Process-Part Co-Design and Qualification for Laser Powder Bed Fusion

Abstract

Additive manufacturing (AM), laser powder-bed fusion (L-PBF) included, can produce parts of complex shape without part-specific fixturing or tooling. AM technologies have been used in a variety of civilian and military applications. However, their complex spatially varying additive nature has led to significant variability in dimensional accuracy, microstructures, porosity, residual stress and fatigue life in the resulting parts. Such part-to-part and machine-to-machine variability has impeded effective and efficient qualification of AM parts, which has hampered the broader usage of AM technologies, especially in fabrication of mission-critical components.The goal of this proposed research is to develop computational methods for efficient qualification of AM parts and to exploit mechanical property variability in AM, such as changes in microstructures and porosity, as increased design freedom for process-part co-design. Such process-part co-design will essentially treat process variability as a feature instead of obstacle in quality control. We aim to simultaneously optimize process control variables, part geometry, and material properties in which material properties will be varied through AM process control.Specific research tasks include: modeling process-structure-properties relationships through physics-informed data-driven machine learning, process-part co-design, quantify the uncertainty, design for AM qualification, on-prem deployment for NAVAIR, inspection-based compensation, enabling simulation optimized scanpath control, and simulation-based scanfile support.Successful completion of the proposed research is expected to not only lead to increased part performance, but also ease AM process control, thus leading to improved AM technology adoption.

Document Details

Document Type
DoD Grant Award
Publication Date
Nov 08, 2024
Source ID
N000142412391

Entities

People

  • Xiaoping Qian

Organizations

  • Office of Naval Research
  • United States Navy
  • University of Wisconsin System

Tags

Fields of Study

  • Materials science

Readers

  • Manufacturing Engineering.
  • Neural Network Machine Learning.
  • Software Engineering

Technology Areas

  • AI & ML
  • Directed Energy