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Artificial intelligence (AI) and additive manufacturing (AM) are both disruptive new technologies. AI has entered many aspects of our lives, but has not been fully realized in the world of AM. Because of the vast amount of data and the digital nature of the technology, AM offers tremendous opportunities in machine learning (ML) and consequently AI.
Companies in the 3D printing sector are rapidly adopting artificial intelligence technologies to further extend additive manufacturing’s capabilities. Machine learning (ML), a subset of AI in which a computer “learns” to perform a task by itself without being explicitly programmed to do so, is the first area in which businesses are developing practical applications. ML when combined with 3D printing technologies could increase the performance of a 3D printer by reducing the risk of error and facilitating automated production.
AM is a digital technology that can significantly benefit from the new advancements in data science, ML and AI. Given that all steps in AM process are done digitally, data collection and organization on the process itself is facilitated. “Some 3D printing companies use machine learning to help design parts, and some use it to help identify which parts are a good fit for additive,” said Zach Simkin, president of Senvol, a New York City company that supplies software and other data-related products for implementing AM.
“Part of what’s unique about additive manufacturing is you’re effectively building the material as you’re processing the part, so material properties can vary substantially,” said Simkin. “Changing the parameters, such as laser power, scan speed, or hatch spacing, can have a significant impact on the part’s properties.” Typically, determining the optimal parameters for generating a part with the desired properties has required expensive and time-consuming trial and error.
For example, ML and AI can be used to optimize process quality and reduce defect density. Improving the feedback control to enhance the process quality is an area that has been heavily investigated. Additionally, AI can be used by combining the design and process together through what is so-called concurrent design. In this process, the information acquired during the build is used to improve the design adaptively and continuously. Design improvements can have multiple objectives including reducing the residual stress during the build process, reducing the weight, and improving the strength in certain areas where the defects have developed.
On the other hand, AM is a highly automated process during the design, process preparation and printing stage. This produces numerous system data that are difficult to visualize and interpret by human. This is exacerbated by the enclosed environment which creates obstacles in observation and monitoring of the process.
Furthermore, the speed of the process makes it challenging to monitor it. ML could come into play for data visualization, image recognition and system modelling to better understand this process. Moreover, the preparation and post-processing stages involve many labor-intensive processes which can be assisted by process automation with intelligent analysis algorithms for planning and decision making.
Automating the 3D printing workflow
For the purpose of the application of AI in AM—given the complex nature of the process—it is beneficial to break down the applications into pre-processing, process, and post-process. In the pre-process, ML can be used in design space (geometrical design, topology optimization, raw materials design and in powder properties).
In the area of raw materials design, recent advancement of ML allows for prediction of materials properties. It also facilitates designing new novel materials and can utilize AM’s unique manufacturing abilities to materialize the designs that were not feasible to.
The combination of artificial intelligence and 3D printing can also help to broaden the range of compatible materials and thus meet the requirements of industrial sectors such as aerospace, which most often require high-temperature materials.
A first application is, for example, the automation of the 3D printing workflow. This comprises various steps, from the creation of the model as a CAD file, to its preparation for printing in a slicing software, to its final printing. Material selection can also be automated with AI: depending on the requirements of the part to be printed, the software makes recommendations on the material to be used to achieve the best result.
AI can also help improve the 3D printing process. For example, the printability of an object can be analyzed before starting any process. The quality of a part can also be predicted and the process can be controlled to avoid printing errors, effectively saving time.
Like other applications, AI can only be applied to AM process if the ML programs have been developed and the data is available for learning. In-situ monitoring and process learning in AM has started relatively recently. Today we have companies and equipment that provide some in situ monitoring data during the process. On the research front, ML has been used in several different facets of process optimization, manipulation, and tailoring. Of interest are defect density, local defects, internal stresses developed during the process, design and dimensional accuracy, microstructural variabilities, and others. Controlling any of these parameters is a challenging task as the number of variables that impact them are immense.
One of the most important factors in AM manufacturing of parts is defects. For some critical applications such as aerospace applications where these defects can cause premature or fatigue failure and could cause catastrophic damage, it is imperative that these defects are avoided. The bottom-up nature of AM which is a huge advantage over traditional manufacturing techniques, provides the opportunity to detect and avoid defects on the flight and during build. One of the factors that has been of interest for the past decade is the melt pool temperature which in turn has been connected to the defect generation during the process.
Dimensional accuracy is another parameter that is important in a variety of applications. Many different factors affect dimensional accuracy. These include the initial geometry and design, type of material and its thermo-physical behavior in a heated environment, build parameters such as laser or e-beam spot size, the powder size, hatch spacing and layer thickness. Understanding the combinatorial effect of all these factors can help with making more accurate parts. Knowing how different the final part is from the initial design, can also help designers adjust their initial design dimensions to come out with the accurate final part. All of these can be learned from a process where analytical tools and devices inside the chamber can accurately measure the dimensions of the part as it is being built. Having that information on the fly, ML algorithms can help compare the build on the flight with the design specs and provide quick feedback to the process so that process can be adjusted accordingly.
Artificial Intelligence (AI) for Additive Manufacturing (AM)
US Army launched SBIR in Jan 2022 with the purpose of this Phase I topic to develop an AI capability that greatly improves the method for identifying and analyzing AM candidate parts. The objective is to develop Artificial Intelligence (AI) capabilities that analyze technical data information and assess the candidacy of a component for additive manufacturing, automate manual processes in order to reduce the time of engineering analysis by up to 80%, increase the pool of Additive Manufacturing (AM) candidates which leads to new opportunities and program creation, optimize the “Can Print / Should Print” analysis for higher yield of impactful AM candidates, and improve logistics trails and increase readiness through increased usage of additive manufacturing.
Currently, there is a manual process in place performed by engineers who are AM Subject Matter Experts. AM SME engineers search through Army databases to pull technical and logistics data and analyze data to determine printability. The development of an AI system which can automate the technical data analysis process through critical factors will greatly benefit efforts. AM can be integrated in a multitude of DoD programs and supply chains will be greatly improved with the increase of AM candidate parts, saving time, money and resources.
References and Resources also include:
https://iopscience.iop.org/article/10.1088/2515-7639/ac2791/meta
