Mark Barfoot Mark Barfoot
Director of AM Programs

Working Toward a Full AM-Data Ecosystem

November 11, 2020

Knowledge tends to grow exponentially, and a large base of knowledge acts as a strong foundation on which to build. The fastest way to accelerate this kind of growth is to facilitate the ability to share information, a principle demonstrated effectively with the growth and advancement of the internet.

Additive manufacturing’s (AM’s) many processes have been developed in a digital world and generate a large volume of data, including part-design files, build parameters and monitoring information, inspection and characterization results, and more. Ideally, we can leverage this data to create predictive models that improve these processes, and the cycle can begin again: collect data, improve, repeat. 

This vision, however, can prove difficult to achieve without a well-developed data ecosystem, and currently the AM community lacks a standard approach to gather, manage and share data. But, recent efforts by the ASTM Additive Manufacturing Center of Excellence (AM CoE) have been made to identify gaps in the data ecosystem, and actions needed to address them. Earlier this year, AM CoE, in its Strategic Guide: Additive Manufacturing Data Management and Schema, outlined three key action items in a plan to develop an AM data ecosystem:

  • Develop a common data dictionary (CDD) to set consistent terms and relationships for AM data.
  • Develop a common data-exchange format (CDEF) to enable robust data sharing. 
  • Automate data acquisition, for affordable, fast and accurate data capture.

EWI has been part of a community effort to push these initiatives forward, and we summarize the status of each initiative here.

Common Data Dictionary

Data elements in the common data dictionary are organized into the buckets as shown on top.

The AM Data Management Working Group (AMDMWG), led by Yan Lu at the National Institute of Standards and Technology, has begun to investigate and address the shortcomings of the AM data ecosystem. Early on, the group identified a basic problem limiting the ability to share data. That is, when it comes to data, everyone does not speak the same language. Many organizations use different terms for the same concept or may use different definitions for the same term. 

The AMDMWG is continuously developing the CDD to establish common terminology, definitions and relationships for AM data elements across every aspect of the AM lifecycle (see the accompanying figure). Experts across industry, academia and the government have developed and reviewed the CDD. 

EWI, the Penn State University Applied Research Laboratory and NIST partnered on an ASTM-funded effort to create a standard based on the CDD. A new ASTM standard work item, WK72172, is in the balloting process and represents the first step toward comprehensive standardization of AM data terminology and, thus, the first step toward robust data-sharing capabilities across organizations.

Common Data-Exchange Format

Once everyone begins to speak the same AM-data language, organizations need a simple way to share their data. Currently, the way data are stored varies greatly depending on whom you talk to; some organizations use commercially available data-management solutions, while others have developed their own solutions. With the CDEF, the AM CoE does not intend to upend current systems but rather to establish a common format as a middle ground. This includes common file formats for storing data, as well as establishing data types, definitions and relationships based on the standards established in the CDD. By establishing a CDEF, organizations will be able to share data easily by translating it from their current management systems to the CDEF, and vice-versa. 

The AM CoE has identified as a major roadblock the amount of time required to parse data from different systems and expects the CDEF to alleviate this all-too-common issue. EWI has begun to actively pursue opportunities with other industry leaders to push this effort forward.

Automated Data Acquisition

Data pedigree is a key requirement for a mature data ecosystem. To be valuable, data must be believable, accurate and error-free. One way to improve data accuracy: removing human error in data entry as much as possible. Too often, data entry occurs by transferring data from one system to another via USB for manual entry into a spreadsheet. 

Recognizing that this manual process can create many potential entry points for mistakes, EWI has built a semi-automated internal website for uploading data to make the data-acquisition process as straightforward and error-free as possible. The website reduces human error by utilizing QR scanning to enter values (where possible, such as for operator names and sample or part IDs). The operator also points to machine-generated log or output files, which automatically are parsed and imported to the database. This setup also allows for effective management of powder-inventory information in the database throughout the full AM-process lifecycle.

As a next step in this effort, EWI plans to develop a fully automated data-importing solution and accompanying standards that establish best practices for this process. With this resource, the AM community will gain more confidence in the correctness of its data. Together with the CDD and CDEF, automated data acquisition will support the sharing of quality data between organizations, greatly accelerating AM growth and understanding.

A Promising Start

The actions outlined above represent a key step and a promising start toward developing a robust AM-data ecosystem. Our shared vision is to enable the quick and secure sharing of AM data between organizations, greatly accelerating the rate at which knowledge is processed and gained, and ultimately leading to more efficient processes across AM. This has been, and will continue to be, a true community effort, requiring the input and consensus of collaborators with a variety of expertise. For information on how to get involved, e-mail Luke Mohr, 3DMP 

Mark Barfoot thanks EWI applications engineer Luke Mohr for preparing this issue’s AM Insights column. Mohr develops schema for the AM database at EWI’s Buffalo Manufacturing Works. He provides technical and strategic support in data science and management and in x-ray CT algorithm development. Utilizing his background in dynamical systems and probability, he applies his subject expertise in determining the need for big data analysis and NDE in AM development and standardization. Note: Any reference to specific equipment and/or materials is for informational purposes only. Any reference made to a specific product does not constitute or imply an endorsement by EWI of the product, or its producer or provider.
Industry-Related Terms: Center, E-Mail
View Glossary of Metalforming Terms


See also: EWI



Must be logged in to post a comment.
There are no comments posted.

Subscribe to the Newsletter

Start receiving newsletters.