000 and 7000 series) are not currently manufactured using additive manufacturing (AM) due to hot cracking. As part of a $5M Department of Energy program, EWI, GE Research, and the University of South Carolina developed a model to predict and avoid hot cracking in René 80. Using additive manufacturing for high performance metal alloys enables design flexibility and the introduction of previously unbuildable parts.
When the measured crack formation for a set of René 80 single-pass walls is plotted against the velocity of the solidification front, a clear activation behavior is observed. This is consistent with the physics-based crack formation criteria and provides a mechanism for predicting and avoiding crack formation.
Our probabilistic crack formation model is an ICME based Process->Thermal->Structure model. The steps in this model is described below:
Process > Thermals: Process parameters and part geometry are used to predict the thermal history experienced by the René 80. This is done using a multi-source Rosenthal model. Rosenthal models are one example of computationally efficient analytic methods EWI often uses for thermal history prediction. In this case, we calibrate the model using in-situ thermal sensing.
Thermal > Structure: A hot cracking criteria is used to relate the predicted thermal histories to crack formation. In this case, we use a simplified version of the physics based RDG crack formation criteria. This model considers the fluid flow between dendrite arms during solidification.
The Process > Thermal > Structure model allows crack formation predictions to be made in seconds and gave consistently good results across multiple builds. Further, the model can be used in a feedback loop to select process parameters, modify design, and modify build strategy to minimize or eliminates cracking.
This specific approach was developed for René 80 but is applicable for other hot-cracking prone alloys.
For a full explanation of the development of this model, you can view our recently published paper in ADDITIVE MANUFACTURING, Physics-based Crack Formation Model for René 80 in laser Blown Direceted Energy Deposition: Theory and Experiment, by filling out the form below:
To discuss this work and how it might be applicable for your application, contact Alex Kitt at [email protected] or Matt Dodds at [email protected].
This work was supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office Award Number DE-EE0009118
