Small Business Innovation Research/Small Business Tech Transfer
Integration of Fast Predictive Model and SLM Process Development Chamber
Project Description
This STTR project seeks to develop a fast predictive model for selective laser melting (SLM) processes and then integrate that model with an SLM chamber that allows full control of process variables and is equipped with in-process sensors. The combination will create a closed loop in which the model suggests process parameter settings for test builds and the sensors provide feedback to the model. This creates a powerful tool for iterative process development far faster than is currently possible by standard simulation methods and accessible to a wide range of potential SLM innovators who are not simulation specialists. The key innovation will be the development of a simple set of empirical equations that relate SLM process inputs to actual build results. This is accomplished by a combination of finite element simulations and verification experiments whose process parameters are selected by a design-of-experiments methodology. The resulting easily calculable empirical functions (a.k.a. the fast predictive model) will replace arduous simulation and undirected trial-and-error as methods of SLM process development. A user-friendly interface will be written that links the fast predictive model to sensorized SLM chamber to allow easy, rapid and flexible SLM process development. The simplicity of the system, and relatively low cost of the SLM chamber will allow large numbers of new innovators and industries to enter the field of SLM and develop novel processes that meet their application needs, as well as help solve specific problems of NASA interest. Phase I activities include 1) development of the fast predictive model, 2) development of a control algorithm and user interface linking the model to the SLM chamber, and 3) demonstration of the integrated system for rapid development of novel SLM processes.
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Anticipated Benefits
The technology developed under this STTR will enable rapid development and optimization of SLM processes. The experimental process development chamber can be configured to simulate existing commercial SLM machines, and thus process developed with the proposed system can be exported to these machines (which are not themselves well designed for process development). This supports the goals of the SLM laboratory at Marshall Space Flight Center as well as participation of the NASA Space Technology Mission Directorate in the Materials Genome Initiative. Implementation of the fast predictive model technology can improve the processes for SLM manufactured parts. This impacts a number of space platforms and terrestrial applications too long to list. Of particular interest to NASA is the use of in-process monitoring to verify build quality. Because the proposed system has in-process monitoring built into the process development methodology, it has a high likelihood of developing processes of which NASA engineers can be confident and for documentation of process quality can be compiled.
Aerospace commercial applications have high overlap with NASA applications including strong interest in fabrication of rocket engine components and a variety of other light-weighted structures. Apart from a desire for faster SLM process development, the commercial market also has a keen interest in in-process monitoring and closed loop process control. The use of feedback from in-process sensors both to develop the fast predictive model and conduct rapid process development entails concepts and techniques that are closely related to in-process control. (I.e., in-process control is essentially continuous, real-time, in situ process development.) Thus it is likely that fast predictive models, as developed under this project, can be implemented to facilitate or enable closed loop process control. Finally, we note that a key goal of this project is to provide a system that will make SLM process development accessible to a large number of new innovators and industries, allowing them to enter the field and create a wide range of applications that are currently unidentified. More »
Aerospace commercial applications have high overlap with NASA applications including strong interest in fabrication of rocket engine components and a variety of other light-weighted structures. Apart from a desire for faster SLM process development, the commercial market also has a keen interest in in-process monitoring and closed loop process control. The use of feedback from in-process sensors both to develop the fast predictive model and conduct rapid process development entails concepts and techniques that are closely related to in-process control. (I.e., in-process control is essentially continuous, real-time, in situ process development.) Thus it is likely that fast predictive models, as developed under this project, can be implemented to facilitate or enable closed loop process control. Finally, we note that a key goal of this project is to provide a system that will make SLM process development accessible to a large number of new innovators and industries, allowing them to enter the field and create a wide range of applications that are currently unidentified. More »
Project Library
Primary U.S. Work Locations and Key Partners
| Organizations Performing Work | Role | Type | Location |
|---|---|---|---|
| Universal Technology Corporation | Lead Organization | Industry | Dayton, Ohio |
Langley Research Center
(LaRC)
|
Supporting Organization | NASA Center | Hampton, Virginia |
| Wright State University | Supporting Organization | Academia | Dayton, Ohio |
Primary U.S. Work Locations
-
Ohio
-
Virginia
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