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Clarifying the relationship between the man–machine environment and its impact on the tunnel wall drilling task performance (TWDTP) is crucial for enhancing the task performance. Based on a questionnaire survey, indicators of the man–machine environment that affect the TWDTP were proposed in this study, and exploratory factor analysis and a structural equation model were employed to examine the potential factors influencing the task performance and their degrees of influence. By comparing the discrepancy between the perceived performance and importance, the satisfaction of potential factors was evaluated, and the priority order for optimizing these factors was determined by considering the degree of influence and dissatisfaction. The results of survey data analysis based on actual tunnel drilling operation scenarios indicated that tools had the greatest impact on the TWDTP, followed by the quality of the physical environment, while human factors had the least influence on the task performance. Convenient functional maintenance is the key to improving the TWDTP, along with enhancing the quality of the working environment. Once these main aspects are optimized, it is important to consider additional factors such as availability of spare tools, efficient personnel organization, man–tool matching, and safety and health assurance. This research approach provides significant guidance in understanding the relationships between the man–machine environmental factors affecting the performance of complex engineering tasks and identifying key influencing factors, thus providing essential insights for optimizing the TWDTP. [ABSTRACT FROM AUTHOR]

Abstract: Hot compression tests of modified 2.25Cr–1Mo steel were conducted on a Gleeble-3500 thermo-mechanical simulator at the temperatures ranging from 1173 to 1473K with the strain rate of 0.01–10s−1 and the height reduction of 60%. Based on the experimental results, an artificial neural network (ANN) model and constitutive equations were developed to predict the hot deformation behavior of modified 2.25Cr–1Mo steel. A comparative evaluation of the constitutive equations and the ANN model was carried out. It was found that the relative errors based on the ANN model varied from −4.63% to 2.23% and those were in the range from −20.48% to 12.11% by using the constitutive equations, and the average root mean square errors were 0.62MPa and 7.66MPa corresponding to the ANN model and constitutive equations, respectively. These results showed that the well-trained ANN model was more accurate and efficient in predicting the hot deformation behavior of modified 2.25Cr–1Mo steel than the constitutive equations. [Copyright &y& Elsevier]

Abstract: Hot compression tests of T24 ferritic steel were carried out using Gleeble-3500 thermo mechanical simulator in the temperature range of 1323–1473K with the strain rate of 0.01–10s−1 and the height reduction of 60%. The flow behavior of T24 ferritic steel was characterized based on analysis of the true stress–strain curves. Constitutive equations incorporating the effects of temperature, strain rate and strain have been developed to model the hot deformation behavior of T24 ferritic steel. Material constants α, n, ln A and activation energy Q in the constitutive equations were calculated as a function of strain. The flow stress values of T24 ferritic steel predicted by the proposed constitutive equations show a good agreement with experimental results, which indicated that the developed constitutive equations could give an accurate and precise prediction for the flow stress of T24 ferritic steel. [Copyright &y& Elsevier]

ZrC- SiC powders are synthesized by high-temperature pyrolysis of hybrid liquid precursors, which are prepared from organic Zr-containing precursor ( PZC) and liquid polycarbosilane ( LPCS). Due to the excellent miscibility between PZC and LPCS, the hybrid liquid precursors are formed by dissolving PZC into LPCS without adding organic solvent. The viscosity and elemental content of Zr and Si of the hybrid precursors are readily adjustable by controlling the LPCS/ PZC mass ratio. SEM and TEM observations reveal that the ZrC- SiC powders pyrolyzed at 1550°C exhibit spherical morphology with characteristic dimension of less than 60 nm, and the two phases are uniformly distributed in composite powders. The advantage of the ZrC- SiC powders synthesized by this novel method is demonstrated by investigating the oxidation behavior of powders with different amount of SiC and ZrC. Below 700°C, ZrC quickly oxidizes to generate an almost nonprotective ZrO2 scale, whereas at ~ 1000°C, dense and protective SiO2 forms that improves the oxidation resistance of the ZrC- SiC composite powders. [ABSTRACT FROM AUTHOR]

Abstract: The true stress–strain data from isothermal hot compression tests on Gleeble-3500 thermo mechanical simulator, in a wide range of temperatures (1323–1473K) and strain rates (0.01–10s−1), were employed to establish the constitutive equations based on modified Zerilli–Armstrong and strain-compensated Arrhenius-type models respectively, and develop the artificial neural network model to predict the high-temperature flow stress of T24 steel. Furthermore, a comparative study has been made on the capability of the three models to represent the elevated temperature flow behavior of this steel. Suitability of the three models were evaluated by comparing the accuracy of prediction of deformation behavior, correlation coefficient and average absolute relative error of prediction, the number of material constants, and the time needed to evaluate these constants. The results showed that the predicted values by the modified Zerilli–Armstrong model could agree well with the experimental values except under the strain rate of 0.01s−1. The predicted flow stress of the other two models shows good agreement with the experimental data. However, the artificial neural network model could track the deformation behavior more accurately throughout the entire temperature and strain rate range though it is strongly dependent on extensive high quality data and characteristic variables and offers no physical insight. [Copyright &y& Elsevier]