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Open Access Publications from the University of California

Material Quality and Process Monitoring in Metal Additive Manufacturing

  • Author(s): Clemon, Lee
  • Advisor(s): Zohdi, Tarek I
  • Dornfeld, David A
  • et al.
Abstract

Additive manufacturing is rapidly becoming available to designers, hobbyists, and industrial manufacturers. Unfortunately, process control and predictability remain beyond the grasp of most users and even machine tool builders. This manuscript employs design and material quality monitoring to explore solutions to these challenges. Metallurgical study and design limitations were explored through intentional design. Design for additive manufacturing was shown to provide insight into both process constraints for aesthetics and metallurgical properties. In assessing the process-structure-property relationships of metal additive manufacturing, test designs must consider the heat and mass transport phenomena as well as post-fabrication analysis requirements, several such designs were presented. On monitoring, an algorithm with inspiration from the field of statistical learning was proposed. This method serves as a computationally feasible approach to monitoring real-time material quality. This algorithm employs a dynamic Bayes network model and can be shown to reduce to a Kalman filter under some assumptions. Thus, direct comparison of the proposed algorithm with known control methods is possible. Graphical analysis was also conducted on the various parameter relationships reported in the literature for powder-bed fusion. The analysis revealed optimal clustering of the parameters and serves as a guide for future researchers to look beyond the melt pool for quality control. In support of in-process monitoring, a numerical simulation and validation experiment were constructed related to the acoustic emissions of particle impacts during directed energy deposition. Representative particle spray is simulated with the discrete element method. Impacts upon the build surface were used as inputs to a finite-difference wave propagation model to approximate the acoustic signature of the spray. The results of these several studies support manufacturing process monitoring for additive manufacturing. They also serve as a base for further investigation into material property tailoring and generally improved system control for additive manufacturing.

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