Learning Objectives
By the end of this section, students will be able to:
- Describe the importance and impact of quality assurance on the additive manufacturing digital thread.
- Identify post-processing steps and decisions that impact the additive manufacturing digital thread.
- Discuss the was in which industry and related standards and external requirements impact the digital thread.
One of the most exciting opportunities for the implementation of the digital thread into real world production applications in the 3D printing sector is for quality assurance. A digital data approach, as described above, can provide a deeper perspective on the end-to-end process of part development, which quality assurance personnel and software could incorporate into their efforts.
With this opportunity, there also challenges, such as the following:
- Many variables, no consistent framework. Currently there are over 100 variables that need to be resolved, documented and stored for each production AM Part.
- No centralized data source. Companies are having to build bespoke tools to track and store this information or are allowing teams to stand up siloed solutions, which may or may not map to industry standards.
- Knowledge transfer and data retention is fragmented. Teams warehouse their own data and documentation is inconsistent – when people leave, so does the information.
If we examine the full 3D printing workflow, we see that there are a number of entries that should be documented in order to maintain a traceability record on each component.
| Design | Material | Process | Post-Process | Inspection |
|---|---|---|---|---|
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Organizations seek to address this issue and keep track of these variables. For example, TRACEam is a software tool that allows users to document and input all their production 3D printing data into a single platform, tied directly to the machine workflow that is most relevant.
One of the greatest challenges with quality management for additive manufacturing approaches is the fact that each individual user of the technology may need or want to be presented with different data. This could be driven by a specific printer but also the user’s industry. For instance, the qualification requirements for the medical device industry differ greatly from that of aerospace or automotive. There are a handful of standards that are listed in the literature that provide a benchmark for these data sets and many can be found in the documents listed below:
| Standard | Primary Focus |
|---|---|
| ANSI/AMSC | Roadmap for AM standards |
| ASTM Material Test Standards | Numerous standards materials testing and characterization |
| ASTM F42 | AM specific testing procedures for limited subjects |
| FAA | Memo released covering the Engineering Considerations for Powder Bed Fusion Additively Manufactured Parts |
| SAE AMS 9001 | Ni Base 625 Alloy for Add Mfg Machines |
| FDA | Technical Considerations for Additive Manufactured Medical Devices |
| NASA | Em20 MSFC Technical Standard Specification For Control And Qualification Of Laser Powder Bed Fusion Metallurgical Processes |
| NIST | Technical Note 1801 |
Another element of quality management is the warehousing of information that is pertinent to the materials and processes of the applications in the industry. There are several areas where this information is being stored for users to analyze and apply in their digital data ecosystems:
The Digital Thread in Post-Processing
Post-processing varies by the printing modality, and may include heat treatment or surface treatment of parts. The quality requirements most often documented are simple completed/non-completed. For some organizations, there are broader (and more expensive) end-to-end ecosystems where a series of machines for material and part inspection are all connected in the same software platform. This allows a single part to be transferred inside an existing database connected to a set of machines.
Let’s work through an example for metal powder bed fusion. When a part is ready for production, a material needs to be selected. If this part is going to be produced to fit a specific standard or industry requirement, most likely there will be some documentation of the material characteristics of the feedstock going into the machine. In some cases, the material vendor shares a written Certificate of Conformance with the entity that is printing the part. This will include characterization details such as material composition, particle size distribution, and morphology details.
For those organizations going beyond this specification, they may need to do additional screening on their in house equipment such as particle size analyzers, SEMs, flow meters, or DSC’s. Each of these tests typically take place on external test equipment (which is designed for all types of materials, not only AM materials), and the data can be outputted to a spreadsheet or test report. Even at this early stage, the team has created a substantial amount of data that may not be directly tied into an organization's digital thread. (Think of it like an island within the workflow.) Next, the material is processed in the machine and goes through a variety of thermal change before consolidating in its final shape. The machine and process data is another island of information.
Finally, a third island of data is created during post-processing and inspection of the part once it is removed from the machine. This could take the form of part scanning like CMM or even CT Scanning to offer users a validated inspection record.
In other words, the data can be analyzed after the fact but in-situ changes or trends are much more difficult to process because the amount of data that is connected in a standard additive manufacturing is a combination of data islands and operator intuition.
An additional hurdle in quality management of 3D printed parts is the difficulty of achieving process repeatability and part-to-part reproducibility. The multitude of different process parameters, along with inconsistencies in buld-to-build job management, can make it difficult to 3D print identical parts across multiple build cycles.
Trends and Consolidation in the Quality Management Digital Thread
Inspection equipment of manufactured parts is becoming more digitized, which allows manufacturers and organizations to leverage machine learning and artificial intelligence. Since part inspection is not unique to the AM production process, the companies in the space have been looking to improve these tools for all manners of manufacturing. Some have taken the approach to build a suite of hardware and software systems that can talk to one another in a similar ecosystem. Others have approached the problem by applying lower cost scanning tools and matching them up with high powered data analysis tools.
With inspection, one of the fundamental challenges is deciding what to inspect and to what degree. Part complexity, material, and size all come into play when thinking about approaches. Alongside our discussion of the digital thread is a conversation about automation. There are certainly inefficiencies in most current 3D printing workflows requiring heavy manual inputs, and part inspection is often one that relies the most heavily on this integration with the user.
Overall, when considering the digital thread for quality management, manufacturers must think about the balance between how much data is being manually captured versus automatically captured, and, perhaps more importantly, how much is being analyzed by operators and engineers versus being automatically analyzed.
Applications of Standards in the 3D printing Digital Thread
Based upon the rapid pace of change throughout the sector, the addition of standards to benchmark and ground approaches to the technology has lagged behind. Most of the standards that have been generated in the industry have been rightly focused on the process and materials. The American Society for Testing and Materials has a working group dedicated specifically to additive manufacturing (ASTM F42). Collaborations between them and the international community, embodied by the International Organization for Standards (ISO), have produced a series of detailed documents to help guide users along their AM development cycle. Many of these standards do provide details on elements of vocabulary and structure for a consistent language across the various platforms but very few report on specific standards relative to the digital elements of 3D printing.
In addition to the AMMD system architecture, the NIST team along with a series of collaborators have created a common dictionary to help users, machine producers, and software companies across the industry develop new tools to increase capabilities among the AM digital thread.
As with many elements of the 3D printing sector, analogous efforts have been taking place in technologies such as machining for a number of years as well. The MTConnect platform is a device information model that helps organizations and teams define a series of structures within their machines that can be outputted to a common data structure. This is an effective tool for some aspects of manufacturing; however given the range of data streams that could be tapped for AM, the variable set can become much wider than a technology like machining. This has led to an approach by many machine manufacturers of 3D printing equipment to build their own software connectors (APIs) that can transfer data to partner software platforms. In some ways having a unique API is a competitive advantage for these organizations but the ability for software developers to have widespread interoperability is highly limited with this approach.
Approaches to integrating the Digital Thread in an Organization
The opportunities presented by the potential of an end-to-end digital AM thread are enormous. We have discussed how the fundamental inputs (a 3D Digital CAD) start the process of 3D printing, and that through the workflow there are a number of interactions that reside both in the digital as well as the physical realm. As we think about how this ecosystem will have an impact on the additive manufacturing sector, it is useful to think about this question from three different perspectives: the printing machine manufacturer, the operator processes and support equipment, and the end user.
The first is within the 3D printing machine manufacturer space. There are dozens of different machine manufacturers producing equipment for different materials, end uses, and customers. Most equipment manufacturers are focused on providing their customers with a stable platform that delivers parts on a repeatable basis. Historically, because many of the platforms were targeted towards the prototyping market, the necessity to have additional software integrations outputting machine analytics or costing was not really necessary. It was also the case that most printer manufacturers were not considering how the technology could relay data from other parts of the 3D printing workflow. That is slowly changing, and the ability to connect machines is coming down in price. WIth that said, the structure and format of these data flows remain at the early stages of usefulness. Efforts such as the MTconnect platform alongside third-party vendors in the AM software space are building that digital functionality space along with efforts from NIST and other standards organizations to structure the data for the end users.
The second perspective is from the standpoint of support equipment and steps involved in the 3D printing workflow. There remain a number of steps that are not machine-centric and often rely on actions of people involved in the operations. This includes aspects such as those associated with the material and material characterization alongside all the post-processing and inspection elements that come with any build. These aspects are starting to get integrated into the digital thread and are critical elements for end-to-end quality assurance for the process.
Finally, the third perspective is based upon the end user. There is a wide range in knowledge regarding AM technology throughout the greater manufacturing environment. Current manufacturers may not have deep experience with the technology, and personnel may not have had AM as a core part of their education or training. Also, there are so many technologies that it is hard for any one person or even one organization to be an expert at every single platform. Often, the adoption and investment in AM equipment and processes comes with high expectations and showing feasibility for a particular application is the first order of business. Ultimately, a mature digital thread that enables users to have an easy way to increase the effectiveness of their operations will help drive more usage and business models for the technology.