Melts of polyether-ether-ketone (PEEK) with three kinds of average molecular weight are solidified by rapid compression from 0.1 to 2.0 GPa at 360 °C within 20 ms, and comparative samples are also made by rapid quenching and slow cooling of the same melts. Through XRD and DSC analyses and mechanical tests of the recovered samples, it is found that bulk materials of PEEK obtained by rapid compression exhibit a single amorphous phase with unique properties such as high thermodynamic stability, excellent friction and wear behaviour, considerable stiffness, exceptional ductile character and high impact toughness. These properties could be attributed to their homogeneous fine structure. A bulk and fully amorphous PEEK with 24 mm diameter and 12 mm thickness is prepared by rapid compression, which considerably exceeds the critical size in a conventional quenching method, thus, directly confirming that the size of the amorphous material is not limited by thermal conductivity in the rapid compression process.

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Although the known maximum total generalized correntropy (MTGC) and generalized maximum blakezisserman total correntropy (GMBZTC) algorithms can maintain good performance under the errors-in-variables (EIV) model disrupted by generalized Gaussian noise, their requirement for manual adjustment of parameters is excessive, greatly increasing the practical difficulty of use. To solve this problem, the total arctangent based on logical distance metric (TACLDM) algorithm is proposed by utilizing the advantage of few parameters in logical distance metric (LDM) theory and the convergence behavior is improved by the arctangent function. Compared with other competing algorithms, the TACLDM algorithm not only has fewer parameters, but also has better robustness to generalized Gaussian noise and significantly reduces the steady-state error. Furthermore, the analysis of the algorithm in the generalized Gaussian noise environment is analyzed in detail in this paper. Finally, computer simulations demonstrate the outstanding performance of the TACLDM algorithm and the rigorous theoretical deduction in this paper.

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