An novel compounding process using nanoCaCO 3 aqueous suspension for preparing polymer/ nano-CaCO 3 composites with nanoparticles dispersed at the nanoscale is reported. The process is called the mild mixing method. In this method, the pre-dispersed nanoparticle suspensions are blended with melting polymers in a weak shearing field using an extruder, followed by removing the water from the vent. The four typical polymeric nanocomposites were prepared by mild mixing method. The dispersion of nano-CaCO 3 in the matrix of the polymer at the nanoscale was confirmed by scanning electron microscopy (SEM). The molecular weights of polycarbonate (PC) and its nanocomposite showed that the degradation had not occurred during the mild mixing processing. The mechanical properties of the composite with 1.5 wt-% nano-CaCO 3 improve slightly. It proved that this approach is suitable for the preparation of nanocomposites based on both polar and non-polar polymers.

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In this paper, the effect of surface ultrasonic rolling treatment (SURT) on surface properties of T4003 cold metal transfer (CMT) welded joints was studied. Surface topography and microstructure changes of the welded joint surface before and after SURT were observed by optical microscope and scanning electron microscope. The hardness and residual stress distribution of welded joint were measured by a microhardness tester and X-ray diffractometer. The change of corrosion resistance of welded joints was studied by electrochemical polarization curve measurement. The results show that surface roughness (Ra) of the weld zone, heat affect zone (HAZ), and base metal after SURT was reduced to 0.320 μm, 0.156 μm, and 0.227 μm, respectively, and surface morphology became smooth. The plastic deformation layer and working hardening layer were formed at the welded joint. The degree of plastic deformation of the weld zone was more serious than that in the base metal, and grains in weld zone was obviously refined. The thickness of plastic deformation layer was about 100 μm. The surface hardness in the weld zone was highest, which is about 420 HV. The refinement of grains and the increase of surface hardness can improve the fatigue life of welded joint. After SURT, the residual stress in the welded joint changes from residual tensile stress to residual compressive stress, which can also improve fatigue life of the welded joint. Surface corrosion resistance of welded joints after SURT was improved due to smooth surface and the formation of fine grains layer.

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Considering its advantages in dealing with highdimensional visual input and learning control policies in discrete domain, Deep Q Network (DQN) could be an alternative method of traditional auto-focus means in the future. In this paper, based on Deep Reinforcement Learning, we propose an endto-end approach that can learn auto-focus policies from visual input and finish at a clear spot automatically. We demonstrate that our method -discretizing the action space with coarse to fine steps and applying DQN is not only a solution to auto-focus but also a general approach towards vision-based control problems. Separate phases of training in virtual and real environments are applied to obtain an effective model. Virtual experiments, which are carried out after the virtual training phase, indicates that our method could achieve 100% accuracy on a certain view with different focus range. Further training on real robots could eliminate the deviation between the simulator and real scenario, leading to reliable performances in real applications.