Search Results (2)

Gleeble thermomechanical simulators are widely utilized tools for the investigation of high-temperature deformation behavior in materials. However, temperature gradients that develop within the specimen during Gleeble compression tests have the potential to result in non-uniform deformation, which may subsequently impact the accuracy of the measured mechanical properties. This study presents an experimental and numerical investigation of the temperature fields in 2017-T4 aluminum alloy specimens prior to Gleeble compression tests at temperatures ranging from $300 °\mathrm{C} \mathrm{to} 500 °\mathrm{C}$ utilizing uniform temperature distribution (ISO-T) tungsten carbide anvils. The use of multiple thermocouples, welded to both the specimen and anvils, offers valuable insights into the temperature gradients and their evolutions. A coupled thermal–electrical finite-element model was developed in Abaqus for the purpose of simulating the resistive heating process. A user amplitude subroutine (UAMP) is implemented to regulate the heating based on a proportional–integral–derivative (PID) algorithm that modulates the current density to follow the specified temperature profile. The numerical results demonstrate that the temperature gradients within the specimen at the end of the heating process, reaching a temperature of $400 °\mathrm{C}$, are minimal, with values below $1.9 °\mathrm{C}$. This is in accordance with the experimental observations. The addition of graphite foils between the specimen and anvils has been shown to effectively reduce the gradients. The use of the measured anvil temperature as a boundary condition, rather than a constant value of $20 °\mathrm{C}$, has been demonstrated to improve the agreement between the simulated and experimental cooling curves. The modeling approach provides a framework for quantifying temperature gradients in Gleeble compression specimens and for assessing their impact on the measured constitutive response of materials at elevated temperatures. Full article

Large-volume cubic high-pressure apparatus is commonly used to produce synthetic diamond. Due to the high pressure, high temperature and alternative stresses in practical production, cracks often occur in the carbide anvil, thereby resulting in significant economic losses or even casualties. Conventional methods are unsuitable for crack detection of the carbide anvil. This paper is concerned with acoustic emission-based crack detection of carbide anvils, regarded as a pattern recognition problem; this is achieved using a microphone, with methods including sound pulse detection, feature extraction, feature optimization and classifier design. Through analyzing the characteristics of background noise, the cracked sound pulses are separated accurately from the originally continuous signal. Subsequently, three different kinds of features including a zero-crossing rate, sound pressure levels, and linear prediction cepstrum coefficients are presented for characterizing the cracked sound pulses. The original high-dimensional features are adaptively optimized using principal component analysis. A hybrid framework of a support vector machine with k nearest neighbors is designed to recognize the cracked sound pulses. Finally, experiments are conducted in a practical diamond workshop to validate the feasibility and efficiency of the proposed method. Full article