摘 要
精密加工工艺作为现代制造业的核心技术之一，对机械零部件的质量具有决定性影响。随着高端装备制造需求的不断增长，研究精密加工工艺与零部件质量之间的关系显得尤为重要。本研究旨在探讨不同精密加工工艺参数对机械零部件表面粗糙度、尺寸精度及材料微观结构的影响，并提出优化方案以提升零部件的整体性能。研究采用实验分析与数值模拟相结合的方法，选取典型金属材料为研究对象，通过设计正交实验和有限元仿真，系统评估切削速度、进给量、切削深度等关键参数的作用机制。结果表明，合理的工艺参数组合能够显著降低表面粗糙度并提高尺寸精度，同时改善材料内部的应力分布和晶粒组织。此外，本研究创新性地引入人工智能算法对加工过程进行预测与优化，实现了加工效率与质量的协同提升。研究表明，基于数据驱动的工艺参数优化方法可有效减少废品率并延长刀具使用寿命，为实际生产提供了重要的理论指导和技术支持。总体而言，本研究不仅揭示了精密加工工艺对零部件质量的具体影响规律，还提出了切实可行的改进措施，为推动制造业向高精度、高效能方向发展做出了积极贡献。
关键词：表面粗糙度；尺寸精度；人工智能优化；材料微观结构
Abstract
Precision machining technology, as one of the core technologies in modern manufacturing, plays a decisive role in the quality of mechanical components. With the growing demand for high-end equipment manufacturing, investigating the relationship between precision machining processes and component quality has become particularly significant. This study aims to explore the effects of different precision machining process parameters on the surface roughness, dimensional accuracy, and microstructure of materials in mechanical components, while proposing optimization strategies to enhance overall component performance. By integrating experimental analysis with numerical simulation, this research selects typical me tallic materials as study subjects and employs orthogonal experiments and finite element simulations to systematically evaluate the mechanisms of key parameters such as cutting speed, feed rate, and depth of cut. The results indicate that an appropriate combination of process parameters can significantly reduce surface roughness and improve dimensional accuracy, while also optimizing stress distribution and grain structure within the material. Furthermore, this study innovatively incorporates artificial intelligence algorithms for predicting and optimizing the machining process, achieving a synergistic improvement in processing efficiency and quality. The findings demonstrate that data-driven optimization methods for process parameters can effectively decrease defect rates and extend tool life, providing crucial theoretical guidance and technical support for practical production. Overall, this study not only elucidates the specific influence patterns of precision machining processes on component quality but also proposes feasible improvement measures, making a positive contribution to advancing the manufacturing industry towards higher precision and efficiency.
Keywords：Surface Roughness；Dimensional Accuracy；Artificial Intelligence Optimization；Material Microstructure
目 录
摘 要 I
Abstract II
引 言 1
第一章 精密加工工艺概述 2
1.1 精密加工的基本概念 2
1.2 精密加工的关键技术 2
1.3 精密加工的发展历程 3
第二章 机械零部件质量要求分析 4
2.1 零部件质量的核心指标 4
2.2 影响零部件质量的主要因素 4
2.3 零部件精度与功能的关系 5
2.4 质量控制的常见方法 5
第三章 精密加工对零部件质量的影响机制 7
3.1 加工误差对质量的影响 7
3.2 表面粗糙度的作用分析 7
3.3 材料特性与加工效果的关系 8
3.4 工艺参数优化的重要性 8
第四章 提高精密加工质量的策略研究 10
4.1 先进设备的应用与选择 10
4.2 数字化技术在加工中的作用 10
4.3 工艺流程的改进措施 11
4.4 质量检测与反馈机制 11
结 论 13
参考文献 14
致 谢 15
精密加工工艺作为现代制造业的核心技术之一，对机械零部件的质量具有决定性影响。随着高端装备制造需求的不断增长，研究精密加工工艺与零部件质量之间的关系显得尤为重要。本研究旨在探讨不同精密加工工艺参数对机械零部件表面粗糙度、尺寸精度及材料微观结构的影响，并提出优化方案以提升零部件的整体性能。研究采用实验分析与数值模拟相结合的方法，选取典型金属材料为研究对象，通过设计正交实验和有限元仿真，系统评估切削速度、进给量、切削深度等关键参数的作用机制。结果表明，合理的工艺参数组合能够显著降低表面粗糙度并提高尺寸精度，同时改善材料内部的应力分布和晶粒组织。此外，本研究创新性地引入人工智能算法对加工过程进行预测与优化，实现了加工效率与质量的协同提升。研究表明，基于数据驱动的工艺参数优化方法可有效减少废品率并延长刀具使用寿命，为实际生产提供了重要的理论指导和技术支持。总体而言，本研究不仅揭示了精密加工工艺对零部件质量的具体影响规律，还提出了切实可行的改进措施，为推动制造业向高精度、高效能方向发展做出了积极贡献。
关键词：表面粗糙度；尺寸精度；人工智能优化；材料微观结构
Abstract
Precision machining technology, as one of the core technologies in modern manufacturing, plays a decisive role in the quality of mechanical components. With the growing demand for high-end equipment manufacturing, investigating the relationship between precision machining processes and component quality has become particularly significant. This study aims to explore the effects of different precision machining process parameters on the surface roughness, dimensional accuracy, and microstructure of materials in mechanical components, while proposing optimization strategies to enhance overall component performance. By integrating experimental analysis with numerical simulation, this research selects typical me tallic materials as study subjects and employs orthogonal experiments and finite element simulations to systematically evaluate the mechanisms of key parameters such as cutting speed, feed rate, and depth of cut. The results indicate that an appropriate combination of process parameters can significantly reduce surface roughness and improve dimensional accuracy, while also optimizing stress distribution and grain structure within the material. Furthermore, this study innovatively incorporates artificial intelligence algorithms for predicting and optimizing the machining process, achieving a synergistic improvement in processing efficiency and quality. The findings demonstrate that data-driven optimization methods for process parameters can effectively decrease defect rates and extend tool life, providing crucial theoretical guidance and technical support for practical production. Overall, this study not only elucidates the specific influence patterns of precision machining processes on component quality but also proposes feasible improvement measures, making a positive contribution to advancing the manufacturing industry towards higher precision and efficiency.
Keywords：Surface Roughness；Dimensional Accuracy；Artificial Intelligence Optimization；Material Microstructure
目 录
摘 要 I
Abstract II
引 言 1
第一章 精密加工工艺概述 2
1.1 精密加工的基本概念 2
1.2 精密加工的关键技术 2
1.3 精密加工的发展历程 3
第二章 机械零部件质量要求分析 4
2.1 零部件质量的核心指标 4
2.2 影响零部件质量的主要因素 4
2.3 零部件精度与功能的关系 5
2.4 质量控制的常见方法 5
第三章 精密加工对零部件质量的影响机制 7
3.1 加工误差对质量的影响 7
3.2 表面粗糙度的作用分析 7
3.3 材料特性与加工效果的关系 8
3.4 工艺参数优化的重要性 8
第四章 提高精密加工质量的策略研究 10
4.1 先进设备的应用与选择 10
4.2 数字化技术在加工中的作用 10
4.3 工艺流程的改进措施 11
4.4 质量检测与反馈机制 11
结 论 13
参考文献 14
致 谢 15
