Assessment of grain structure evolution with resonant ultrasound spectroscopy in additively manufactured nickel alloys

Rossin, Jeff; Goodlet, Brent; Torbet, Chris; Musinski, William D.; Cox, Marie E.; Miller, Jonathan D.; Groeber, Michael A.; Mayes, Alex; Biedermann, Eric; Smith, Stephen W.; Daly, Samantha; Pollock, Tresa M.

doi:10.1016/j.matchar.2020.110501

Cited by 29 publications

(9 citation statements)

References 23 publications

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“…The application of spectroscopy in AM continues to evolve and new applications keep emerging. Resonant ultrasound spectroscopy has been employed for detecting part to part microstructure variability between built AM components [ 115 ]. Laser-induced breakdown spectroscopy has provided real-time quantitative multi-elemental analysis during the manufacturing process [ 116 , 117 ].…”

Section: Discussionmentioning

confidence: 99%

Application of Spectroscopy in Additive Manufacturing

2021

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Additive manufacturing (AM) is a rapidly expanding material production technique that brings new opportunities in various fields as it enables fast and low-cost prototyping as well as easy customisation. However, it is still hindered by raw material selection, processing defects and final product assessment/adjustment in pre-, in- and post-processing stages. Spectroscopic techniques offer suitable inspection, diagnosis and product trouble-shooting at each stage of AM processing. This review outlines the limitations in AM processes and the prospective role of spectroscopy in addressing these challenges. An overview on the principles and applications of AM techniques is presented, followed by the principles of spectroscopic techniques involved in AM and their applications in assessing additively manufactured parts.

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Section: Discussionmentioning

confidence: 99%

Application of Spectroscopy in Additive Manufacturing

2021

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“…Let's consider that we have initially selected N, as defined in Eq. (11), to be a specific number. This results in n eigenvalues, where n is defined in Eq.…”

Section: Analyzing the Trace Of The Matrix γmentioning

confidence: 99%

“…These include pioneering works to find elastic constants [1][2][3][4][5][6][7], piezoelectric coefficients [6], crystallographic orientation [8,9], and dislocation density [10]. With the widespread adoption of additive manufacturing techniques in the industry, RUS has emerged as a valuable tool for detecting the microstructural effects that arise from heat treatment in 3-D printed metals [11]. Additionally, RUS has proven effective in controlling the quality of complex additively manufactured components [12].…”

Section: Introductionmentioning

confidence: 99%

Resonant ultrasound spectroscopy: How much information lies in higher frequencies

Farzbod

2022

The Journal of the Acoustical Society of America

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Resonance ultrasound spectroscopy (RUS) is a well-established experimental technique for measuring all the elastic properties and a few anelastic properties of a material. A sample with known geometry is excited to vibrate in a wide range of frequencies, and its resonant frequencies are detected. The resonant frequencies depend on elastic constants; as such, a complete set of elastic constants can be extracted from the resonant frequency values. In this work, we investigate first the sensitivity of resonant frequencies to some of the elastic constants, and second, how much data are in the resonant frequencies as they go higher. It has been speculated that the higher modes have more info about the off-diagonal elastic constants and shear modes of vibrations. We will investigate this hypothesis and study resonant frequencies' limiting behavior.

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“…Additive manufacturing (AM) techniques have evolved from rapid prototyping techniques to a widely used manufacturing process with unique benefits over conventional processes [ 1 , 2 ]. According to the ISO/ASTM 52900 [ 3 ] norm, additive manufacturing (AM) is the general term for techniques in creating components by adding material repeatedly (layer-by-layer) while adhering to a computer-aided design model.…”

Section: Introductionmentioning

confidence: 99%

A Smart, Data-Driven Approach to Qualify Additively Manufactured Steel Samples for Print-Parameter-Based Imperfections

Alaparthi,

Subadra,

Sheikhi

2024

Materials

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With additive manufacturing (AM) processes such as Wire Arc Additive Manufacturing (WAAM), components with complex shapes or with functional properties can be produced, with advantages in the areas of resource conservation, lightweight construction, and load-optimized production. However, proving component quality is a challenge because it is not possible to produce 100% defect-free components. In addition to this, statistically determined fluctuations in the wire quality, gas flow, and their interaction with process parameters result in a quality of the components that is not 100% reproducible. Complex testing procedures are therefore required to demonstrate the quality of the components, which are not cost-effective and lead to less efficiency. As part of the project “3DPrintFEM”, a sound emission analysis is used to evaluate the quality of AM components. Within the scope of the project, an approach was being developed to determine the quality of an AM part dependent not necessarily on its geometry. Samples were produced from WAAM, which were later cut and milled to precision. To determine the frequencies, the samples were put through a resonant frequency test (RFM). The unwanted modes were then removed from the spectrum produced by the experiments by comparing it with FEM simulations. Later, defects were introduced in experimental samples in compliance with the ISO 5817 guidelines. In order to create a database of frequencies related to the degree of the sample defect, they were subjected to RFM. The database was further augmented through frequencies from simulations performed on samples with similar geometries, and, hence, a training set was generated for an algorithm. A machine-learning algorithm based on regression modelling was trained based on the database to sort samples according to the degree of flaws in them. The algorithm’s detectability was evaluated using samples that had a known level of flaws which forms the test dataset. Based on the outcome, the algorithm will be integrated into an equipment developed in-house to monitor the quality of samples produced, thereby having an in-house quality assessment routine. The equipment shall be less expensive than conventional acoustic equipment, thus helping the industry cut costs when validating the quality of their components.

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Assessment of grain structure evolution with resonant ultrasound spectroscopy in additively manufactured nickel alloys

Cited by 29 publications

References 23 publications

Application of Spectroscopy in Additive Manufacturing

Application of Spectroscopy in Additive Manufacturing

Resonant ultrasound spectroscopy: How much information lies in higher frequencies

A Smart, Data-Driven Approach to Qualify Additively Manufactured Steel Samples for Print-Parameter-Based Imperfections

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