Event Type:
MSE Grad Presentation
Date
Talk Title:
Multi-scale Materials Characterization, Part Testing and In-situ Process Monitoring enabled Multivariate Statistical Process Control in Laser Powder Bed Fusion Metal Additive Manufacturing
Location:
Via Zoom Video Conferencing
https://gatech.zoom.us/j/96405988226?pwd=ZlN1b0x5NWlzS1F1TUt4bmhPTml4UT09
Other Info
Meeting ID: 964 0598 8226 Passcode: 318224

Committee Members: 

  • Prof. Suman Das, Advisor, ME/MSE
  • Prof. Hamid Garmestani, MSE
  • Prof. Sandra Magnus, AE/MSE/INTA
  • Prof. Jianjun Shi, ISyE/ME
  • Prof. Preet Singh, MSE

Abstract:

Widespread adoption and industrial scaling of Additive Manufacturing (AM) technologies, specifically in metals, is currently challenged by a variety of issues including (but not limited to) dimensional and form errors, undesired (and oftentimes stochastic) porosity, delamination of parts, extreme variations in part properties (geometry, mechanical and physical properties), undesirable failure rates, and significant costs to optimize processes for gaining acceptable part quality, albeit with fairly limited statistical confidence. The objective of this dissertation proposal is to establish the basis for pre-process materials characterization, in-situ process monitoring, and post process multi-scale part characterization required to enable a framework for Qualification, Validation and Verification (QVV) through process control charts using multivariate statistics in Laser Powder Bed Fusion (L-PBF) Metal AM. In pursuit of this objective, parts made of an extra-low interstitials variant of Ti-6Al-4V, a specialty Titanium alloy of immense interest to the AM community and ubiquitously used in mission-critical applications in the Aerospace and Healthcare industries, will be manufactured using a commercially proven L-PBF metal AM system (3D Systems ProX DMP 320). The proposed research tasks include multi-scale feedstock material (pre-process) and part (post-process) characterization, multi-fidelity (in-situ) process monitoring, and multivariate statistics-enabled analysis of data streams. The aims of the proposed research are to: (i)study variations in microstructure and chemistry of parts manufactured across multiple builds under the same regimen -to understand variations within a typically allowable feedstock, process, and output part specification (ii)study variations in bulk mechanical and physical properties of parts manufactured across multiple builds under the same regimen -to understand variations within a typically allowable feedstock, process, and output part specification (iii)identify correlations between process parameters, microstructure, and part properties to quantify variations, dependence, evaluate uncertainties, establish tolerance limits, and develop process control charts using multivariate statistics for L-PBF Metal AM. If successful, the outcomes of this dissertation research will enable, in L-PBF Metal AM: (i)real-time process monitoring enabled statistical quality control (ii)reliable reduction in post-process non-destructive test and evaluations (iii)significant reduction of empirical testing required to fully qualify processes/parts. (iv)framework for comprehensive QVV methodology. This proposed dissertation research work is supported by the Office of Naval Research and the National Institute for Standards and Technology through grants N000141512900 and 70NANB21H008 respectively.