The reduction of weight for car Body-in-White (BIW) structures through the use of high/ultra-high strength aluminium alloys is the most efficient way to achieve CO2 emissions and reduce fuel consumption. Hot and warm stamping are forming techniques commonly used in the automotive industry to form aluminium alloy sheets into structural components. However, it is challenging to improve the production rate and achieve further cost savings with these mature forming technologies. Moreover, there are significant challenges in current forming technologies to form dissimilar alloys, and the use of tailor welded blanks for BIW necessitates the development of novel forming technologies.
The present work aims to develop a novel sheet metal forming technology – Fast light Alloys Stamping Technology (FAST) for manufacturing panel components from Dissimilar Alloys – Tailor Welded Blanks (DA-TWBs), whilst achieving desirable mechanical properties in a cost and time efficient manner. The dissimilar alloys in this study consist of two base materials of 6xxx series Al-Mg-Si and 7xxx series Al-Zn-Mg-Cu alloys, which were joined by friction stir welding. The feasibility of the FAST was initially studied on the aluminium alloys AA6082 and AA7075, then applied to the application of DA-TWBs by using the common processing window that was suitable for both AA6082 and AA7075. The optimisation of the processing window of the FAST process and a comprehensive understanding of the thermal-mechanical properties and a post-Paint Bake Cycle (PBC) strength investigation on various forming process condition were conducted. The implementation of the proposed FAST process was conducted by forming M and U-shaped panel components in lab scale. The FAST optimal process was successfully implemented to form a U-shaped component which was made from DA-TWBs at 300 °C and enabled a significant reduction of total cycle time from several hours to 10 seconds, which further improved the production rate to 12.5 spm (strokes per minute).
In order to reduce experimental efforts, the present research described an efficient method to determine the critical processing parameters, i.e. the integration of the Finite Element (FE) simulated temperature evolutions with the Continuous Cooling Precipitation (CCP) diagrams of aluminium alloys. Through the optimisation of processing parameters, the temperature evolutions and CCP diagrams do not intersect, indicating that the post-PBC strength of the aluminium alloys could be fully retained after a proper artificial ageing process. A general aluminium alloy-independent model with one set of model constants was therefore developed to predict the Interfacial Heat Transfer Coefficient (IHTC) evolutions as a function of contact pressure, surface roughness, initial blank temperature, initial blank thickness, tool material, coating material and lubricant material. Subsequently, the predicted IHTC evolutions for AA6082 and AA7075 were used to simulate their temperature evolutions, which were then integrated with their CCP diagrams to identify the critical processing parameters in hot and warm stamping processes to meet the desired post-PBC strength of the AA6082 and AA7075, which were then experimentally verified by the results of the dissimilar alloy forming.
A software agnostic platform ‘Smart Forming’, was developed to provide cloud Finite Element Analysis (FEA) of a hot and warm stamping process in three stages, namely pre-FE modelling, FE simulation and post-FE evaluation. When the desired materials and processing window were uploaded on the platform, the flow stress, material properties, IHTC and friction coefficient were predicted by the model-driven functional modulus and then generated in the form of compatible packages that could be implemented into the desired FE software. Subsequently, the FE simulation was performed either locally or remotely on the developed platform. When the simulated evolutionary thermomechanical characteristics of the formed component were uploaded, the formability, quenching efficiency and post-PBC strength could be predicted and then demonstrated on a dedicated visualiser on the developed platform. Cloud FEA of FAST was conducted to demonstrate the function of the developed platform, showing an error of less than 10 %. Open Acces
