Researchers have devised a data-centric approach to generative modeling for 3D-printed steel. The team believes that their model could help determine the quality of a design and the material before fabrication. Additive manufacturing processes, commonly known as 3D printing, present a multitude of exciting opportunities across numerous industries including engineering, aerospace, and automotive. This is due to the development of AM for metallic materials which has facilitated the production of complex and intricate components such as fuel nozzles for jet engines. However, unlike conventional materials, metals that have been fabricated and formed via AM processes exhibit greater levels of variation in both their geometric and mechanical properties. These variations are not widely understood which impedes post-manufacture testing for establishing safety standards which mean manufacturers are met with a certain barrier.Of all the emerging technologies in AM, one of the most promising for the production of large-scale components is wire arc additive manufacturing. This process is a variation of direct energy deposition technology and utilizes an arc welding process to 3D-print metal components. Unlike conventional metal powder AM methods, WAAM melts metal wire using an electric arc as a heat source on a metal substrate base. When the wire is melted and expressed in the form of beads on the substrate. As the beads adhere to one another, a layer of metal material is produced. The process is then repeated, layer-by-layer until the metal component is completed.However, there are still challenges associated with this process owing to the uncertainty around the structural and mechanical properties and complex thermal deformations. Hence there is a need for a method to support an effective material characterization for WAAM. Researchers develop a generative statistical model that enables ensemble-based predictions of the performance of a stainless steel WAAM component before it is manufactured,” explained Dodwell. In order to successfully characterize the mechanical properties of WAAM steel effectively, the team also needed to develop a method for isolating geometric variation characterization. Then by combining the generative statistical models for mechanical and geometric variation in WAAM steel, a unified statistical model was produced. What is more, this generative statistical model treats both sources of variation independently.The team demonstrated that acquiring relatively small amounts of training data can be utilized for successfully training a generative statistical model for AM steel. This paves the way for making general predictions of performance at a variety of structural lengths, however, the team state that experimental testing remains imperative where safety-critical certifications are concerned.The research is published in the Proceedings of the Royal Society.
Researchers have devised a data-centric approach to generative modeling for 3D-printed steel. The team believes that their model could help determine the quality of a design and the material before fabrication. Additive manufacturing processes, commonly known as 3D printing, present a multitude of exciting opportunities across numerous industries including engineering, aerospace, and automotive. This is due to the development of AM for metallic materials which has facilitated the production of complex and intricate components such as fuel nozzles for jet engines. However, unlike conventional materials, metals that have been fabricated and formed via AM processes exhibit greater levels of variation in both their geometric and mechanical properties. These variations are not widely understood which impedes post-manufacture testing for establishing safety standards which mean manufacturers are met with a certain barrier.Of all the emerging technologies in AM, one of the most promising for the production of large-scale components is wire arc additive manufacturing. This process is a variation of direct energy deposition technology and utilizes an arc welding process to 3D-print metal components. Unlike conventional metal powder AM methods, WAAM melts metal wire using an electric arc as a heat source on a metal substrate base. When the wire is melted and expressed in the form of beads on the substrate. As the beads adhere to one another, a layer of metal material is produced. The process is then repeated, layer-by-layer until the metal component is completed.However, there are still challenges associated with this process owing to the uncertainty around the structural and mechanical properties and complex thermal deformations. Hence there is a need for a method to support an effective material characterization for WAAM. Researchers develop a generative statistical model that enables ensemble-based predictions of the performance of a stainless steel WAAM component before it is manufactured,” explained Dodwell. In order to successfully characterize the mechanical properties of WAAM steel effectively, the team also needed to develop a method for isolating geometric variation characterization. Then by combining the generative statistical models for mechanical and geometric variation in WAAM steel, a unified statistical model was produced. What is more, this generative statistical model treats both sources of variation independently.The team demonstrated that acquiring relatively small amounts of training data can be utilized for successfully training a generative statistical model for AM steel. This paves the way for making general predictions of performance at a variety of structural lengths, however, the team state that experimental testing remains imperative where safety-critical certifications are concerned.The research is published in the Proceedings of the Royal Society.
