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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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In precipitation gravimetry, the precipitating agent should react specifically or selectively with the analyte. While a specific reagent reacts with the analyte alone, a selective reagent can react with a limited number of chemical species.
The obtained precipitate should be either a pure substance of known composition or easily converted to one by a simple process, such as ignition or drying. In addition, the precipitate should be insoluble and easily filterable. In general, filterability...
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A Study on Establishing a Microstructure-Related Hardness Model with Precipitate Segmentation Using Deep Learning

Chan Wang1, Duoqi Shi1,2, Shaolin Li1,2

  • 1School of Energy and Power Engineering, Beihang University, Beijing 100191, China.

Materials (Basel, Switzerland)
|March 14, 2020
PubMed
Summary
This summary is machine-generated.

This study developed a deep learning method to analyze microstructures in GH4720Li superalloys. The new approach accurately models hardness based on precipitate size and fraction, improving material performance predictions.

Keywords:
deep learning methoddifferent generations of γ’ precipitateslarge-area SEM imagesmicrostructure-related hardness modelγ’ coarsening

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Area of Science:

  • Materials Science
  • Metallurgy
  • Computational Materials Science

Background:

  • Traditional methods for analyzing microstructure in Ni-based superalloys like GH4720Li are limited.
  • Distinguishing precipitate generations using standard thresholding is difficult and requires manual input.
  • Accurate microstructural characterization is crucial for predicting alloy performance.

Purpose of the Study:

  • To establish a microstructure-related hardness model for GH4720Li.
  • To develop an automated method for extracting precipitate parameters from SEM images.
  • To investigate precipitate stability and its effect on hardness at elevated temperatures.

Main Methods:

  • Utilized scanning electron microscopy (SEM) with the AuTomated Large Area Scanning (ATLAS) module.
  • Applied a U-Net deep learning model for automated segmentation and parameter extraction of γ' precipitates.
  • Employed the Lifshitz-Slyozov encounter modified (LSEM) model to predict precipitate coarsening.

Main Results:

  • The U-Net model accurately segmented and extracted parameters of different γ' precipitate generations.
  • Primary and secondary precipitates exhibited good stability at high temperatures.
  • Tertiary precipitates coarsened selectively, with behavior predictable by the LSEM model.
  • A decrease in hardness correlated with γ' precipitate coarsening.

Conclusions:

  • A novel microstructure-related hardness model for GH4720Li was established.
  • The model effectively correlates microstructural features with alloy hardness.
  • This approach enables accurate hardness prediction for GH4720Li under various microstructural conditions.