Aluminum Wheel Digital Simulation And Process Optimization

Aluminum alloy wheel is the inevitable product of modern automotive industry because it's lightweight, high-speed and modernization. The research's background is the cooperative project "Aluminum wheel lightweight design and development" of Zhejiang University and Zhejiang Wanfeng Auto Wheel Co. Ltd. The theme of this dissertation is the simulation of low-pressure die casting (LPDC) aluminum automotive wheel and its optimization. Based on a great deal of domestic and international documents, the origin thoughts, new theories and technologies of correlative subjects, use an approach which combined theory research, digital simulation and experiments to realize the integration of casting simulation, structure finite element analysis, soft computing, fatigue design and process optimization etc. The integrated approach provides a theory basis and technical methods for reduce the weight of LPDC A356 automotive wheel.1. Based on hydrodynamics, heat transfer theory and casting forming theory, improved SOLA scheme is adopted to solve the momentum transport corrected Poisson pressure equation, and the VOF (Volume of Fluid) method to trace the free surface in mold filling processes. A new criterion, Retained Melt Modulus (RMM) is used to predict porosity defect. Experiments validate that RMM criterion is more accurate than Niyama criterion when be used in LPDC Aluminum wheel porosity prediction. Deterministic modeling is adopted to simulate the microstructure of casting. Casting simulation changes the present status that mould design and process absolutely depended on the experience of engineers, provides a visual tool for LPDC automotive wheel process.2. Casting's quality is difficult to control because the LPDC process is complicated and many factors influence process. Appropriate simplification and hypothesis are adopted in this paper temperature of various die part and pouring temperature are studied as process parameters. Taguchi method is used to design orthogonal experiments to analyze the process parameters. Use a soft computing strategy which combined artificial neural network (ANN) and genetic algorithm (GA) to optimize the process. BP network is used to build up a nonlinear mapping relationship between process parameters and control objectives based on casting simulation results. Genetic algorithm is applied to realize parameters optimization. One style low-pressure die casting A356 aluminum alloy wheel was researched as an instance. Five process parameters such as pouring temperature, top die temperature, bottom die temperature, side die temperature, core die temperature are optimized. The results show that this approach is effective to get optimized process parameters and control porosity defect and solidification time.3. Wheel is the important safe part of the passenger car, its mechanic performance directly effect if the car can run safely. Bench tests include of dynamic bending fatigue test, dynamic radial fatigue test and dynamic impact test are major test for inspecting the safe performance of Aluminum wheel. In general, bending fatigue failure is the major failure style. In this paper, a finite element model of bending test is built to calculate the stress distribution of the wheel and get stress-time plot, average stress and average stress amplitude. Dangerous point is also found by stress distribution results. A real stress experiment is used to validate the finite element model is accurate. Based on experiment results and engineering experience, a simplified finite element model of 13°impact test is built. Wheel structure is optimized according to the stress distribution result in the dangerous location. Impact performance is improved after modified the wheel structure. Also a real wheel's experiments validate the simplified finite element model is accurate and reliable.4. Based on finite element analysis results, nominal stress approach and local stress-strain approach respective with Manson-Coffin and Simth-Waston-Topper damage formula are used to predict aluminum alloy wheel's fatigue life of bending test. Compared with one type 16×6.5J wheel real bending fatigue test results, wheel fatigue life predicted by Simth-Waston-Topper is more approximate to testing life. But the fatigue life predicted by SWT method is still a gap to real result when the load is small that will be noticed in applied in engineering.5. Fracture mechanics basic theory and damage tolerance design method are applied in Aluminum wheel fatigue design. The effect by casting defect and microstructure is considered. A fatigue life prediction model concerned SDAS and casting defect is built based on small fatigue crack propagation theory. This model realizes the integration of multi-scale casting simulation, finite element analysis and fatigue analysis. An integrated platform to wheel structural performance (both fatigue and impact) that includes of casting process simulation, casting defect prediction is constructed primarily. Compared with traditional fatigue prediction approach such as nominal stress approach and local stress-strain approach, this model proposed in the paper is more accurate and reliable whether in small or big load conditions.This research focus on the design and manufacture of LPDC automotive aluminum alloy wheel, refers to casting simulation, microstructure simulation, casting process and mould structure optimization based on low pressure die casting simulation. Taguchi method and soft computing method are used to build the neural network model of the casting quality and process. Genetic algorithm is used to realize the process optimization。The model of bending test is built by finite element method, according to the stress distribution of the wheel, the fatigue life is predicted with sufficient accuracy. Also use finite element method to build a simplified model for impact test based on some experiments. Casting simulation is integrated into fatigue analysis to realize the CAE technology integration of process simulation and structure finite element simulation.