Two millimetre thick Alclad 7B04-T74 aluminium alloy was friction spot welded at different tool rotation speeds. The weld formation, Alclad redistribution, microstructures and mechanical properties of the joints were investigated. The results indicate that inappropriate tool rotation speeds can give rise to weld defects, such as annular groove, void and surface concavity. After welding, the original surface Alclad is redistributed as a U shaped Alclad layer in the weld. When the tool rotation speed is relatively high, eutectic films can be observed in the stir zone, and the Alclad layer in the weld is a preferred crack propagation path during tensile shear testing. The optimised joint with a tensile shear failure load of 11 921 N can be obtained at a tool rotation speed of 1500 rev min 21 .

No abstract

In early August 2006, a crumb rubber concrete (CRC) bridge deck was constructed in a suburb of Tianjin, China. Although small, measuring 24 × 8 × 0·12 m, it was the first instance of the application of a CRC bridge deck reported in public literature. More than 10 years have passed since, and the last inspection of the deck carried out in June 2017 showed that few cracks have developed in the deck surface. Visual inspection of the bottom of the bridge deck showed no sign of stains owing to water leakage. At the same time, six samples were cored from the deck and were evaluated for split strength, density and rubber content. Four cored samples were further scanned by computerised image software to reveal the distribution of rubber crumbs. In March 2019, water permeability and carbonation tests were carried out on the deck as well as on a nearby plain concrete bridge deck. This study reports on these findings and discusses issues such as flexural strength and rubber floating, and provides recommendations for rubber content in crack-controlling CRC mix design. This study concludes that the Tianjin CRC bridge deck is in good condition, that the material properties show little deterioration and that its durability has been sustained.

Friction stir welding (FSW) is regarded as an important joining process for the next generation of aerospace aluminum alloys. However, the performance of the FSW process often suffers from low precision and a long test cycle. In order to overcome these problems, a machine learning model based on a backpropagation neural network (BPNN) was developed to optimize the FSW of 2195 aluminum alloys. A four-dimensional mapping relationship between welding parameters and mechanical properties of joints was established through the analysis and mining of FSW data. The intelligent optimization of the welding process and the prediction of joint properties were realized. The weld formation characteristics at different welding parameters were analyzed to reveal the metallurgical mechanism behind the mapping relationship of the process-property obtained by the BPNN model. The results showed that the prediction accuracy of the method proposed could reach 92%. The welding parameters optimized by the BPNN model were 1810 rpm, 105 mm/min, and 3 kN for the rotational speed, welding speed, and welding pressure, respectively. Under these conditions, the tensile strength of the joint was found to be 415 MPa, which deviated from the experimental value by 3.71%.