Shenglei Wu, Jianhui Liu, Yazhou Wang, Jumei Lu and Ziyang Zhang
Sufficient sample data are the necessary condition to ensure high reliability; however, there are relatively poor fatigue test data in the engineering, which affects fatigue…
Abstract
Purpose
Sufficient sample data are the necessary condition to ensure high reliability; however, there are relatively poor fatigue test data in the engineering, which affects fatigue life's prediction accuracy. Based on this, this research intends to analyze the fatigue data with small sample characteristics, and then realize the life assessment under different stress levels.
Design/methodology/approach
Firstly, the Bootstrap method and the principle of fatigue life percentile consistency are used to realize sample aggregation and information fusion. Secondly, the classical outlier detection algorithm (DBSCAN) is used to check the sample data. Then, based on the stress field intensity method, the influence of the non-uniform stress field near the notch root on the fatigue life is analyzed, and the calculation methods of the fatigue damage zone radius and the weighting function are revised. Finally, combined with Weibull distribution, a framework for assessing multiaxial low-cycle fatigue life has been developed.
Findings
The experimental data of Q355(D) material verified the model and compared it with the Yao’s stress field intensity method. The results show that the predictions of the model put forward in this research are all located within the double dispersion zone, with better prediction accuracies than the Yao’s stress field intensity method.
Originality/value
Aiming at the fatigue test data with small sample characteristics, this research has presented a new method of notch fatigue analysis based on the stress field intensity method, which is combined with the Weibull distribution to construct a low-cycle fatigue life analysis framework, to promote the development of multiaxial fatigue from experimental studies to practical engineering applications.
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Meiqun Yin and Lei Sheng
This paper aims to find the endogenous relationship between innovation input and corporate performance and deepen the study of innovation performance theory in industry and…
Abstract
Purpose
This paper aims to find the endogenous relationship between innovation input and corporate performance and deepen the study of innovation performance theory in industry and enterprise at the micro level.
Design/methodology/approach
This paper selects the firms listed on A shares in Shanghai and Shenzhen Stock Exchanges from 2009 to 2015 as samples. The authors cluster these samples according to the factors of production and classify the samples into three types: technology-intensive, capital-intensive and labor-intensive. After obtaining the samples and classifying them, the authors conduct a research on the endogenous relationship between the innovation input and the corporate performance through the simultaneous equations model and 3SLS estimation method. Meanwhile, they also make a study on the influence of executive incentive mechanism on the relationship between the innovation input and the corporate performance.
Findings
In technology-intensive industry, the increase of pre-innovation input will enhance the corporate performance in the current period, however, which will slow down the pace of innovation and lead to lower corporate performance in the future, and then increase innovation input again. In contrast, in capital-intensive industries, innovation input just improves corporate performance in the current period and the promotion of corporate performance will promote the intensity of innovation input in the future. With labor-intensive industries, innovation input also depends on early good returns, but innovation input has no significant impact on the corporate performance both at present and in the future. While in the executive incentive mechanism, salary incentive has a significant positive regulatory effect on the relationship between innovation input and corporate performance.
Originality/value
This paper presents a new research perspective on the relationship between innovation input and firm corporate performance, which is of great value to the listed company in balancing the R&D input with the company’s target performance and the design of executive incentive mechanism.