摘要
激光切割作为一种高效、精密的现代加工技术，广泛应用于航空航天、汽车制造和电子工业等领域。然而，工艺参数对切割质量的影响复杂且非线性，亟需系统优化以提升加工精度与效率。本研究旨在通过分析关键工艺参数（如功率、切割速度、焦距和气体压力）对切割质量的影响规律，建立科学的参数优化模型，从而实现高质量的激光切割。研究采用正交试验设计结合响应面法，构建了切割宽度、表面粗糙度及热影响区等质量指标的数学模型，并利用遗传算法进行多目标优化。结果表明，各工艺参数对不同质量指标的作用机制存在显著差异，其中功率和切割速度是主导因素。优化后的工艺参数显著提升了切割质量，使表面粗糙度降低约25%，热影响区减少约18%。此外，本研究创新性地引入了基于机器学习的预测模型，用于实时评估切割质量，为智能化激光加工提供了理论支持和技术保障。研究结论不仅揭示了工艺参数与切割质量间的内在关系，还为实际生产中的参数选择提供了指导，具有重要的学术价值和工程应用前景。
关键词：激光切割；工艺参数优化；切割质量；响应面法；机器学习预测模型
Abstract
Laser cutting, as an efficient and precise modern manufacturing technology, has been widely applied in fields such as aerospace, automotive manufacturing, and electronics industry. However, the influence of process parameters on cutting quality is complex and nonlinear, necessitating systematic optimization to enhance processing accuracy and efficiency. This study aims to analyze the impact patterns of key process parameters, including power, cutting speed, focal length, and gas pressure, on cutting quality and establish a scientific parameter optimization model for achieving high-quality laser cutting. The research adopted an orthogonal experimental design combined with response surface methodology to construct mathematical models for quality indicators such as cutting width, surface roughness, and heat-affected zone, and utilized genetic algorithms for multi-ob jective optimization. The results indicate that the mechanisms by which various process parameters affect different quality indicators differ significantly, with power and cutting speed being the dominant factors. After optimization, the process parameters substantially improved cutting quality, reducing surface roughness by approximately 25% and the heat-affected zone by about 18%. Additionally, this study innovatively introduced a machine-learning-based predictive model for real-time evaluation of cutting quality, providing theoretical support and technical assurance for intelligent laser processing. The conclusions not only reveal the intrinsic relationship between process parameters and cutting quality but also offer guidance for parameter selection in practical production, demonstrating significant academic value and engineering application potential.
Keywords：Laser Cutting; Process Parameter Optimization; Cutting Quality; Response Surface Methodology; Machine Learning Prediction Model
目 录
摘要 I
Abstract II
一、绪论 1
(一) 激光切割工艺的研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法概述 2
二、激光切割工艺参数影响机制分析 2
(一) 激光功率对切割质量的影响 2
(二) 切割速度与材料去除效率的关系 3
(三) 辅助气体压力的作用机制 3
三、工艺参数优化方法研究 4
(一) 参数优化的数学建模方法 4
(二) 基于试验设计的参数优化策略 4
(三) 优化算法在激光切割中的应用 5
四、切割质量评价与改进措施研究 5
(一) 切割质量评价指标体系构建 5
(二) 表面粗糙度与热影响区分析 6
(三) 提高切割质量的技术改进方案 7
结 论 8
参考文献 9
