产品属性

厂家(产地)

LNP

牌号

LC003E

用途级别

通用

品牌

150680

所有参数

LNP THERMOCOMP LC003E compound PEEK SABIC 总代理直销

SABIC LNP THERMOCOMP LC003E compound PEEK产品介绍：

PEEK聚合物及其复合材料制品可在高温高压蒸汽中长期连续使用，机械性能基本没有变化，最初一段工作时间内力学性能反而会提高，主要是因为注塑过程中PEEK零件应力的释放，及随着PEEK零件温度的升高，相当于对PEEK零件进行了热处理，进一步使结晶度提高。

PEEK具有突出的耐高低温性，阻燃性、耐辐射性以及良好的机械性能，使其在**航天工业中可作为超高温导线，在石油工业中可作为潜油泵电机绕组线及连接线，在原子能发电站中可作为核岛驱动机构的绕组线圈长期在辐照条件下使用；同时也广泛适用于高速牵引机车。

SABIC LNP THERMOCOMP LC003E compound PEEK特性：

具有热固性塑料的耐热性、化学稳定性和热塑性塑料的成型加工性。聚醚醚酮还具有优异的耐热性。其热变形温度为160℃，当用20%～30%的玻璃纤维增强时,热变形温度可提高到280～300℃。聚醚醚酮的热稳定性良好，在空气中420℃。2h情况下失重仅为2%，500℃时为2.5%,500℃时才产生显著的热失重。聚醚醚酮的长期使用温度约为200℃，在此温度下，仍可保持较高的拉伸强度和弯曲模量，它还是一种非常坚固的材料，有优异的长期耐蠕变性和耐疲劳性能。

聚醚醚酮的电绝缘性能非常优异，体积电阻率约为10～10Ω·cm。它在高频范围内仍具有较小的介电常数和介电损耗。例如，在10Hz时，在室它的介电常数仅为3.2，介电损耗仅为0.02。

聚醚醚酮的化学稳定性也非常好，除浓硫酸外，几乎对任何化学试剂都非常稳定，即使在较高的温度下，仍能保持良好的化学稳定性。另外，它还具有**的耐热水性和耐蒸汽性。在200~250 ℃的蒸汽中可以长时间使用。

我司还供应以下产品:

供应 PEEK LNP LUBRICOMP LL004 compound 生产商: SABIC Innovative Plastics Asia Pacific

供应 PEEK LNP LUBRICOMP LL004C compound 生产商: SABIC Innovative Plastics Asia Pacific

供应 PEEK LNP LUBRICOMP LL004E compound 生产商: SABIC Innovative Plastics Asia Pacific

供应 PEEK LNP LUBRICOMP LX91475 compound 生产商: SABIC Innovative Plastics Asia Pacific

SABIC LNP THERMOCOMP LC003E compound PEEK详解：

sub-pixel level. The projection of the local image models on this set of spatial patterns is used as input data to the network, as described in the next paragraph. Neural network training results To estimate the HR pixels, it has been used a multi-layer perceptron (MLP) archi-tecture [11], with two layers of neurons. The hidden layer is composed of 10 neurons with hyperbolic tangent activation functions. These neurons are connected to a single neuron in the output layer, with a linear activation function. The output of this neuron provides a zero-mean, unit-variance estimation of the high-resolution central pixel. Renormalization provides the final value. A diagram of the system is presented in fig-ure 2 a).The network has been trained using a conjugate gradient descent method on syn-thetic data sets with various noise levels. The training data has been generated syn-thetically from a set of 23 high-resolution images of urban scenes acquired by the Quickbird satellite imaging system. Fig. 2. a) Diagram of the superresolution neural processing stage; b) network average predic-tion error, measured on a validation test set, as a function of the dimensionality of the networkinput space for a 3x3 neighborhood; c) average prediction error for a 5x5 neighborhood. The neural system has been trained for several input sizes, to study quantitatively the effect on the predicting error of augmenting the input data with the value corre-sponding to each sorted principal component. In figure 2 is presented the evolution of this error as a function of the number of eigenvectors retained in the dimensionalityreduction step, for local windows of sizes 3x3 and 5x5. As it is apparent, the error de-creases non-gradually, with almost flat zones interleaved with step decreases in theprediction error. Furthermore, these step changes occurred also in zones where the representational error of the dimensionality reduction step might be seen in principle as negligible (see figure 1, for a 3x3 window), highlighting the inadequacy of an MSE-based criteria in this case to select the dimension of the network input space.

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