EWI Welcomes Essentium to Membership

EWI is pleased to welcome Essentium to membership. The company manufactures and delivers industrial 3D printers and materials offering a 3D printing platform that can be applied in prosthetic sockets, orthotic devices, low temp jigs and fixtures, rapid prototyping, and castings. Essentium serves the aerospace, automotive, consumer goods, contract manufacturing, and biomedical industries and is headquartered in Texas with locations in the Asia Pacific and Europe.

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New EWI Research to Be Presented at June Webinars

EWI’s June 2021 webinar program will feature recently published research findings based on three internal R&D projects:

All EWI webinars are free, but advanced registration is required.

To download the papers associated with these upcoming events in advance of the webinars, click on the images below:

A Solution for Laser Beam Quality Analysis in L-PBF AM Production

Co-processing of HLAW & SAW to Enhance Productivity in High Strength Steel Joints

Detection of Kissing Bond in Solid-state Welds with Full Matrix Capture and Total Focusing Method (FMC/TFM)

To learn more about EWI webinars, click here or contact [email protected].

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Developing a New Model to Predict AM Part Performance

In additive manufacturing (AM), post-processing steps are just as crucial to the success of the final product as the build itself. That’s why both the build process and the post process need to be taken into account in models that predict AM part performance.

A new performance prediction tool for AM-built parts has been developed by the Additive Manufacturing Consortium (operated by EWI). This work is described in a paper by EWI associates Alex Kitt, Luke Mohr, and Arushi Dhakad, Predicting AM Part Performance. Discussion covers work done on titanium alloys and proposes further work to apply the model to other metal alloys and post-build processes.

You are invited to download this paper now – at no charge – by submitting the form on this page.

If you want to learn more about this work or are interested in participating in subsequent studies on the topic, contact Alex Kitt, Director of Data Science, at [email protected].

Complete this form to download the paper:

To view the paper, please submit the form above.

Want to contact an EWI expert about a project? Call 614.688.5152 or click here.

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Produce Reliable Products with a Documentable Polymer Selection Process

Produce Reliable Products with a Documentable Polymer Selection Process

by Jeff Ellis

Material selection is an important part of every design cycle because material failures eventually cause product failures. Of course, every company wants to avoid product failures in the marketplace. To do so, material properties as a function of their service life and surrounding environments must be considered. Rigorous material selection follows a process to incorporate these considerations and outputs traceable documentation that details the reasoning that supports the decisions.

The process implements the following tasks to incorporate a systematic approach to material selection:

Drafting a Requirements Document

The requirements document includes the known conditions that the material will encounter during its lifecycle. For a polymer material, this includes — but is not limited to — resin at the material supplier, shipping to molder, molding conditions, shipping molded parts to assembler, assembly stresses, shipping product to customer, customer use, and disposal. Through its life, the polymer could encounter environments that are hot, cold, humid, dry, high stress, oxidative, and ultraviolet irradiative, as well as others. The material must continue to pass all the requirements through these environments to allow the product to function. The list below contains some of the conditions to consider when drafting the requirements document.

  • Mechanical properties
  • Thermal properties
  • Chemical compatibility
  • Aesthetics
  • Processing
  • Long term stability
  • Barrier properties
  • Price, availability, and global supply

Each requirement should have a pass/fail limit associated with it. An example is a ketchup bottle must survive a 20 lb. hand squeeze during use at typical indoor temperatures. This requirement includes both mechanical and thermal requirements. This quantifiable metric that can be translated into selection criteria in the next section.

Translating Material Selection Criteria

Once the document is finalized, then each of the requirements can be translated into a selection criterion for the material. For ketchup bottle example, depending on the design, it could translate to a Young’s modulus of at least 1 GPa, a flexural strength of at least 50 MPa, and an elongation at yield of at least 4% at a temperature range from 5 to 50°C. This translation is not trivial and requires a fundamental understanding of material science, but if done properly, it is highly valuable to mitigating risk of product failure.

The translated quantifiable metrics can be used for selection criteria. They are material properties that are typically listed on polymer datasheets and can be used to down-select material grades. Once all selection criteria have been translated from the requirements, then the initial selection of materials can commence. 

Selecting a Polymer Family

There are too many specific polymer grades to search all of them for a product. Therefore, during the initial selection of materials, polymer families (e.g., PET versus PC) are compared against the selection criteria. The material properties that exceed the selection criteria metrics are scored high, while those that do not score low. The scores are summed in a table, and generic polymer grades that meet the most criteria are ranked highest. Many times, there is not a single material family that satisfies all the selection criteria. In these cases, weighting of the criteria is necessary. For example, the mechanical properties may be weighted higher than the aesthetic properties because the product must not break when squeezed, but being a certain color is only a nice to have. Weighted or not, the highest-ranking material families will be best suited to producing the lowest-risk product.

Finalizing a Specific Material Grade

During final material selection, the highest-ranking families from the previous task are used to inform specific grade selection. For example, if PET was found to be the highest-ranking family, then PET grades from different resin suppliers would be considered for use. Their technical datasheets are used to ensure that the specific grade meets the selection criteria. Just as in selecting a material family, a ranking table is populated and used to determine the best material for the product.

Many times, the technical datasheet does not have enough information to inform every one of the pass/fail criteria. In this case, it is important to test for the unknown values. For example, a proprietary chemical formulation (e.g., new flavored ketchup) will contact the material being selected, but there is no chemical resistance data for this binary interaction. It is important to understand the chemical effects the liquid has on the material. To gather data, an Environmental Stress Cracking (ESC) test could be performed to fill the void in the data. Tests should be run on the top material candidates to fill all data voids before finalizing a resin for production.


The material selection process described here has been used by many product design teams field robust products. The output of the process provides detailed documentation to why material decisions were made, which is valuable for future product design teams working on product upgrades, new products, or sourcing alternative materials due to supply chain issues. Investing in a rigorous material selection process pays for itself by creating referenceable selection documentation and producing reliable products.

If you would like to learn more about polymer material selection, contact Jeff Ellis at [email protected] or 614.688.5114.

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Additive Manufacturing: What Can It Do for You?

By Henry Cialone
President & CEO, EWI

Additive manufacturing. Though many manufacturers have heard this term, the phrase may not be well or widely understood. I’d like to change that. So let’s talk about what additive manufacturing (AM) is and what it can do.

What Is AM?

When we think of “manufacturing,” we typically think of traditional manufacturing (molding, casting, etc.), which is understandable, as it’s been the standard pretty much since the beginning of time. Traditional manufacturing starts with a piece of material that is then refined by removing bits and pieces through milling, turning, drilling — machining in one form or another — to achieve the desired form. This form of manufacturing is, technically speaking, subtractive manufacturing.

Additive manufacturing takes the opposite approach….

To read the full article, click here.

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In-situ Process Monitoring: Making AM Smarter

In-situ process monitoring has been around for a while in most manufacturing industries – from the trained eye of a supervisor to an automated manufacturing execution system. Additive manufacturing (AM) is no different; it uses sensors to provide both a better understanding of the process and valuable data that can be analyzed to infer the quality of the just-printed component.

AM processes like laser powder bed fusion (L-PBF) and directed energy deposition (DED) are very similar to welding. At their core, they are repetitive materials joining actions where a final component is built upon successive welds.

Figure 1: LPBF can be viewed as a
highly repetitive welding process.

The number of stochastic process defects that can occur in AM processes like L-PBF are high simply because the same unit welding process is being repeated over thousands of layers, leading to miles and miles of weld material per part! This increases the inspection burden of an AM component, and currently presents a serious bottleneck to qualifying and certifying printed parts.

Based on in-situ data generated during the printing process, just-printed parts can be screened and sorted into categories based on the level of inspection required. For example, there may be some parts that need detailed inspections, and those which are clearly defective and not worth inspecting. Furthermore, regions of interest in the part can be identified based on the in-situ process data and subsequently prioritized during high-resolution CT inspection. At the end of the day, the objective is to reduce the overall inspection burden, and improve yield – saving time, money, and energy.

EWI’s open architecture system (OAS) is a customizable L-PBF test platform which offers complete access to the hardware, optics, and software overlays. As a result, the OAS offers unparalleled control of the path planning variables, optical hardware, motion control digital input for triggering and integration, on and off-axis ports for sensor integration and multiple sensor-mounting ports within the process chamber. Using the OAS, EWI has executed numerous government and commercially funded programs to correlate in-situ process monitoring data with known quality metrics, and demonstrate closed-loop process control.

Having multiple high-fidelity sensors simultaneously recording data is a unique capability that presents multiple challenges. Some key questions are:  

  • Which sensors really matter for my application and quality objectives?
  • What methods and techniques can I use to analyze the data?
  • Can I handle all the data being generated?
  • How do I combine the various sensor data-streams together?

The short answer is: it depends.

It is important to treat each sensor as a unique lens through which a specific aspect of the process can be understood. So far, a one-size-fits-all sensor does not exist. For example, optical cameras may be great to observe issues with recoating but fall short when information about the thermal history of the part is required. So, it is important to map sensors to the defect types they can detect and rationalize their use within the context of the specific part and the quality requirements. Figure 2 shows how sensors and data processing methods can be linked to detect a specific defect.

Figure 2: Sensors mapped to defects

EWI is currently collaborating with the University of Nebraska-Lincoln (UNL) to develop techniques to collect, curate, and analyze multiple sensor-streams of data. The first set of builds as part of the recent project involve collecting in-situ data from the following sensors:

  • Hall-effect sensor mounted on the recoater drive motor
  • Recoater-mounted laser profilometer to monitor out-of-plane distortion
  • Stratonics two-color pyrometer for accurate melt-pool temperature readings
  • Thermal imaging camera for inter-layer temperature data

EWI and UNL will present additional updates and results from this project at ICAM 2021. To discuss implementing in-situ process monitoring in your AM setup, contact Ajay Krishnan directly at [email protected].

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EWI Presents Two Webinars on Large-scale DED in May

This month, EWI will offer a two-part webinar series on the topic of large-scale directed energy deposition (DED):

Both of these free webinars will be 45 minutes and include Q&A time at the end.

While there is no charge for either event, registration is required in advance. To sign up for one or both webinars, click below:

For more information about these and other EWI webinars, contact Michelle Bulan at [email protected].

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