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In materials that exhibit elastic and plastic behavior, known as elastoplastic materials, residual stresses can accumulate when these materials experience plastic deformation. This deformation arises from either high levels of shearing stress or significant strains. Residual stresses are internal stresses that persist within a material after removing the external force causing deformation. This phenomenon is demonstrated when observing the behavior of a shaft under torque; notably, the...
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The mechanical characteristics of steel are assessed through various tests that evaluate its strength, toughness, and flexibility. These tests include tension, torsion, impact, bending, and hardness assessments, each providing crucial information about steel's suitability for specific applications.
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Related Experiment Video

Updated: Jul 27, 2025

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation
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Aero-Engine Blade Cryogenic Cooling Milling Deformation Simulation and Process Parameter Optimization.

Ting Chen1, Yun Xu1, Bo Huang1

  • 1School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.

Materials (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

Low-temperature milling significantly reduces aero-engine blade deformation by over 31.36%. Optimized parameters using a particle swarm algorithm ensure blade profile accuracy within allowable error margins.

Keywords:
aero-engine bladecryogenic machiningparticle swarm algorithmsimulationstitanium

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Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Aerospace Engineering

Background:

  • Aero-engine blade machining faces challenges with residual stress, milling forces, and thermal deformation impacting profile accuracy.
  • Accurate blade profiles are critical for engine performance and longevity.

Purpose of the Study:

  • To investigate the impact of cryogenic milling and process parameters on aero-engine blade deformation.
  • To develop a predictive model and optimize machining parameters for enhanced blade profile accuracy.

Main Methods:

  • Simulations using DEFORM 11.0 and ABAQUS 2020 to analyze heat-force fields and blade deformation.
  • Design of experiments including single-factor control and Box-Behnken Design (BBD) for parameter analysis.
  • Application of multiple quadratic regression and particle swarm optimization (PSO) algorithm for parameter optimization.

Main Results:

  • Low-temperature milling (-190 °C to -10 °C) reduced deformation rates by over 31.36% compared to dry milling (10 °C to 20 °C).
  • Initial optimization attempts showed blade profile margins exceeding the ±50 µm tolerance.
  • PSO algorithm optimization achieved a maximum deformation of 0.0396 mm within the -160 °C to -180 °C range, meeting accuracy requirements.

Conclusions:

  • Cryogenic milling is an effective strategy for reducing aero-engine blade deformation during machining.
  • Mathematical modeling and PSO-based optimization successfully identified process parameters to ensure high blade profile accuracy.
  • The optimized cryogenic milling process meets stringent aerospace manufacturing tolerances for blade profiles.