University of Michigan

Other

Dm3d Technology, LLC

Lijun Song

Jyoti Mazumder

Jyotirmoy Mazumder

Cunshan Wang

A microstructure detector and in-situ method for real-time determination of the microstructure of a material undergoing alloying or other phase transformation. The method carried out by the detector includes the steps of: (a) detecting light emitted from a plasma plume created during phase transformation of a material; (b) determining at least some of the spectral content of the detected light; and (c) determining an expected microstructure of the transformed material from the determined spectral content. Closed loop control of the phase transformation process can be carried out using feedback from the detector to achieve a desired microstructure.

In-situ identification and control of microstructures produced by phase transformation of a material

An additive manufacturing process is monitored, in situ, using optical emission spectroscopy to analyze the composition, phase transformation or manufacturing defects. The system or method may include an analysis of contours of the plasma line intensity, or pre-processing of the plasma spectral line including signal-to-noise ratio analysis, baseline removal, line identification, line de-convolution and fitting. Improvements may additionally involve consideration of plasma parameters such as plasma spectral line intensity, line ratio, plasma temperature and electron density using high-resolution optical emission spectroscopy in both visible and ultraviolet regions. Parameters of the plasma may be determined using an intensity ratio of the ions or atoms emission lines, a FWHM of the line profile for electron density estimation, or a Boltzmann plot for plasma temperature estimation. One or more techniques may be used to monitor when there is a lack of deposition.

Smart additive manufacturing system (SAMS)

A direct metal deposition (DMD) process is stabilized by monitoring the temperature and the shape of the melt pool during deposition, applying a recursive least square (RLS) model estimation algorithm to adaptively identify process characteristics in accordance with the temperature and the shape of the melt pool, and delivering the process characteristics to a generalized predictive controller with input constraints to control the process. The process may be controlled by adjusting laser power or by adjusting the speed of the movement of the laser. In the preferred embodiment the temperature is monitored using a two-color pyrometer, and the shape of the melt pool is monitored by detecting the edge of the melt pool with a camera and/or photodetector.

Growing community of inventors

Ann Arbor, MI, United States of America

Lijun Song