Time and temperature effect on the linear–nonlinear viscoelastic transition threshold of a polymeric system

In the present study, the transition of a polymeric material from the linear to the nonlinear viscoelastic behavior and the determination of the nonlinearity stress threshold variation with time and temperature are investigated. For this purpose a systematic experimental program consisted of thermal and mechanical characterization of certain polymeric material followed by isothermal creep tests at different stress levels and temperatures was conducted. Through isochronal curves that occurred from creep tests, the nonlinearity threshold dependence on both time and temperature is presented. The reported results provide information that is critical for the design and development of polymer structures and components. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

聚烯烃/纳米SiO2复合材料研究进展

论述了纳米SiO2的表面处理方法、复合材料的制备技术及其力学和光学性能。表明纳米SiO2分散于聚烯烃(PP、HDPE)中,可以提高基体的韧性、强度和模量。以纳米SiO2作为填料的聚乙烯棚膜,呈现优良的红外线吸收性能和可见光透过性能。为此,纳米SiO2可作为一种聚乙烯农膜的新型保温剂。     

聚酰亚胺的改性

本文介绍了旨在克服聚酰亚胺某些性能缺陷的改性产品 ,其中包括可溶型、透明型、低热膨胀型、共缩聚型、功能型、高粘接型、加成型聚酰亚胺和聚酰亚胺 /无机纳米复合材料等品种的研究动向 ,指出目前应重点研究聚酰亚胺 /无机纳米复合材料     

Buckling optimization of hybrid-fiber multilayer-sandwich cylindrical shells under external lateral pressure

This paper describes a method of analyzing the maximum buckling strength of hybrid-fiber multilayer-sandwich cylindrical shells under external lateral pressure with respect to fiber orientations and weighting factors. Formulae for optimizing these parameters are presented and conditions for the application of these formulae are discussed. A discriminant is derived for assessing whether the hybrid sandwich cylindrical shells for given different unidirectional composites should reach the extreme value of critical pressure.     

锌基复合材料研究进展

评述了锌基复合材料的发展和研究概况,着重介绍了锌基复合材料的制备工艺和性能研究现状,最后对锌基复合材料的研究方向和应用前景提出了一些设想和展望。     

不连续增强镁基复合材料的界面行为

综述了近年来国内外关于Ａｌ２Ｏ３短纤维、ＳｉＣ晶须、ＳｉＣ颗粒、Ｂ４Ｃ颗粒增强镁基复合材料的界面结构及其对复合材料性能影响的研究进展，并对今后的研究提出了一些看法。     

颗粒增强TiAl基复合材料及其Si的韧化作用研究EI

运用ＸＤ工艺制备了颗粒增强ＴｉＡｌ基复合材料。增强颗粒均匀分布于ＴｉＡｌ基体中并细化ＴｉＡｌ合金。通过在ＴｉＢ_２／ＴｉＡｌ、ＴｉＣ／ＴｉＡｌ复合材料中加入适量的Ｓｉ，颗粒增强ＴｉＡｌ基复合材料的室温延性得到了改善。     

Surface treatment for adhesive-bonded joints by excimer laser

In this work a treatment for surface preparation to improve mechanical resistance in adhesive bonding of plastic composites reinforced with fibres and metallic material, has been performed using an excimer laser. The following couplings have been selected to reproduce joints commonly used in the aerospace and automotive industry: CFC (carbon fibre composite) with CFC, CFC with Al 2024T3, Al 99% with Al 99%, GFC (glass fibre composite) with zinc-coated sheet in low carbon steel FeP01. The surfaces have been prepared using an excimer laser, adopting several values of laser parameters. The obtained surfaces have been examined by optical and scanning electron microscope: comparative measures of wetting and roughness have been performed to obtain an accurate characterisation and to select the proper finishes suitable to improve the mechanical resistance of the joints. The results obtained show that laser treatment always improves the final resistance of the joint; notable increases, and no significant surface damages have been highlighted. Better results have been obtained with the Al 99% with Al 99% joints which, with a low number of pulses treatment, have shown an increase of mechanical resistance up to the 70%.     

Making costly manufacturing smart with transfer learning under limited data: A case study on composites autoclave processing

The integration of advanced manufacturing processes with ground-breaking Artificial Intelligence methods continue to provide unprecedented opportunities towards modern cyber-physical manufacturing processes, known as smart manufacturing or Industry 4.0. However, the “smartness” level of such approaches closely depends on the degree to which the implemented predictive models can handle uncertainties and production data shifts in the factory over time. In the case of change in a manufacturing process configuration with no sufficient new data, conventional Machine Learning (ML) models often tend to perform poorly. In this article, a transfer learning (TL) framework is proposed to tackle the aforementioned issue in modeling smart manufacturing. Namely, the proposed TL framework is able to adapt to probable shifts in the production process design and deliver accurate predictions without the need to re-train the model. Armed with sequential unfreezing and early stopping methods, the model demonstrated the ability to avoid catastrophic forgetting in the presence of severely limited data. Through the exemplified industry-focused case study on autoclave composite processing, the model yielded a drastic (88%) improvement in the generalization accuracy compared to the conventional learning, while reducing the computational and temporal cost by 56%.