LAMPL RESEARCH ON ADDITIVE MANUFACTURING
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Laser Additive Manufacturing Research
Objectives:

The goal of this project area is to develop the critical and enhanced capabilities of building three dimensional functional parts directly from CAD drawings and synthesizing novel materials using various laser-based additive manufacturing processes.  It also aims at developing a complete set of predictive models that will facilitate design, optimization, qualifications of the AM processes and their commercial applications. Specific objectives of the research include:

  • Find optimal operating conditions for various applications in order to:
    • maximize build rate
    • minimize sub-surface flaws
    • maximize part quality
  • Develop functionally grade deposition capabilities of material for various materials.
  • Develop a comprehensive set of predictive multi-physics models that link process-microstructure-mechanical properties for laser-based additive manufacturing processes.
  • Develop data-driven models that will capture the physical relationships
  • Develop the capabilities of synthesizing metal matrix composites
  • Develop the in-situ synthesis of novel materials such as bulk metallic glass materials, shape memory alloys and high entropy alloys
  • Develop in-process monitoring and control schemes for the laser additive manufacturing processes.

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Research Plan:

The study of laser additive manufacturing at Purdue is based on the simultaneous experimental and numerical investigation of various AM processes. The experimental investigations are carried out using multiple additive manufacturing systems (both blown powder and powderbed, see the bottom figures) to develop various applications including direct fabrication of functional parts, in-situ synthesis of metal matrix composites and new alloys, and functionally graded materials. Modeling efforts include multi-physics based comprehensive predictive models for prediction of powder flow, melting and solidification, resultant microstructure, deposited layer dimension and resultant residual stresses.   To this end, various in-house developed codes have been developed and are coupled with commercial FEM codes such as Abaqus to calculate the resultant residual stresses and mechanical properties.  A number of parallel processing computing workstations in cluster are available and being used for these computational work.

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Research Progress:

  • Experimental studies on AM of over 30 different materials, including following materials:

Iron and steels

Fe, H13, SS316, SS304, ODS steel

Other Alloys

Ti-based:  CP Ti, Ti6Al4V

Ni-based: Inconel 625, Inconel 690, Inconel 718

Al-based:  Al4047, Al6061, Al7075

NiTi shape memory alloy

Bulk metallic glasses (FebalCr25Mo20W10Mn5C3Si2B5, Zr65Al10Ni10Cu15)

Co-Cr-based: Stellite 6 (Co-Cr-W-C+others), Co-Cr-Mo

Monel K500 (Ni63Co27-33+others)

Metal matrix composites

Ti-TiC

Al-SiC

Ti6Al4V-TiC

In690-TiC

Other materials

W, Mo, Graphite, ZrO2

  • A comprehensive set of physics-based models:
    • Process models: 
      • modeling for powder flow distribution and temperature from the co-axial nozzle,
      • comprehensive molten pool model considering free surface tracking, coupled with the powder distribution model has been developed to optimize operating conditions of the directed energy deposition processes (see Figs. 1 and 2 below)
      • high fidelity powderbed fusion process model, considering powder spread, powder dynamics, molten pool dynamics considering denudation and powder-molten material interaction, etc.
    • Microstructure prediction models:
      • phase field modeling for solidification (2D, 3D)
      • cellular-automata model for large domain grain morphology prediction (3D)
      • hybrid cellular automat-phase field model that improves the computational efficiency by 4 orders of magnitude over phase field model (2D, 3D)
      • solid state phase transformation models
    • Mechanics model
      • prediction of mechanical properties based on microstructure (multiscale models)
      • prediction of mechanical properties based on microstructure (structural genome based)
      • metallo-thermo-mechanical modeling for prediction of residual stresses
  • In-process monitoring
    • An on-line track monitoring system based on a vision system and a scanning laser has been developed.   This system is capable of measuring the three dimensional deposited tracks in real time (see Figs. 3 and 4 below). 
    • In-process molten pool monitoring
    • In-process porosity monitoring
  • Applications:
    • Successful fabrication of various parts including hip implants (see the sample parts on the top of this page). 
    • Functional gradient coating
    • Synthesis of bulk metallic glasses
    • Synthesis of Nitinol with controlled phase transformation temperature and mechanical properties
    • Remanufacturing of legacy parts
    • Synthesis of magnetic materials
    • Synthesis of nuclear reactor materials
    • In-situ synthesis of metal matrix composites

Fig. 1: Molten pool and deposition

track model

Fig. 2: Molten pool fluid flow prediction

dottedBlockOneCrop.png

Fig. 3: Actual track

dottedBlockOneMeas.png

Fig. 4: Measured Track by the vision system

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Fig. 5: 5 axis direct deposition system

 

Fig. 6: Optomec LENS 750

SPONSORS

National Science Foundation, DoD, DoE, DTRA, NASA
Purdue Research Foundation
Indiana 21st Century Research and Technology Fund
Industrial Companies

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