Evaluation of high-temperature tensile behavior for metal foils by a novel resistance heating assisted tensile testing system using samples with optimized structures

To evaluate the tensile behavior of metal foils by resistance heating(RH)assisted tensile testing system accurately,this study proposed to embed a digital image correlation(DIC)system with laser speckles for the measurement of full-field strain distribution.Furthermore,the sample structures were optimized to achieve uniform temperature and strain distribution.An infrared camera was used to monitor the temperature distribution.Rectangular samples instead of dog-bone shaped samples were proposed.A model for calculating the temperature distribution was established to optimize the sample structure.The parameters that influence the temperature distribution and tensile behavior were studied.As results,compared to the strain measured by a non-contact extensometer,the maximum deviation of the strain measured by DIC was less than 6%when the nominal strain was larger than 0.013.It is confirmed that the proposed tensile testing system is reliable for measuring the temperature and full-field strain distributions.Sample shape influenced temperature distributions of smaller samples while it almost had no influence on the temperature distributions of larger samples.The temperature difference was not affected by the material type but by the sample size.The proposed rectangular shape was validated to be feasible for RH assisted tensile testing.The sample length was successfully optimized for a more uniform temperature distribution by the established model.Although the tensile deformation was not influenced by the sample shape,the temperature distribution resulted in a non-uniform strain distribution before achieving ultimate tensile strength.Longer effective sample length between two clamping jigs contributed to a more uniform temperature distribution and material deformation.A more accurate evaluation of high-temperature tensile behavior for metal foils can be achieved by the proposed RH assisted tensile testing system using rectangular samples with an optimized structure.

The Topology Sampling of H2SO4·NH3 with Meta-Dynamics Method

The configurations of molecular clusters have significant impacts on their growth into fine particles in atmosphere.In this paper,we explore the topology space of the structure of H2SO4·NH3 dimer with a novel sampling technique of meta-dynamics(MTD)method and ab initio molecular dynamics simulations.The simulations are carried out at the temperatures of both 50 K and 242 K,which represent the typical high and low latitudes of troposphere.The results show that,compared with only traditional MD simulations,the structure samplings are significantly accelerated with MTD method.Therefore,more isomers of the dimer are discovered within the same simulation time scale.In addition,the results show that MTD is more efficient for circumstances with high temperature.

Real-time grain breakage sensing for rice combine harvesters using machine vision technology

Breakage rate is one of the most important indicators to evaluate the harvesting performance of a combine harvester.It is affected by operating parameters of a combine such as feeding rate,the peripheral speed of the threshing cylinder and concave clearance,and shows complex non-linear law.Real-time acquisition of the breakage rate is an effective way to find the correlation of them.In addition,real-time monitoring of the breakage rate can help the driver optimize and adjust the operating parameters of a combine harvester to avoid the breakage rate exceeding the standard.In this study,a real-time monitoring method for the grain breakage rate of the rice combine harvester based on machine vision was proposed.The structure of the sampling device was designed to obtain rice kernel images of high quality in the harvesting process.According to the working characteristics of the combine,the illumination and installation of the light source were optimized,and the lateral lighting system was constructed.A two-step method of“color training-verification”was applied to identify the whole and broken kernels.In the first step,the local threshold algorithm was used to get the edge of kernel particles in a few training images with binary transformation,extract the color spectrum of each particle in color-space HSL and output the recognition model file.The second step was to verify the recognition accuracy and the breakage rate monitoring accuracy through grabbing and processing images in the laboratory.The experiments of about 2300 particles showed that the recognition accuracy of 96%was attained,and the monitoring values of breakage rate and the true artificial monitoring values had good trend consistency.The monitoring device of grain breakage rate based on machine vision can provide technical supports for the intellectualization of combine harvester.

A robust and efficient structural reliability method combining radial-based importance sampling and Kriging

Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the computational burden, related research focuses mainly on reduction of samples and application of surrogate model, which substitutes the performance function. However,the reduction of samples is achieved commonly at the expense of loss of robustness, and the construction of surrogate model is computationally expensive. In view of this, this paper presents a robust and efficient method in the same direction. The present method uses radial-based importance sampling (RBIS) to reduce samples without loss of robustness. Importantly, Kriging is fully used to efficiently implement RBIS. It not only serves as a surrogate to classify samples as we all know, but also guides the procedure to determine the optimal radius, with which RBIS would reduce samples to the highest degree. When used as a surrogate, Kriging is established through active learning, where the previously evaluated points to determine the optimal radius are reused. The robustness and efficiency of the present method are validated by five representative examples, where the present method is compared mainly with two fundamental reliability methods based on active learning Kriging.