基于自编码器和遗传算法的机械制造流程优化研究  

Research on Optimization of Mechanical Manufacturing Process Based on Autoencoders and Genetic Algorithms

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作  者:胡荣耀 HU Rongyao(Guizhou Technological College of Machinery and Electricity,Qiannan,Guizhou 558000,China)

机构地区:[1]贵州机电职业技术学院,贵州黔南558000

出  处:《自动化应用》2025年第7期262-264,共3页Automation Application

摘  要:提出了一种基于自编码器和遗传算法的混合智能机械制造流程优化系统。该系统利用自编码器对高维制造数据进行降维,然后使用遗传算法在压缩的特征空间中搜索最优的制造参数组合。这种方法既能有效处理复杂的非线性关系,又能在大规模搜索空间中快速找到近似最优解。实验结果表明,该系统在提高生产效率、降低能源消耗、减少废品率和提升产品质量方面均取得显著成效,为制造业的智能化和精细化管理提供了创新性的解决方案。This paper proposes a hybrid intelligent mechanical manufacturing process optimization system based on autoencoders and genetic algorithms.The system utilizes autoencoders to perform dimensionality reduction on high-dimensional manufacturing data,followed by the use of genetic algorithms to search for the optimal combination of manufacturing parameters within the compressed feature space.This approach is capable of effectively handling complex nonlinear relationships and quickly finding near-optimal solutions in large-scale search spaces.Experimental results demonstrate that the system has achieved significant results in improving production efficiency,reducing energy consumption,reducing scrap rates,and enhancing product quality,providing an innovative solution for the intelligent and refined management of the manufacturing industry.

关 键 词:自编码器 遗传算法 机械制造 

分 类 号:TP183[自动化与计算机技术—控制理论与控制工程]

 

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