摘要
高硅含量铝基电子封装材料有诸多优点,但因切削性能较差,限制了其推广应用。为了解决该问题,本文基于课题组前期研究工作及实验数据,进行高硅含量铝基电子封装材料切削用量优化策略研究;利用MATLAB软件编写神经网络-遗传算法(BP-GA)对切削力进行预测,再利用遗传优化算法建立以最小切削力为目标的切削用量优化算法,确定切削用量的最优解;搭建实验平台,并采用优化后的切削用量进行切削实验,验证优化结果的有效性。上述研究成果为建立高硅含量铝基电子封装材料优化系统奠定基础。
Aluminum-based electronic packaging materials with high silicon content have broad application prospects,but their cutting performance is poor,which seriously limits its promotion and application.Based on the preliminary research work and data,a study on the optimization strategy of cutting parameters of aluminum-based electronic packaging materials with high silicon content is carried out.A neural network-genetic algorithm(BP-GA)is programmed to predict cutting force and cutting temperature by MATLAB software.The genetic optimization algorithm is used to establish a cutting parameter optimization algorithm with the the goal of minimum cutting force to determine the optimal solution of the cutting parameters.An experimental platform is developed,and the optimization results is used to conduct cutting experiments to verify the effectiveness of the optimization results.The above research results lay the foundation for the establishment of an optimization system for aluminum-based electronic packaging materials with high silicon content.
作者
刘小莹
王浩
李怒飞
周利平
王计生
Liu Xiaoying;Wang Hao;Li Nufei;Zhou Liping;Wang Jisheng(Associate Professor,Chengdu Medical College,Chengdu 610500,China;不详)
出处
《工具技术》
北大核心
2022年第3期44-48,共5页
Tool Engineering
基金
四川省重点实验室开放式课题(2021CXKY02)
西华杯大学生创新创业项目(2021075)。
关键词
AL-SI复合材料
切削用量优化
遗传算法
神经网络
优化系统
Al-Si composite material
optimization of cutting parameters
genetic algorithm
neural network
optimization system
