首页|基于机器学习的Inconel718孔增材制造工艺多目标优化

基于机器学习的Inconel718孔增材制造工艺多目标优化

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对基于机器学习的增材制造工艺多目标优化进行了研究,提出了一种基于改进遗传算法的钻孔加工参数多目标优化模型,求解出最优钻孔参数,提高钻孔加工的质量和效率,继而实现增材零件尺寸精度和加工质量的提升,使零件达到使用标准.首先对钻孔加工参数多目标优化模型进行构建,然后采用改进后的遗传算法对其进行求解,最后对求解结果进行实验测试.测试结果表明:改进后的遗传算法求解获取的最优加工参数组合所得实际测试结果的孔尺寸平均误差仅为3.3%,孔表面粗糙度误差仅为7.1%,能够达到提高增材零件尺寸精度和加工质量的目的,使其达到使用标准,推动了增材制造工艺在工业领域的应用与推广.
Multi objective optimization of Inconel 718 hole additive manufacturing process based on machine learning
This article studies the multi-objective optimization of additive manufacturing processes based on machine learning,and proposes a multi-objective optimization model for drilling machining parameters based on improved genetic algorithm.The optimal drilling parameters are solved to improve the quality and efficiency of drilling machining,thereby achieving the improvement of dimensional accuracy and machining quality of additive parts and achieving the use standards of the parts.Firstly,a multi-objective optimization model for drilling machining parameters is constructed,and then an improved genetic algorithm is used to solve it.Finally,the solution results are experimentally tested.The test results show that the optimal machining parameter combination obtained by the improved genetic algorithm in actual experiments has an average error of only 3.3%in hole size and 7.1%in hole surface roughness compared to the actual test results.This can achieve the goal of improving the dimensional accuracy and machining quality of additive parts,making them meet the usage standards,and promoting the application and promotion of additive manufacturing technology in the industrial field.

machine learningadditive manufacturing processdrilling processinggenetic algorithm

张晶晶、卢慧、李艳利、翟媛媛

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陕西服装工程学院,陕西 咸阳 712046

机器学习 增材制造工艺 钻孔加工 遗传算法

2024

模具技术
上海交通大学

模具技术

CSTPCD
影响因子:0.219
ISSN:1001-4934
年,卷(期):2024.(6)