Search

In the machining of carbon fiber reinforced polymer (CFRP) components, tool wear grows rapidly due to the highly abrasive property of carbon fibers, resulting in unfavorable part quality such as delamination and fiber pullout. The tool wear progression, cutting forces, and their quantitative relationship are highly dependent on the fiber orientation. This paper predicts the tool wear progression based on cutting force signals in milling unidirectional (UD) CFRP with the fiber orientation effect. A deep learning model based on multi-channel 1D convolutional neural network (CNN) and long short-term memory (LSTM) is used, with the benefit of considering the unique force variation features due to the CFRP anisotropy. Milling experiments with the fiber orientations of 0°, 45°, 90°, and 135° under different feed rates and cutting speeds were performed. The multi-channel 1D CNN obtains the force signal features from individual univariate time series, and combines the time-series information from all channels as the final feature representation in the final layer of the network. The LSTM layer extracts the temporal and spatial characteristics of the dynamic force signals. Moving window and distribution analysis techniques are applied to the predicted tool wear results to reduce the effect of force signal disturbance caused by CFRP inhomogeneity. The model achieves the tool wear prediction with R2 of 95.04% and MAE of 2.94 μm. Moreover, benefiting from its high feature representation capacity, the proposed method shows higher prediction accuracy for different cutting conditions with more than 25% improvement compared to other commonly used data-driven methods, including 1DCNN, 2DCNN, LSTM, BPNN, and SVR. [ABSTRACT FROM AUTHOR]

It has been well recognized that, for most climatic records, their current states are influenced by both past conditions and current dynamical excitations. However, how to properly use this idea to improve the climate predictive skills, is still an open question. In this study, we evaluated the decadal hindcast experiments of 11 models (participating in phase 5 of the Coupled Model Intercomparison Project, CMIP5) in simulating the effects of past conditions (memory part, M(t)) and the current dynamical excitations (non-memory part, ε (t) ). Poor skills in simulating the memory part of surface air temperatures (SAT) are found in all the considered models. Over most regions of China, the CMIP5 models significantly overestimated the long-term memory (LTM) of SAT. While in the southwest, the LTM was significantly underestimated. After removing the biased memory part from the simulations using fractional integral statistical model (FISM), the remaining non-memory part, however, was found reasonably simulated in the multi-model means. On annual scale, there were high correlations between the simulated and the observed ε (t) over most regions of the country, and for most cases they had the same sign. These findings indicated that the current errors of dynamical models may be partly due to the unrealistic simulations of the impacts from the past. To improve predictive skills, a new strategy was thus suggested. As FISM is capable of extracting M(t) quantitatively, by combining FISM with dynamical models (which may produce reasonable estimations of ε (t) ), improved climate predictions with the effects of past conditions properly considered may become possible. [ABSTRACT FROM AUTHOR]

Due to the tool-workpiece separation effect, high-speed ultrasonic vibration cutting has the proven ability to reduce tool flank wear and prolong tool life by up to approximately three times when turning Ti-6Al-4V alloys in the recent research. Temperature reduction caused by the cooling medium penetrating into the gaps of the tool-workpiece contact zone is considered to be the primary reason for this remarkable performance. In this paper, a tool-workpiece thermocouple system was developed for the temperature measurement of high-speed ultrasonic vibration cutting Ti-6Al-4V alloys. First, the calibration of the tool-workpiece thermocouple was conducted to identify the piecewise linear relation between thermoelectric power and cutting temperature. Next, the insulation of the thermoelectric circuit from both the vibration-stimulated circuit and the experiment platform was realized. Finally, comparative cutting experiments on high-speed ultrasonic vibration cutting and conventional cutting with and without cutting fluid were carried out. The experimental results demonstrate that in the dry cutting experiments, no obvious temperature difference between these two methods occurred. However, when cutting fluid was applied, compared to conventional cutting, a maximum 30% temperature reduction could be realized by high-speed ultrasonic vibration cutting with a duty cycle value of 0.55 at cutting speeds of 250 to 300 m/min. Therefore, cutting fluid is thought to be an essential part of effective cooling in the process of high-speed ultrasonic vibration cutting and its cooling behavior during such a short separation interval needs further researches. [ABSTRACT FROM AUTHOR]

As a common expression host, Escherichia coli has received more and more attention due to the recently developed secretory expression system, which offers advantages like reduced downstream bioprocesses and improved product quality. These advantages, coupled with high-density fermentation technology, make it a preferred system for large-scale production of many proteins utilized in industry and agriculture at a reduced process cost. To improve the secretion efficiency of target proteins, various strategies, including signal peptide optimization, periplasmic leakage, and chaperones co-expression have been developed. In addition, the optimization of the fermentation conditions such as temperature, inducer, and medium were also taken into account for the extracellular production in the high-density fermentation to reduce the cost of production. Here, these strategies ranging from genetic engineering to fermentation optimization were summarized for the future guidance of extracellular production of recombinant proteins using E. coli. [ABSTRACT FROM AUTHOR]

The original version of this article contained a mistake. Figure7 in the original article is incorrect. [ABSTRACT FROM AUTHOR]

In this study, the performance of CMIP5 models in simulating the El Niño-Southern Oscillation (ENSO) is evaluated by using a new metric based on percolation theory. The surface air temperatures (SATs) over the tropical Pacific Ocean are constructed as a SAT network, and the nodes within the network are linked if they are highly connected (e.g., high correlations). It has been confirmed from reanalysis datasets that the SAT network undergoes an abrupt percolation phase transition when the influences of the sea surface temperature anomalies (SSTAs) below are strong enough. However, from simulations of the CMIP5 models, most models are found incapable of capturing the observed phase transition at a proper critical point Pc. For the 15 considered models, four even miss the phase transition, indicating that the simulated SAT network is too stable to be significantly changed by the SSTA below. Only four models can be considered cautiously with some skills in simulating the observed phase transition of the SAT network. By comparing the simulated SSTA patterns with the node vulnerabilities, which is the chance of each node being isolated during a ENSO event, we find that the improperly simulated sea-air interactions are responsible for the missing of the observed percolation phase transition. Accordingly, a careful study of the sea-air couplers, as well as the atmospheric components of the CMIP5 models is suggested. Since the percolation phase transition of the SAT network is a useful phenomenon to indicate whether the ENSO impacts can be transferred remotely, it deserves more attention for future model development. [ABSTRACT FROM AUTHOR]