论文部分内容阅读
生物医学领域中的生物植入体必须使用具有生物相容性的材料进行连接以适应人体内复杂的生物、物理和化学环境。激光透射焊接(LTW)是解决生物医用材料之间连接的一种新方法。由于利用实验方式获得生物医用材料的最佳焊接工艺参数时间长、成本高,因此提出一种数值模拟驱动实验设计、工艺参数建模与优化的方法,对激光透射焊接生物医用材料进行了系统的研究。首先利用有限元模型(FEM)对焊接过程中的温度场进行模拟,并用焊接实验对模拟结果进行验证;然后利用FEM的模拟结果进行实验设计,用人工神经元网络(ANN)建立工艺参数和焊接结果之间的数学模型,并用FEM的模拟结果对此数学模型的预测结果进行验证;最后采用满意度函数(DF)与遗传算法(NSGA-II)相结合的方法,对工艺参数进行多目标优化,并对优化结果进行验证。结果表明:优化的预测结果、实验结果、模拟结果之间均取得了较好的一致性,此方法为有效指导生物医学领域中的焊接实验、提高焊接质量和降低生产成本开辟了新途径。
Biological implants in the biomedical field must be linked using biocompatible materials to suit the complex biological, physical, and chemical environment in the body. Laser-transmissive welding (LTW) is a new method to solve the connection between biomedical materials. Because of the long time and high cost of using the experimental method to obtain the best welding process parameters of biomedical materials, a method of numerical simulation driven experimental design and process parameter modeling and optimization is proposed. The biomedical materials of laser transmission welding are systematically the study. Firstly, the finite element model (FEM) was used to simulate the temperature field in the welding process and the simulation results were verified by the welding experiment. Then the experimental design was carried out by using the FEM simulation results. The artificial neural network (ANN) was used to establish the process parameters and welding The results of the mathematical model, and FEM simulation results to verify the results of the mathematical model to predict; Finally, the use of satisfaction function (DF) and genetic algorithm (NSGA-II) combination of methods for process parameters for multi-objective optimization , And verify the optimization results. The results show that the optimized prediction results, the experimental results and the simulation results are in good agreement. This method opens a new way for effectively guiding the welding experiments in the biomedical field, improving the welding quality and reducing the production cost.