Jarred C. Heigel

Application of Finite Element, Phase-field, and CALPHAD-based Methods to Additive Manufacturing of Ni-based Superalloys

Numerical simulations are used in this work to investigate aspects of microstructure and microseg-regation during rapid solidification of a Ni-based superalloy in a laser powder bed fusion additive manufacturing process. Thermal modeling by finite element analysis simulates the laser melt pool, with surface temperatures in agreement with in situ thermographic measurements on Inconel 625. Geometric and thermal features of the simulated melt pools are extracted and used in subsequent mesoscale simulations. Solidification in the melt pool is simulated on two length scales. For the multicomponent alloy Inconel 625, microsegregation between dendrite arms is calculated using the Scheil-Gulliver solidification model and DICTRA software. Phase-field simulations, using Ni-Nb as a binary analogue to Inconel 625, produced microstructures with primary cellular/dendritic arm spacings in agreement with measured spacings in experimentally observed microstructures and a lesser extent of microsegregation than predicted by DICTRA simulations. The composition profiles are used to compare thermodynamic driving forces for nucleation against experimentally observed precipitates identified by electron and X-ray diffraction analyses. Our analysis lists the precipitates that may form from FCC phase of enriched interdendritic compositions and compares these against experimentally observed phases from 1 h heat treatments at two temperatures: stress relief at 1143 K (870 °C) or homogenization at 1423 K (1150 °C).

Measurement of the Melt Pool Length During Single Scan Tracks in a Commercial Laser Powder Bed Fusion Process

Contact author: jarred.heigel@nist.gov Certain commercial equipment, instruments, or materials are identified in this paper in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited. ABSTRACT This work presents high speed thermographic measurements of the melt pool length during single track laser scans on nickel alloy 625 substrates. Scans are made using a commercial laser powder bed fusion machine while measurements of the radiation from the surface are made using a high speed (1800 frames per second) infrared camera. The melt pool length measurement is based on the detection of the liquidus-solidus transition that is evident in the temperature profile. Seven different combinations of programmed laser power (49 W to 195 W) and scan speed (200 mm/s to 800 mm/s) are investigated and numerous replications using a variety of scan lengths (4 mm to 12 mm) are performed. Results show that the melt pool length reaches steady state within 2 mm of the start of each scan. Melt pool length increases with laser power, but its relationship with scan speed is less obvious because there is no significant difference between cases performed at the highest laser power of 195 W. Although keyholing appears to affect the anticipated trends in melt pool length, further research is required.

The Effects of Emissivity and Camera Point Spread Function on the Temperature Measurement of Segmented Chip Formation Using Infrared Thermography

This paper uses simulation to investigate measurement errors resulting from the camera point spread function (PSF) when measuring the temperature of segmented chip formation using infrared (IR) thermography. The PSF of the IR camera effectively filters the results which can cause significant errors due to the large temperature gradients and abrupt transitions between features and their corresponding emissivity values. The different emissivity values of the tool, workpiece, chip body, and shear band affect the apparent difference in the emitted energy of these different features. This decreases the measured temperature in the regions of most interest: along the tool-chip interface and the periodic shear zone. The method in this study creates an appropriate emissivity map from post-process measurements and applies it to results from the temperature distribution of the cutting zone predicted by commercial finite element analysis (FEA) software. Comparisons between the simulation results and experiment results show that the emissivity values obtained form the post process chip analysis lead to good agreement. The resulting radiant intensity distribution becomes the input for an IR camera simulation module developed by the authors and presented in earlier work [1]. The earlier work used the true temperature distribution predicted by the FEA as the simulation module input, and did not incorporate the IR camera’s PSF. Implementation of the actual IR camera’s PSF allows the simulation module to more accurately represent the measurements of the IR camera and ultimately allow the comparison of the simulation results to the measurement results. Simulation results show that the PSF accounts for 45% of the 42 °C radiance temperature error at the tool-chip contact along the rake face. The PSF accounts for approximately 15% of the 46 °C radiance temperature measurement error at a point in the center of the catastrophic shear band. These errors consider the effects of motion blur (integration time) and magnification (size-of-source), as described in the earlier work [1].Copyright

The Effects of Integration Time and Size-of-Source on the Temperature Measurement of Segmented Chip Formation Using Infrared Thermography

This paper illustrates the errors due to integration time and size-of-source effects when measuring the temperature of segmented chip formation using infrared (IR) thermography. Segmented chip formation involves narrow periodic shear bands that experience rapid heating and move at high velocities and accelerations. As a result, the values of the measured temperatures depend strongly on the temporal and spatial measurement window used. In this study, an ideal infrared camera is simulated to understand the effects of integration time and size-of-source on the measurement. This analysis does not consider the temporal and spatial transfer functions of the camera system, thus simplifying the analysis to be applicable to all IR thermography users. Incorporating appropriate transfer functions would make the analysis specific to a given camera system. Finite element analysis (FEA) simulation results provide a reference cutting process which is manipulated to mimic motion blur and size-of-source effects. For this purpose, the FEA results adequately represent the cutting process with rapid heating and high chip velocities. For the studied cases, size-of-source has relatively little impact on the measurement results when compared to the effects of integration time. Results show integration times from 1 μs to 90 μs significantly affect the measurement results. The maximum temperature measured by the simulated IR camera decreases from an FEA maximum of 735 °C to 668 °C at 90 μs integration time. Integration time significantly affects temperature measurement in the periodic shear band but does not significantly affect the simulated measurement error of the chip temperature near the tool rake face.© 2009 ASME

Thermo-Mechanical Modeling of Thin Wall Builds using Powder Fed Directed Energy Deposition

This chapter presents a model to predict the temperature history and resulting distortion that occurs in parts processed using Directed Energy Deposition (DED). DED has similarities to powder bed fusion processes, such as the use of a high intensity energy source to melt and fuse powder, creating fully dense part. However, DED processes inject powder into the melt pool and does not melt selected areas in a pre-placed bed of powder. Consequently, the entire surface area of the part produced with DED is exposed and an increased amount of energy is expelled though radiation and convection. This effect becomes even more pronounced during the creation of thin walled structures. Since the thermo-mechanical behavior of any additive processes is dependent on accurately accounting for the energy balance during the process, a greater effort is required to develop an accurate surface convection model of the DED process. This chapter expands upon earlier work by the author by presenting and discussing the initial modeling efforts that led to the conclusion that an empirically-based convection model is required to model the DED process. The chapter concludes by presenting the results from the new model for a variety of deposition cases and discusses how well the results compare with in situ measurements of the process.

Assessing the use of an infrared spectrum hyperpixel array imager to measure temperature during additive and subtractive manufacturing

Accurate non-contact temperature measurement is important to optimize manufacturing processes. This applies to both additive (3D printing) and subtractive (material removal by machining) manufacturing. Performing accurate single wavelength thermography suffers numerous challenges. A potential alternative is hyperpixel array hyperspectral imaging. Focusing on metals, this paper discusses issues involved such as unknown or changing emissivity, inaccurate greybody assumptions, motion blur, and size of source effects. The algorithm which converts measured thermal spectra to emissivity and temperature uses a customized multistep non-linear equation solver to determine the best-fit emission curve. Emissivity dependence on wavelength may be assumed uniform or have a relationship typical for metals. The custom software displays residuals for intensity, temperature, and emissivity to gauge the correctness of the greybody assumption. Initial results are shown from a laser powder-bed fusion additive process, as well as a machining process. In addition, the effects of motion blur are analyzed, which occurs in both additive and subtractive manufacturing processes. In a laser powder-bed fusion additive process, the scanning laser causes the melt pool to move rapidly, causing a motion blur-like effect. In machining, measuring temperature of the rapidly moving chip is a desirable goal to develop and validate simulations of the cutting process. A moving slit target is imaged to characterize how the measured temperature values are affected by motion of a measured target.

Recent experiments assessing the uncertainty of metal cutting temperature measurements when using the NIST high-speed dual-spectrum optical system

Process models, including finite element modeling simulations, are important for optimizing the metal cutting process, allowing industry to make parts faster, better, and at less cost. Measurements of the process can be used to improve and verify the accuracy of these models. There are many error sources when using infrared radiation thermography to measure the temperature distribution of the tool, workpiece, and chip during metal cutting. Furthermore, metal cutting presents unique measurement challenges due to factors such as the high magnification required, high surface speeds, micro-blackbody effects, and changing emissivity as chips form. As part of an ongoing effort to improve our understanding of uncertainties associated with these thermographic measurements, two sets of experiments were performed. One set explored how well the surface temperature of the cutting tool accurately reflects the internal temperature. This was accomplished by simultaneously measuring the temperature using both a thermal camera and a thermocouple embedded within the cutting tool. The other set investigated correcting for motion blur, point spread function, and a less than ideal range of sensitivity of the thermal camera when measuring the shear zone temperature of the chip. In theory, this correction could be performed using deconvolution. Unfortunately, deconvolutions are sensitive to noise and it is difficult to gauge the uncertainty of the computed values. Thus, convolutions of various assumed inputs were computed and compared to the measured temperatures. Assumed inputs which yielded a good fit to the measured temperatures were considered candidate values. The range of those candidate values yields a measure of the uncertainty of the calculation.