Learning Objectives
By the end of this section, students will be able to:
- Understand the importance and role of the digital thread in both conventional and additive manufacturing.
- Describe the end-to-end digital requirements for additive manufacturing.
Additive manufacturing is a small piece of the overall manufacturing and production landscape. Within any manufacturing environment there will be multiple machines that in conjunction with the operators work together to produce physical goods. The interaction between machines, the facility, and the employees generate terabytes of data daily and insights through leveraging the digital thread have been transforming the manufacturing industry for many years.
One of the principal trends moving the pace of development forward is that computing power, wireless connectivity, and the internet have allowed sensors and integration platforms to become less expensive. Functionally, the primary driver of any manufacturing organization is producing a physical product in a safe and efficient manner that allows the firm to make a profit. The systems within a manufacturing environment are oftentimes highly complex and rely on the integration of information from several processes and people over a sustained amount of time. Even the most optimized and high-tech manufacturing floor is ripe with areas where sensors or increased data analysis could lead to efficiency gains that can influence the bottom line. Framing these questions and hypotheses is a central tenet to the digital thread because just capturing data at different points in the manufacturing process does not inherently add value. There needs to be some action or response tied to the outcome of this information. Listed below are some example hypotheses that manufacturing companies have sought to examine with increased digital connectivity:
- How efficiently are my machines operating?
- Are there machine optimizations that should be made to increase sustainability and decrease energy usage?
- Where are the errors in my manufacturing process occurring and can we fix or predict them?
- Can I predict when my machine is going to break down or when I have to replace a tool?
Because the pace of development with sensors and wireless connectivity has accelerated more rapidly than most manufacturing plants have updated their machines, there are operational challenges to implementing digital strategies into an organization. Newer CNC equipment may have sensors already built in that allow easy downloading of analytics but older generation machines still perform their functional role very well but are not connected to any other parts of the plant. Now there are retrofit kits that enable users to install sensors on their legacy equipment in less than one hour. The functionality of the sensors varies, but users can typically select parameters that give them visibility into the machine status, heat generated from the machine, and operational statistics.
Perisense offers low-cost retro-fit sensors that help inform production decisions on manufacturing lines. The sensors keep track of runtime, part count, process repeatability, and maintenance indicators.
The challenge of digital connectivity may start with the data that one might be trying to capture. For example, a data component might be some error or inconsistency in the build. However, there is an increasing disconnect between how long it takes to acquire the data and the window of time that the information is relevant or actionable. If the error data is discovered after the build, the entire build may be disqualified, wasting expensive material and time.
In conventional manufacturing similar gaps persists, especially as it refers to quality assurance. Presumably most organizations have some quality metrics in place to ensure that the parts that are being produced fulfill the customer requirements. Most of these metric systems are able to pick out an individual part or batch of parts that are out of spec once the full part is built. Digital tools based on the machine can now identify problems earlier on in the part manufacturing process. Signal irregularities or even electrical disturbances can be gleaned from mundane machine sensors and relayed back to computer systems that aim to keep a long-term cumulative view of the machine performance.
Key Digital Requirements for Additive Manufacturing
Additive manufacturing has many unique features that enable it to impact a wide variety of industrial applications. Fundamentally, the process is rooted in a digital ecosystem; no printing can take place without some sort of digital file as an input. The necessity of digital inputs avoids a major hurdle that faces other forming technologies where a 2-D drawing may suffice without any digital record. This has major implications for building a full-fledged digital thread capability within the process workflow but arguably just as important, it requires those that engage with the technology have at least some familiarity with operating in a digital ecosystem. The breadth of manufacturing sophistication and workforce training is something that should not be overlooked. Even 30 years into its existence, the AM industry faces limits on widespread adoption and operator/engineer training.
Throughout this chapter, the AM digital thread will be evaluated at each step of the process workflow. As it pertains to the concept of the digital thread, we will try to maintain a consistent nomenclature; however, the nature of the additive manufacturing industry is that the acceleration of development and advances is ever increasing. The table below outlines the high-level concepts that will form the foundation of this chapter. We will cover five main sections of the digital thread: Design, File Processing, Simulation, Build Monitoring, and Quality Assurance. Within each of these sections, the methods that have been employed to retrieve data from the manufacturing ecosystem along with how end users can leverage the information to make meaningful advances in their business will be discussed.
| Design | File Processing | Simulation | Build Monitoring | Quality Assurance |
|---|---|---|---|---|
| Production of the digital files for use in 3D printing including higher order operations | Transfer and dissemination of design files to the printer destination. This includes formatting individuals into a complete build with multiple components and designating printer settings | Digital simulation of the functional part, process simulation, and analysis of the final part, as well as part processing aspects such as support structures | Data generated from the printing and operation the machinery including yield/efficiency data, part production metrics, and quality reports | Data generated from various workflow steps, including materials processing and post-processing, which contribute to a complete view of the resulting parts. |
When considering which 3D printing workflow components have a significant digital component, the obvious component is the fundamental design input – the STL and related files. However, several other elements have a significant digital footprint, including the following:
- Parts selection
- Materials profile
- Machine characteristics and requirements
- Post-processing steps and data
- Testing and inspection
By using digital inputs, parts selection can be automated to assist with the evaluation of whether a part makes sense for a given AM printing system. Contained within the data of a digital part are things like geometry and part dimensions. As one progresses through the workflow the material inputs become another element of construction. Data can be generated on the type and caliber of material along with information that results in a more complete view of the operations in the facility such as usage and price.
Next, the machine and process data provide a window into the actual operations that go into constructing the part. The exact data stream coming from this step will be highly variable depending on the type of process modality. Similar to traditional manufacturing, most early model 3D printers have limited ability to track and export build information to a coherent platform. Most of the time this information ends up residing in the machine and unconnected from other information generated in the full workflow. Aspects of the machine and process data that can be currently captured include build height, timing, cost as well as more fundamental machine performance details generated from data from embedded sensors. Many printers now have the ability to take a picture of each layer or provide user feedback on critical parameters such as the environmental conditions inside the build chamber.
Once the parts are removed from the machine, there are a number of steps necessary to deem a production part completed. This could include support material removal, heat treatment, machining, or finishing. Most of these steps are conventional manufacturing processes that historically do not have a great deal of interconnectedness to digital documentation. Nevertheless, several printer modalities have very specific post-processing requirements and equipment that are being integrated into extensions of the printers themselves. This assists in the ability of the users to document a full digital transcript of all the activities that take place following the printing process. The reason that these steps become critical is two fold. First, since many of the processes can have a major impact on the final part properties, it is important that the end customer has visibility into the end-to-end process. Second, throughout the post-processing procedures there may be multiple employees or facilities handling each part. By documenting each part of that workflow errors can be minimized and long-term traceability preserved.
The final stage of the AM digital workflow process is testing and inspection of the parts. There are multiple approaches to validating components and this is typically driven by the industry requirements. Many inspection methods are digital in nature whether it be dimensional analysis or material testing. Again, similar to traditional machine tools, the digital outputs may live on independent servers or platforms that are not obviously connected to a centralized repository. With all that in mind, the breadth of the 3D printing digital thread is expansive and is not uniform across even the same printing modalities.
The Challenges of the Digital Thread for Additive Manufacturing
The AM digital thread is by no means mature. While certain technologies are well established, most manufacturing, logistics, and related organizations would admit that the technology as a whole remains a new entity for manufacturing. There are three primary challenges to adoption of the digital thread in the additive manufacturing sector:
- Stability of the AM Process and Technologies
- Cost for Implementing End to End Solutions
- Workforce that Can Build, Interact and Leverage the Digital Thread
Stability of the AM Process and Technologies
3D printers can be considered a platform technology that enables a wide range of applications for many industries. There are dozens of printer manufacturers accounting for thousands installations globally. The relative newness of the technology and the intensive research and development underway means that significant incremental improvements or capability expansions are represented by new machines and processes. As a result, many facilities may have multiple printer modalities. Each system may have a unique workflow with different part production and processing requirements. In an ideal world, these inputs would be centralized on a data stream where information could be connected from part to part. There are software tools that are starting to connect some of the machines to gain intelligence; however it is a significant challenge to adapt to such a wide array of technologies, materials and vendor platforms and do so in a way that makes sense for multiple industries.
Costs for Implementing End-to-End Solutions
One of the biggest factors that has pushed the concept of the digital thread throughout a broad user base is the cost reductions for sensors, data storage, and high-quality internet connectivity. This set of parameters makes it possible for even the smallest manufacturer to add low cost solutions to their facility such as cameras or temperature sensors. Ultimately, this is a positive thing, but in many cases, additional data streams become unique islands of information that may not be connected to any other part of the manufacturing process. In order for the full concept of the digital thread to be realized, a significant amount of infrastructure needs to be put into place including either cloud or local databases, security protocols, and methodologies for maintaining the systems. Even more importantly, it may require manufacturing facilities to recruit different types of employees to manage these processes. Expertise in data science, machine learning, computer science, and artificial intelligence may be required alongside a fundamental understanding of how the mechanical and material aspects of the production processes work.
Cost is measured not only by money but also by time for implementation. Depending on what sorts of legacy IT systems are in place at a given facility, it may take some time for facilities that are trying to modernize to integrate new platforms. This transfer becomes one of the biggest barriers to success and can push out time scales significantly. Finally, printing technology and the digital thread are not static. Things will continue to evolve and organizations can be slow in deciding when is the right time to make an investment that can pay real dividends but also not be obsolete in a few short years.
Workforce that can Build, Interact, and Leverage the Digital Thread
Assuming an organization can build a platform that enables widespread data analytics on their manufacturing processes, success is not immediately guaranteed. Data is useless unless it has some contextual grounding. Certainly, interesting insights can be gained developing machine metrics but the higher order benefits of the digital thread will come from a close collaboration and translation of real life activities on the factory floor from employees partnered with new information that is coming from the digital thread. There is a risk that having too much information being generated during the manufacturing processes can be harmful or so confusing that the tools ultimately become ignored. In many ways, the reliance on just numbers to provide top-down insight on an additive manufacturing operation is likely not going to be successful. The hope of most organizations is that the digital thread augments a workforce that together can become more efficient. This may mean that the tools will have to be designed with the existing workforce in mind and additional training would be required to make the digital thread approaches worthwhile.
Long-Term Implications for Additive Manufacturing
As 3D printing technologies continue to evolve there will be many more platforms and even fundamentally new technologies that will continue to add tools to the manufacturers’ tool belt. It is likely that more capabilities that were once segmented (such as post-processing, digital inputs) would be more integrated into the machines themselves so that users starting new will have an easier time getting deploying a digital thread strategy. Ultimately, the potential for building a robust digital strategy around 3D printing is extremely powerful. The process is driven by digital designs that can be transferred around the world (or even out of this world) with the click of a button and can make supply chains much more robust through distributed manufacturing.
The other hope for more digital engagement with the manual workflow of 3D printing is that the technology will become more repeatable and stable. This has enormous implications for enabling business models for 3D printing, whether it is customized part production or building a digital warehouse of files to support repair or replacement parts. These promises are still more fantasy than reality for the average manufacturer; to implement them, the operators, engineers, and customers will need to be engaged at a level to ground the possibilities in practical reality.