基于人工智能算法的刀具磨损形貌预测研究现状
Research Status of Tool Wear Morphology Prediction Based on Artificial Intelligence Algorithms
周鑫 1韩翠红 1曲周德 1王井玲1
作者信息
摘要
磨损表面形貌能够反映运动副的磨损状态,通过对运动副表面磨损形貌进行研究分析,可以得到其磨损规律,预测磨损形貌变化.随着人工智能的快速发展以及在工程中的广泛应用,人工智能技术中的人工神经网络、模糊神经网络算法、遗传神经网络算法、支持向量机和多目标粒子群优化算法等方法逐步应用于磨损表面形貌表征参数的预测,且具有较高的预测精度.本文主要介绍国内外利用人工智能技术对磨损表面形貌的研究现状,分析各种算法的优点和应用局限性.总结了人工智能技术在磨损表面形貌预测领域中亟待解决的关键难题以及未来的研究方向.
Abstract
The wear surface morphology can reflect the wear state of the kinematic pair.The wear morphology change can be predicted by studying the wear morphology and wear law of the moving pair.With the rapid development and appli-cation in the engineering,artificial intelligence technology has been gradually applied to the prediction of wear surface mor-phology representation parameters,and has high prediction accuracy,such as artificial neural networks,fuzzy neural network algorithms,genetic neural network algorithms,support vector machines and multi-objective particle swarm optimization algo-rithms and so on.This paper mainly introduces the research status of wear surface morphology using artificial intelligence technology at home and abroad,and analyzes the advantages and application limitations of various algorithms.The key prob-lems and the future research directions are summarized.
关键词
人工神经网络/模糊神经网络算法/遗传神经网络算法/支持向量机/多项目粒子群优化算法Key words
artificial neural network/fuzzy neural network algorithm/genetic neural network algorithm/support vector machine/multi-item particle swarm optimization algorithm引用本文复制引用
基金项目
国家自然科学基金(52105200)
天津职业技术师范大学校级科研项目(KJ1704)
出版年
2024
NETL
NSTL
万方数据