LONDON, UK, Mar 4, 2024 – AMFG is excited to announce that they are collaborating with Materials Processing Institute (MPI) and Additive Manufacturing Solutions (AMS) in a £500,000 Innovate UK project to develop an advanced database ‘SMART App’, linking powder input properties against AM part performance to provide a predictive tool via Machine Learning.
Image: Scott Graham
Project Objectives
The primary objective of the project is to enhance powder reusability through collaborative efforts. MPI and AMS will systematically collect and analyze data related to reused powder, leveraging this information to develop a predictive model for powder quality. This model will guide processes aimed at improving the overall quality of the powder. AMFG, in turn, plays a crucial role in empowering 3D manufacturers by offering valuable suggestions on optimizing powder use to enhance quality and meet specific part requirements.
Nick Parry, industrial digitalisation group manager at the Teesside-based Materials Processing Institute, commented: “SMART-APP is the next logical step to continue the work the Institute has already undertaken in powder characterization. This predictive tool will develop and enable world-class production of AM components, with smart solutions for resource efficiency and providing longer use of materials feedstock and reducing wastage.”
The ultimate goal is to facilitate world-class production of additive manufacturing components, incorporating smart designs for resource efficiency and promoting the prolonged use of materials feedstock to minimize wastage. The development of a versatile predictive tool necessitates innovative characterization methodologies, correlating powder characteristics such as size and morphology to flowability, ensuring optimal performance and sustainability in the additive manufacturing process.
SMART-APP is set to revolutionize beam-based additive manufacturing with the development of an advanced and commercially viable predictive tool for materials management. This innovative tool aims to enhance the cost-effectiveness and efficiency of additive manufacturing processes. Built on a dual foundation, the first being a physical model derived from materials characterization history, the tool assesses the quality change of powder after each use.
Alexander Grimmer, technical consultant at London-headquartered AMFG, offered: “This initiative aims to transform additive manufacturing towards more resource-efficient methods. SMART-APP aims to instill trust in the additive manufacturing realm by forecasting powder quality and recommending processes to restore desired powder properties for reuse. AMFG eagerly anticipates contributing to a cutting-edge material management system in this project, poised to deliver substantial environmental and economic benefits to the industry. This endeavor is set to expedite the widespread adoption of additive manufacturing.”
Image: Getty Images
AI Counterpart
The second foundation is an AI model, a digital counterpart, predicting the powder’s shelf-life and maximum reusability based on its processability. Beyond evaluating the quality degradation of each powder batch, the tool incorporates comprehensive lifecycle assessment data for each material. As a result, the predictive tool not only identifies quality changes but also proposes alternative process parameters and efficient methods for powder reclamation, offering a holistic solution for optimized materials management in additive manufacturing.
The project is centered on optimizing the laser powder bed fusion process (L-PBF). Current powder characterization methods primarily focus on the static and dynamic capabilities of virgin powders, offering limited insights into powder processability and an underdeveloped understanding of how powder properties age and impact the material performance of manufactured parts.
Rob Higham, CEO of Additive Manufacturing Solutions, added: “AMS is delighted to have the support of Innovate UK to continue developing our portfolio of world-leading powder and AM process optimization capability. This marks our first step towards a ground-breaking approach to dynamic materials management. The potential of the AM process remains a potential in many people’s eyes. It could be realized with the development of a versatile and smart predictive tool for tracking powder quality after each reuse.”
Project Processes
Within the L-PBF process, virgin powder undergoes aging after a limited number of uses, and a substantial portion (~2/3) of unused powder is extracted during the de-powdering of the build plate, with the remaining proportion screened from the overflow chamber and mixed with soot. Additive manufacturing users reintroduce this unused but processed powder back into the production cycle. Determining powder shelf-life (age) involves investigating correlations between the physical and chemical parameters of the powder. The resulting correlation matrix will be input into a model, enabling the development of a sophisticated predictive tool. This tool, integrated into a smart materials management strategy, aims to optimize processes, enhancing overall product performance through informed decision-making in laser powder bed fusion.
In this project, they will leverage the established baseline methodologies from the Materials Processing Institute while introducing new methodologies that correlate with fresh sets of parameters. The creation of a new physical model, utilizing data from the characterization data bank, will propose process parameters. Their innovative machine-learning tool will capitalize on these trends to predict performance. The final stage of innovation will involve exploring the groundwork for a commercial app, marking the first-time application as a materials management tool for resource efficiency among additive manufacturing users.
See here for more information about the Materials Processing Institute (MPI)
See here for more information about the Additive Manufacturing Solutions (AMS)
About AMFG
AMFG is a leading provider of MES software for manufacturing. Their software solutions empower manufacturers, allowing them to manage their workflows and achieve streamlined, automated processes.
With over 500 successful implementations in 35 countries and across a range of industries, we specialize in enabling companies to successfully integrate our software for AM and CNC production, into their wider manufacturing processes and scale their AM operations.
For more information, please visit www.amfg.ai.