skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: A Review of Constitutive Models and Thermal Properties for Nickel-based Superalloys Across Machining-Specific Regimes
Nickel-based superalloys (Ni-alloys) are widely used in flight-critical aeroengine components because of their excellent material properties at high temperatures such as yield strength, ductility, and creep resistance. However, these desirable high-temperature properties also make Ni-alloys very difficult to machine. This paper provides an overview and benchmarking of various constitutive models to provide the process modeling community with an objective comparison between various calibrated material models, to increase the accuracy of process model predictions for machining of Ni-alloys. Various studies involving the Johnson-Cook model and the calibration of its constants in finite element simulations are discussed. Significant discrepancies exist between researchers' approaches to calibrating constitutive models. Moreover, this paper provides a comprehensive overview of pedigreed physical material properties for a range of Ni-alloys. In this context, the variation of thermal properties and thermally induced stresses over machining temperature regimes are modeled for a variety of Ni-alloys. The chemical compositions and applications for a range of relevant Ni-alloys are also explored. Overall, this manuscript identifies the need for more comprehensive analysis and process-specific characterization of thermomechanical properties for difficult-to-machine Ni-alloys to improve machining performance and aeroengine component quality.  more » « less
Award ID(s):
2143806
PAR ID:
10394028
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Journal of Manufacturing Science and Engineering
ISSN:
1087-1357
Page Range / eLocation ID:
1 to 112
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Machining processes involve various sources of uncertainty which lead to inaccurate interpretation of results in the surface integrity of machined products. This work presents a physics-informed, data-driven modeling framework for achieving comprehensive uncertainty quantification (UQ) of the impact of process and material variability on machining-induced residual stress (RS). Uncertainty due to the variation in bulk material properties and model input parameters in machining are considered. Preliminary results showed that variations in calibration parameters have a substantial effect on modeling RS, while the variation in material properties has a smaller effect. Further research directions for UQ in machining are also outlined. 
    more » « less
  2. Refractory high entropy alloys (RHEAs) have gained significant attention in recent years as potential replacements for Ni-based superalloys in gas turbine applications. Improving their properties, such as their high-temperature yield strength, is crucial to their success. Unfortunately, exploring this vast chemical space using exclusively experimental approaches is impractical due to the considerable cost of the synthesis, processing, and testing of candidate alloys, particularly at operation-relevant temperatures. On the other hand, the lack of reasonably accurate predictive property models, especially for high-temperature properties, makes traditional Integrated Computational Materials Engineering (ICME) methods inadequate. In this paper, we address this challenge by combining machine-learning models, easy-to-implement physics-based models, and inexpensive proxy experimental data to develop robust and fast-acting models using the concept of Bayesian updating. The framework combines data from one of the most comprehensive databases on RHEAs (Borg et al., 2020) with one of the most widely used physics-based strength models for BCC-based RHEAs (Maresca and Curtin, 2020) into a compact predictive model that is significantly more accurate than the state-of-the-art. This model is cross-validated, tested for physics-informed extrapolation, and rigorously benchmarked against standard Gaussian process regressors (GPRs) in a toy Bayesian optimization problem. Such a model can be used as a tool within ICME frameworks to screen for RHEAs with superior high-temperature properties. The code associated with this work is available at: https://codeocean.com/capsule/7849853/tree/v2. 
    more » « less
  3. Abstract In recent years, semiconductors, electronics, optics, and various other industries have seen a significant surge in the use of sapphire materials, driven by their exceptional mechanical and chemical properties. The machining of sapphire surfaces plays a crucial role in all these applications. However, due to sapphires’ exceptionally high hardness (Mohs hardness of 9, Vickers hardness of 2300) and brittleness, machining them often presents challenges such as microcracking and chipping of the workpiece, as well as significant tool wear, making sapphires difficult to cut. To enhance the machining efficiency and machined surface integrity, ultrasonic vibration-assisted (UV-A) machining of sapphire has already been studied, showing improved performance with lower cutting force, better surface finish, and extended tool life. Scribing tests using a single-diamond tool not only are an effective method to understand the material removal mechanism and deformation characteristics during such UV-A machining processes but also can be used as a potential process for separating IC chips from wafers. This paper presents a comprehensive study of the UV-A scribing process, aiming to develop an understanding of sapphire’s material removal mechanism under varying ultrasonic power levels and cutting tool geometries. In this experimental investigation, the effect of five different levels of ultrasonic power and three different cutting tool tip angles at various feeding depths on the scribe-induced features of the sapphire surface has been presented with a quantitative and qualitative comparison. The findings indicate that at feeding depths less than 6 μm, UV-A scribing with 40–80% ultrasonic power can reduce cutting force up to 50% and thus improve scribe quality. However, between feeding depths of 6 to 10 μm, this advantage of using ultrasonic vibration gradually diminishes. Additionally, UV-A scribing with a smaller tool tip angle (60°) was found to lower cutting force by 65% and improve scribe quality, effectively inhibiting residual stress formation and microcrack propagation. Furthermore, UV-A scribing also facilitated higher critical feeding depths at around 10 μm, compared to 6 μm in conventional scribing. 
    more » « less
  4. Abstract This study employs a high-fidelity numerical framework to determine the plastic material flow patterns and temperature distributions that lead to void formation during friction stir welding (FSW), and to relate the void morphologies to the underlying alloy material properties and process conditions. Three aluminum alloys, viz., 6061-T6, 7075-T6, and 5053-H18, were investigated under varying traverse speeds. The choice of aluminum alloys enables the investigation of a wide range of thermal and mechanical properties. The numerical simulations were validated using experimental observations of void morphologies in these three alloys. Temperatures, plastic strain rates, and material flow patterns are considered. The key results from this study are as follows: (1) the predicted stir zone and void morphology are in good agreement with the experimental observations, (2) the temperature and plastic strain rate maps in the steady-state process conditions show a strong dependency on the alloy type and traverse speeds, (3) the material velocity contours provide a good insight into the material flow in the stir zone for the FSW process conditions that result in voids as well as those that do not result in voids. The numerical model and the ensuing parametric studies presented in this study provide a framework for understanding material flow under different process conditions in aluminum alloys and potentially in other alloys. Furthermore, the utility of the numerical model for making quantitative predictions and investigating different process parameters to reduce void formation is demonstrated. 
    more » « less
  5. Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures, thermal processing (heat treatment) history and the composition of alloys. Due to computational costs and lengthy experimental procedures, obtaining the thermal conductivity for novel alloys, particularly parts made with additive manufacturing, is difficult and it is almost impossible to optimize the compositional space for an absolute targeted value of thermal conductivity. To address these difficulties, a machine learning method is explored to predict the TC of additive manufactured alloys. To accomplish this, an extensive thermal conductivity dataset for additively manufactured alloys was generated for several AM alloy families (nickel, copper, iron, cobalt-based) over various temperatures (300–1273 K). This unique dataset was used in training and validating machine learning models. Among the five different regression machine learning models trained with the dataset, extreme gradient boosting performs the best as compared with other models with an R2 score of 0.99. Furthermore, the accuracy of this model was tested using Inconel 718 and GRCop-42 fabricated with laser powder bed fusion-based additive manufacture, which have never been observed by the extreme gradient boosting model, and a good match between the experimental results and machine learning prediction was observed. The average mean error in predicting the thermal conductivity of Inconel 718 and GRCop-42 at different temperatures was 3.9% and 2.08%, respectively. This paper demonstrates that the thermal conductivity of novel AM alloys could be predicted quickly based on the dataset and the ML model. 
    more » « less