CPS: Synergy: An Integrated Simulation and Process Control Platform for Distributed Manufacturing Process Chains
pdf
Rapid and customized part realization in all industrial sectors imposes stringent demands on part attributes, e.g., mechanical properties, microstructure, surface finish, geometry, etc. However, part attributes can very rarely be directly measured and/or controlled in the production process. Instead, measurements are taken of accessible and measurable primary process responses that are known to influence the part's attributes. These primary process responses are then controlled through the manipulation of a set of controllable process parameters. This widely used strategy assumes that the proper control of the primary process responses will implicitly yield the desired part attributes. The current work aims to replace this implicit assumption by a model-based explicit evaluation of the part's attributes that uses newly established process models, available measurements of process responses and historical data from a data base that is continuously updated. In effect, this approach implies a direct instead of an implicit control of the part’s desired attributes and, as such, also moves a step closer to rapid part certification. In this project we have developed a fast part-scale thermal model of the additive process, a fine-scale model of powder spreading, melting and solidification, and a microscale mechanical model to predict material properties and part performance. These models are efficiently coupled together through a voxel representation of the part, so that fields such as temperature, microstructure, and stress can be passed as inputs and outputs at the various scales. At the same time, we have developed a state-of-the-art open-architecture Direct Material Deposition (DMD) machine, called ARPI, that is instrumented to provide validation data for the simulation framework developed. The integrated platform developed under this project will allow linkage of simulated and measured data throughout the additive manufacturing workflow, enabling prediction and control of desired part attributes through the measurable primary process responses.
Submitted by kfe760
on
Rapid and customized part realization in all industrial sectors imposes stringent demands on part attributes, e.g., mechanical properties, microstructure, surface finish, geometry, etc. However, part attributes can very rarely be directly measured and/or controlled in the production process. Instead, measurements are taken of accessible and measurable primary process responses that are known to influence the part's attributes. These primary process responses are then controlled through the manipulation of a set of controllable process parameters. This widely used strategy assumes that the proper control of the primary process responses will implicitly yield the desired part attributes. The current work aims to replace this implicit assumption by a model-based explicit evaluation of the part's attributes that uses newly established process models, available measurements of process responses and historical data from a data base that is continuously updated. In effect, this approach implies a direct instead of an implicit control of the part’s desired attributes and, as such, also moves a step closer to rapid part certification. In this project we have developed a fast part-scale thermal model of the additive process, a fine-scale model of powder spreading, melting and solidification, and a microscale mechanical model to predict material properties and part performance. These models are efficiently coupled together through a voxel representation of the part, so that fields such as temperature, microstructure, and stress can be passed as inputs and outputs at the various scales. At the same time, we have developed a state-of-the-art open-architecture Direct Material Deposition (DMD) machine, called ARPI, that is instrumented to provide validation data for the simulation framework developed. The integrated platform developed under this project will allow linkage of simulated and measured data throughout the additive manufacturing workflow, enabling prediction and control of desired part attributes through the measurable primary process responses.