Search

In traditional methods, features extracted from the hysteresis loop are manually analyzed to establish a model for predicting tensile force in one ferromagnetic material. However, the hysteresis loop is sensitive to material type, thus increasing the difficulty in predicting tensile forces in two materials. Moreover, manual feature extraction is time consuming. It is necessary to complete automatic feature extraction and elimination of the influence of material type on the hysteresis loop in order to automatically establish a prediction model for tensile force prediction in two materials. In this article, an evolutionary computation-based method was proposed to perform the above-mentioned two tasks simultaneously. The proposed method involves three steps. First, hysteresis loops of two materials under different tensile forces were measured. Second, the measured hysteresis loops were transformed into corresponding transformed ascending hysteresis curves through a transformation operation of the curve of hysteresis loop change (CHLC) for eliminating the influence of material type on the hysteresis loop. Third, an improved bat algorithm was proposed to eliminate further the influence of material type on the transformed ascending hysteresis curve. The improved bat algorithm iteratively gave the position of a material type-insensitive feature on the transformed ascending hysteresis curve. The material type-insensitive feature-based neural network was used to predict tensile forces in two materials. Experimental results verified the proposed method. The motivation of obtaining a general tensile force prediction model, the applicability of the proposed method in more than two materials, and the influence of CHLC transformation operation on the improved bat algorithm and neural network are also discussed. [ABSTRACT FROM AUTHOR]

In the tension measurement by using magneto-elastic tension sensor, generally the changes of hysteresis loop with tension is applied. But due to the changes in environment temperature, the changes of hysteresis loop contain not only the tension information but also the temperature influence. In order to eliminate the influence of temperature change, a temperature compensation method used in rod-like structure tension measurement is proposed in this paper. The proposed method involves three key steps. First, a curve of hysteresis loop change (CHLC) is defined to reflect the influence of temperature and tension. It contains two components: 1) the tension component of CHLC and 2) the temperature component of CHLC which is an unknown nonlinear curve related to the temperature changes. Second, a prediction model of temperature component of CHLC based on the neural network is proposed. Finally, the temperature influence on CHLC is eliminated by calculating the difference between the CHLC and predicted temperature component of CHLC, and the predicted tension component of CHLC is obtained. The experimental results show that the CHLC can be used as the basis of the temperature compensation method. Moreover, the temperature influence can be analyzed by CHLC when the tension is invariable, whereas the tension influence can also be analyzed when the temperature is invariable. The temperature component of CHLC can be obtained rapidly and accurately by the neural network-based prediction model with a prediction error less than 10−5. The degree of CHLC distortion caused by temperature change is reduced from 10−2 to 10−5 with the proposed temperature compensation method. [ABSTRACT FROM AUTHOR]

In recent years, non-destructive evaluation (NDE) has attracted increasing attentions. Different electromagnetic signals have been used for studying the microstructure of materials. However, the features extracted for NDE share the limitation that these features were separately designed and roughly assembled. In order to overcome this limitation, a group of features that include the basic binary pattern of eddy current (BPE) and three improved features named simple-BPE, spat-BPE, and direction BPE (DBPE) are proposed to predict the residual strain (RS). Four features are designed through encoding the modulus variation of multi-frequency, multi-spatial, and multi-direction eddy current signal. In the experiment part, two types of experiments are used to test our four features. Based on the results, the performance of BPE-based methods outperforms the conventional features. Within four proposed features, the best prediction accuracy (0.03057997 for C45E4 and 0.03167161 for AlCu4Mg1) is obtained by adopting spat-BPE; the most efficient feature is simple-BPE, and a balanced performance is achieved by BPE and DBPE, performed not as good as the other three features. It has been proved that our binary-encoding-based features are beneficial to building the relationship between the NDE signal and the property of material (e.g., RS) and could be applied to a wide range of applications. [ABSTRACT FROM PUBLISHER]