Search Results (6)

In complex environments, traditional device-free localization (DFL) methods based on received signal strength (RSS) encounter difficulties in simultaneously achieving high accuracy and efficiency due to multipath effects and noise interference. These methods typically depend on convex sparsity regularization, which, despite its computational convenience, is insufficient in capturing the sparsity of signals. In contrast, non-convex sparsity regularization methods, while theoretically more capable of approximating ideal sparsity, are associated with higher computational complexity and a greater likelihood of getting stuck in local optima. To address these issues, this study proposes a synthetic model based on a novel weakly convex penalty function called Springback. This model combines a compression term (${\ell }_1$) that promotes sparsity and a rebound term (${\ell }_2$) that preserves signal amplitude, adjusting parameters to balance sparsity and computational complexity. Furthermore, to tackle the low efficiency of traditional synthetic models when dealing with large-scale data, we introduce a Springback-transform model based on an analytical transform learning framework. This model can directly extract sparse features from signals, avoiding the complex computational processes inherent in traditional synthetic models. Both models are solved using a difference of convex algorithm (DCA), significantly improving positioning accuracy and computational efficiency. Experimental results demonstrate that the proposed models exhibit high accuracy, low positioning error, and a short computation time across various environments, outperforming other state-of-the-art models. These achievements offer a new solution to the problem of DFL in complex environments, with high practical value and application prospects. Full article

An epoxy-based shape memory polymer (SMP) is synthesized and examined for its deterioration in shape fixity due to springback and isothermal viscoelastic recovery at different ambient temperatures. Shape fixity depends not only on material properties but also on programming conditions. A constitutive finite deformation model is incorporated to predict the behavior of the proposed SMP and find maximum shape fixity. A programming approach is followed in which, in contrast to hot programming, the SMPs are neither heated before deformation nor cooled afterward but are deformed at ambient temperature and then stress-relaxed. The proximity of the programming temperature to the glass transition temperature plays a crucial role in determining the shape fixity of SMP. It has been found that the SMP with a glass transition temperature of 42.9 °C can achieve maximum shape fixity of 92.25% when programmed at 23 °C with 100 min stress relaxation time. Thermal contraction and dynamic tests are performed in the Dynamic Mechanical Analyzer (DMA) to determine structural relaxation properties and distinguish the programming temperature in the cold, warm or hot temperature zone. The shape memory tests are carried out using temperature-controlled UTM to determine the shape fixity and shape recovery of SMP. The SMPs are subjected to a full thermomechanical cycle with different stress relaxation times and programming temperatures. Full article

There is a time-dependent springback phenomenon seen during the process of the bending-straightening of slender shafts, which has a great influence on the evaluation of straightness after straightening, creating a risk of misjudgment. This paper presents a dynamic evaluation method of straightness considering time-dependent springback in the bending-straightening process. Firstly, based on viscoelastic mechanics and bending-straightening, the influencing factors of time-dependent springback were analyzed on the basis of certain assumptions, including straightening stroke.${\delta }_C$, fulcrum distance L, instantaneous springback ${\delta }_b$, straightening time $t_s$, and straightening force $F_{max}$. As the main part of the proposed dynamic evaluation method, the GA-BP neural network is used to establish a model for fast prediction of time-dependent springback in straightening, and it is compared with the linear regression model. The maximum prediction error of the GA-BP model was 0.0038 mm, which was much lower than that of the regression model, at 0.014 mm. The root mean square error (RMSE) of the GA-BP model was 0.0042, and that of the regression model was 0.0098. Finally, the effectiveness of the dynamic straightness evaluation method considering time-dependent springback is verified by experiments. Finally, the sensitivity and relative importance of the influencing factors are analyzed, and the order is ${\delta }_C>t_s>F_{max}>L>{\delta }_b$. Full article

Titanium bent tubular parts attract extensive applications, thus meeting the ever-growing demands for light weight, high reliability, and long service life, etc. To improve bending limit and forming quality, local-heat-assisted bending has been developed. However, significant springback seriously reduces the dimensional accuracy of the bent tubular parts even under elevated forming temperatures, and coupled thermal-mechanical working conditions make springback behavior more complex and difficult to control in warm bending of titanium tubular materials. In this paper, using warm bending of thin-walled commercial pure titanium tube as a case, a coupled thermal-mechanical finite element model of through-process heating-bending-unloading is constructed and verified, for predicting the springback behavior in warm bending. Based on the model, the time-dependent evolutions of springback angle and residual stress distribution during thermal-mechanical unloading are studied. In addition, the influences of forming temperature and bending angle on springback angle, thickness variation, and cross-section flattening of bent tubes are clarified. This research provides a fundamental understanding of the thermal-mechanical-affected springback behavior upon local-heat-assisted bending for improving the forming accuracy of titanium bent tubular parts and structures. Full article

In this study, the time-dependent mechanical behavior of the magnesium alloy sheet (AZ31B) was investigated through the creep and stress relaxation tests with respect to the temperature and pre-strain. The microstructure changes during creep and stress relaxation were investigated. As the tensile deformation increased in the material, twinning and dynamic recrystallization occurred, especially after the plastic instability. As a result, AZ31B showed lower resistance to creep and stress relaxation due to dynamic recrystallization. Additionally, time-dependent springback characteristics in the V- and L-bending processes concerning the holding time and different forming conditions were investigated. We analyzed changes of microstructure at each forming temperature and process. The uniaxial tensile creep test was conducted to compare the microstructures in various pre-strain conditions with those at the secondary creep stage. For the bending process, the change of the microstructure after the forming was compared to that with punch holding maintained for 1000 s after forming. Due to recrystallization, with the holding time in the die set of 60 s, the springback angle decreased by nearly 70%. Increased holding time in the die set resulted in a reduced springback angle. Full article

Warm U-draw bending tests were performed on a 5182 aluminum alloy under isothermal and non-isothermal conditions, and the amounts of springback under the corresponding conditions were measured. Finite element method analyses were then conducted to calculate the tangential stress distribution on the cross-section of the sheet during the warm forming process. It was found that the experimentally measured springback values were proportionally related to the differences in the amounts of tangential stresses at the top and bottom layers of the sheet section. A functional model that can account for the correlation between the amount of springback and the difference in tangential stresses at the top and bottom layers of the sheet section was derived based on an Euler beam and a nonlinear flow stress model with temperature and strain rate dependencies. The developed model, which can predict springback behavior using only results of forming analyses of warm formed aluminum alloy sheets, is anticipated to provide for advancements in the understanding of springback behavior at warm temperatures and improve the efficiency of design and analysis processes used to fabricate parts with complicated shapes by saving considerable time and costs for the analysis of springback. Full article