Development of Fast light Alloys Stamping Technology (FAST) for manufacturing panel components from Dissimilar Alloys – Tailor Welded Blanks (DA-TWBs)

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

Analysis of the Associations between Vitamin D and Albuminuria or β-Cell Function in Chinese Type 2 Diabetes

Objective. To investigate the associations of 25-(OH)D and β-cell function or insulin resistance or albuminuria in Chinese type 2 diabetic patients. Methods. In total, 1408 type 2 diabetic patients without vitamin D supplement were included in this retrospective study. Results. Comparison between patients with and without 25-(OH)D deficiency indicated that, compared with patients with 25-(OH)D ≥ 50 nmol/L, patients with 25-(OH)D < 50 nmol/L showed a higher level of urine albumin-creatinine ratio (ACR) (90.15±10.30 mg/g versus 52.79±14.97 mg/g). Multiple regression analysis indicated that 25-(OH)D was independently and negatively correlated with urine ACR (OR=0.985, 95%CI 0.972–0.999, P=0.03), adjusted by age, diabetic duration, HBP duration, SBP, HbA1c, creatinine, LDL-C, triglyceride, total cholesterol, and HDL-C. Compared with patients with normal level of urine ACR, patients with higher level of urine ACR showed a significant lower level of 25-(OH)D (34.49±13.52 nmol/L versus 37.46±13.6 nmol/L, P=0.00). Analysis of the associations of 25-(OH)D and β-cell function or insulin resistance showed that 25-(OH)D may not correlate with β-cell function or insulin resistance. Conclusion. 25-(OH)D was independently associated with albuminuria in Chinese type 2 diabetic patients but was not associated with β-cell function or insulin resistance

Numerical forming limit prediction for the optimisation of initial blank shape in hot stamping of AA7075

The prediction of failure and optimisation of the initial blank shape of a component formed under the complex thermal and mechanical conditions could not simply rely on its thinning or strain. In contrast, the prediction of forming limit of material as a comprehensive result of temperature, strain rate and loading path is an essential indicator in a non-isothermal hot stamping process. However, it is challenging to implement an advanced forming limit model into conventional finite element (FE) software to simulate the failure of the formed component and thus optimise its initial blank shape. In this work, a unified viscoplastic-Hosford-MK model was developed to post-process the simulated evolutionary thermomechanical characteristics and then numerically predict the forming limit criterion of AA7075 in a hot stamping process, of which results were demonstrated on a dedicated visualiser that independent of FE software. Subsequently, the initial blank shape was optimised by analysing the evolutionary temperature, strain rate and loading path of failure regions. Furthermore, the developed forming limit model and optimised initial blank shape were experimentally verified with an error of less than 10%

Cloud FEA of hot stamping processes using a software agnostic platform

Finite element analysis (FEA) of a hot stamping process demands the implementation of accurate material properties and boundary conditions to precisely predict and evaluate the post-form quality of a component. A software agnostic platform was developed to provide cloud FEA of a hot stamping process in three stages, namely, pre-FE modelling, FE simulation and post-FE evaluation. When the desired materials and process window were uploaded on the platform, the flow stress, material properties, interfacial heat transfer coefficient (IHTC) and friction coefficient were predicted by the model-driven functional modules 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-form strength could be predicted and then demonstrated on a dedicated visualiser on the developed platform. Cloud FEA of two different hot stamping technologies was conducted to demonstrate the function of the developed platform, showing an error of less than 10%

Exploring the mechanism of Yishen Daluo decoction in the treatment of multiple sclerosis based on network pharmacology and in vitro experiments

Objective: To explore the mechanism and related active components of Yishen Daluo decoction (YSDLD) in treating multiple sclerosis (MS). Methods: Targets of YSDLD were collected through the TCMSP, Chemistry, and TCMID databases. The MS targets were collected through OMIM, DrugBank, Gencards, TTD, and Pharmgkb databases. We built “component–target” network diagrams and protein–protein interaction (PPI) diagrams and performed topological analysis. The targets were subjected to GO and KEGG enrichment analysis. Molecular docking verification was conducted on selected targets and molecules. Finally, in vitro experiments were conducted. BV2 cells were induced by lipopolysaccharide for model establishment. CCK8 experiment was conducted to explore the effect of YSDLD and RT-qPCR technology was used to explore the expression of key targets. Results: There were 184 active components in YSDLD and 898 targets of its action. There were 940 MS targets, and 215 targets were shared by YSDLD and MS. According to the “component–target” diagram, the top five key components included quercetin, kaempferol, beta-sitosterol, stigmasterol, and naringenin. IL-6, IL-1β, TNF-α, AKT1, and VEGFA were the important targets identified by PPI network topology analysis. A total of 564 functions were identified by GO enrichment analysis (P < .01), mainly involving inflammatory response, hypoxia response, plasma membrane, neuronal cell body, protein phosphatase binding, and cytokine activity. KEGG enrichment analysis enriched 98 pathways (P < .01). YSDLD at the concentration of 20 μg/mL had no effect on BV2 cells. RT-qPCR indicated that YSDLD at the concentrations of 15 μg/mL and 20 μg/mL alleviated LPS-induced inflammatory injury and lowered the content of inflammatory factors (P < .05). Conclusion: In this paper, the network pharmacology and in vitro experiments were used to explore the potential mechanism of YSDLD in treating MS. The research provides a good basis for the development of YSDLD and drugs for MS in future