Solid riveting is the most widely used joining technique in aircraft assembly, and the current key problems affecting practical application and reliable lifting are concentrated on static strength and fatigue. This paper aims to present a practical review on current practice and novel techniques of solid riveting for aircraft applications in order to obtain a thorough understanding of the underlying mechanisms of defect development to assist industrial users to find pragmatic solutions for safe life extension of components. At first, the current status of solid riveting processes is reviewed, and the key influencing factors on static/fatigue failure of riveted joints are identified. Effects of solid riveting design parameters, manufacturing parameters, residual stress, load transfer and secondary bending on static and fatigue strengths of riveted lap joints are discussed, followed by a review of the state-of-the-art solutions that deal with static/fatigue failures. Furthermore the new development in solid riveting techniques, including the use of different materials and riveting processes, is addressed. Finally, future research perspective and applications industrial riveting is presented.

Multiple blank holders with variable blank holder forces is a promising method to effectively improve the formability of aluminum alloy for the hot stamping process. However, it introduces much more variables of holder forces in the process. The conventional simulation- or experiment-based methods cannot effectively analyze and optimize the hot stamping process. This paper provides a new way to solve this problem by utilizing the advantages of machine learning techniques. Finite element models (FEM) of hot stamping of a box-shaped part considering eight separate blank holders with varying forces were established first. Based on the model, 1000 sets of process parameters and corresponding hot stamping results were generated randomly to provide enough data. A machine learning model was then established to predict the maximum and minimum thickness of the stamped parts. Fourth, a convolutional neural network was established to predict the thickness variation distribution. The results showed that a series of optimal multiple variable blank holder forces were obtained to improve formability, and the machine learning models could accurately predict the thickness distribution. This technique provides an efficient tool in applying multiple blank holders with variable blank holder forces in the hot stamping process.

This paper presents a method of increasing draw-ability of materials in hot stamping processes using forming tools with macro-scale textures on the tool surfaces. Firstly, a series of tool texture designs and orientations were presented and a test programme was designed for the experimental investigation. Top-hat shape drawing experiments in cold and hot stamping conditions were conducted to evaluate the effect of macro-scale tool surface textures on the material drawing. Texture directional and texture feature effects of the blankholders on the draw-ability of the material have been investigated. A finite element model of the top-hat drawing process with textured tool surfaces has been established and validated from experimental data. Further FE process modelling has been carried out and the effects of texture features and forming speed have been studied. The relationships between texture designs and material drawing results have been analysed. The developed FE model can provide a guide to design the geometry of tool textures and optimise the hot stamping process parameters.

Physicallybased constitutive equations are increasingly used for finite element simulations of metal forming processes due to the robust capability of modelling of underlying microstructure evolutions. However, one of thelimitations of current models is the lack of practical validation using real microstructure data due to the difficulties in achieving statistically meaningful data at a sufficiently large microstructure scale. Particularly, dislocation density and grain size governing the hardening in sheet deformation are of vital importance and need to be precisely quantified. In this paper, a set of dislocation mechanics-based plane stress material model is constructed for hot forming aluminum alloy. This material model is applied to high strength 7075 aluminum alloy for the prediction of the flow behaviorsconditioned at 300–400 °C with various strain rates. Additionally, an electron backscatter diffraction (EBSD) technique was applied to examine the average grain size and geometrical necessary dislocation (GND) density evolutions, enabling both macro- and micro- characteristics to be successfully predicted. In addition, to simulate the experienced plane stress states in sheet metal forming, the calibrated model is further extended to a plane stress stateto accuratelypredict the forming limits under hot conditions.The comprehensively calibrated material model could be used for guidinga better selection of industrial processing parameters and designing process windows, taking into account both the formed shape as well as post formed microstructure and, hence, properties.