摘 要
机械加工工艺参数优化对提高产品质量和生产效率具有重要意义，传统优化方法存在耗时长、精度低等问题。基于此，本文提出一种基于机器学习的机械加工工艺参数优化方法，旨在通过数据驱动的方式实现高效精准的参数优化。首先构建包含多种加工条件和结果的数据库，利用特征工程技术提取有效特征，为后续模型训练奠定基础。选用支持向量机、随机森林等机器学习算法建立预测模型，采用交叉验证等手段确保模型泛化能力。将优化问题转化为求解目标函数极值问题，运用遗传算法在预测模型基础上进行全局搜索以确定最优工艺参数组合。实验结果表明，该方法能够显著提升加工精度，降低表面粗糙度，与传统方法相比平均优化效率提高约30%，且在不同类型的加工任务中均表现出良好的适应性。创新点在于融合机器学习与进化算法优势，突破传统优化局限，提供了一种智能化、通用性强的机械加工工艺参数优化新思路，为智能制造领域的发展提供了理论依据和技术支持。
关键词：机械加工工艺参数优化 机器学习 遗传算法
Abstract
The optimization of machining process parameters is crucial for enhancing product quality and production efficiency. Traditional optimization methods suffer from issues such as time-consuming processes and low accuracy. To address these challenges, this paper proposes a machine learning-based approach for optimizing machining process parameters, aiming to achieve efficient and precise parameter optimization through data-driven methods. A database encompassing various machining conditions and outcomes is first constructed, and feature engineering techniques are employed to extract effective features, laying the foundation for subsequent model training. Machine learning algorithms such as Support Vector Machines (SVM) and Random Forests are selected to establish predictive models, with cross-validation and other techniques ensuring the generalization capability of the models. The optimization problem is transformed into solving the extremum of an ob jective function, and Genetic Algorithms are utilized to perform global searches based on the predictive models to determine the optimal combination of process parameters. Experimental results demonstrate that this method significantly improves machining accuracy and reduces surface roughness, achieving an average optimization efficiency improvement of approximately 30% compared to traditional methods, while exhibiting good adaptability across different types of machining tasks. The innovation lies in integrating the advantages of machine learning and evolutionary algorithms, breaking through the limitations of traditional optimization methods, and providing a new intelligent and versatile approach to machining process parameter optimization, offering theoretical support and technical guidance for the development of smart manufacturing.
Keyword:Mechanical Processing Parameter Optimization Machine Learning Genetic Algorithm
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 1
2机械加工工艺参数分析 2
2.1工艺参数对加工质量的影响 2
2.2工艺参数的测量与采集 2
2.3数据预处理与特征提取 3
3机器学习模型构建 4
3.1模型选择与算法原理 4
3.2模型训练与验证 4
3.3参数优化策略 5
4实验验证与结果分析 6
4.1实验设计与实施 6
4.2结果对比与评价 6
4.3应用案例分析 7
结论 8
参考文献 9
致谢 10
机械加工工艺参数优化对提高产品质量和生产效率具有重要意义，传统优化方法存在耗时长、精度低等问题。基于此，本文提出一种基于机器学习的机械加工工艺参数优化方法，旨在通过数据驱动的方式实现高效精准的参数优化。首先构建包含多种加工条件和结果的数据库，利用特征工程技术提取有效特征，为后续模型训练奠定基础。选用支持向量机、随机森林等机器学习算法建立预测模型，采用交叉验证等手段确保模型泛化能力。将优化问题转化为求解目标函数极值问题，运用遗传算法在预测模型基础上进行全局搜索以确定最优工艺参数组合。实验结果表明，该方法能够显著提升加工精度，降低表面粗糙度，与传统方法相比平均优化效率提高约30%，且在不同类型的加工任务中均表现出良好的适应性。创新点在于融合机器学习与进化算法优势，突破传统优化局限，提供了一种智能化、通用性强的机械加工工艺参数优化新思路，为智能制造领域的发展提供了理论依据和技术支持。
关键词：机械加工工艺参数优化 机器学习 遗传算法
Abstract
The optimization of machining process parameters is crucial for enhancing product quality and production efficiency. Traditional optimization methods suffer from issues such as time-consuming processes and low accuracy. To address these challenges, this paper proposes a machine learning-based approach for optimizing machining process parameters, aiming to achieve efficient and precise parameter optimization through data-driven methods. A database encompassing various machining conditions and outcomes is first constructed, and feature engineering techniques are employed to extract effective features, laying the foundation for subsequent model training. Machine learning algorithms such as Support Vector Machines (SVM) and Random Forests are selected to establish predictive models, with cross-validation and other techniques ensuring the generalization capability of the models. The optimization problem is transformed into solving the extremum of an ob jective function, and Genetic Algorithms are utilized to perform global searches based on the predictive models to determine the optimal combination of process parameters. Experimental results demonstrate that this method significantly improves machining accuracy and reduces surface roughness, achieving an average optimization efficiency improvement of approximately 30% compared to traditional methods, while exhibiting good adaptability across different types of machining tasks. The innovation lies in integrating the advantages of machine learning and evolutionary algorithms, breaking through the limitations of traditional optimization methods, and providing a new intelligent and versatile approach to machining process parameter optimization, offering theoretical support and technical guidance for the development of smart manufacturing.
Keyword:Mechanical Processing Parameter Optimization Machine Learning Genetic Algorithm
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 1
2机械加工工艺参数分析 2
2.1工艺参数对加工质量的影响 2
2.2工艺参数的测量与采集 2
2.3数据预处理与特征提取 3
3机器学习模型构建 4
3.1模型选择与算法原理 4
3.2模型训练与验证 4
3.3参数优化策略 5
4实验验证与结果分析 6
4.1实验设计与实施 6
4.2结果对比与评价 6
4.3应用案例分析 7
结论 8
参考文献 9
致谢 10
