摘要
焊接缺陷的无损检测与智能化识别是现代工业制造领域的重要研究方向，其对提升产品质量和保障结构安全具有关键意义。随着工业技术的发展，传统无损检测方法已难以满足复杂焊接结构的高精度检测需求，因此亟需引入智能化技术以提高检测效率与准确性。本研究旨在探索先进的无损检测技术，并结合人工智能算法实现焊接缺陷的高效识别与分类。研究采用超声波检测、射线检测及红外热成像等多种无损检测手段获取焊接区域的物理特性数据，并通过深度学习模型对采集的数据进行特征提取与模式识别。实验结果表明，基于卷积神经网络的智能识别方法能够显著提升缺陷检测的准确率，同时大幅缩短分析时间。此外，本研究提出了一种多模态数据融合策略，有效解决了单一检测技术在复杂工况下的局限性问题。研究的主要贡献在于将智能化技术与传统无损检测方法深度融合，为焊接缺陷的精准评估提供了新思路，同时为工业领域的智能化升级奠定了理论和技术基础。
关键词：焊接缺陷；无损检测；智能化识别；深度学习；多模态数据融合
Abstract
The non-destructive testing and intelligent recognition of welding defects are important research directions in the modern industrial manufacturing field, playing a critical role in enhancing product quality and ensuring structural safety. With the advancement of industrial technology, traditional non-destructive testing methods have become insufficient to meet the high-precision requirements for complex welded structures, thus necessitating the integration of intelligent technologies to improve detection efficiency and accuracy. This study focuses on exploring advanced non-destructive testing techniques combined with artificial intelligence algorithms to achieve efficient identification and classification of welding defects. Ultrasonic testing, radiographic testing, infrared thermography, and other non-destructive methods were employed to acquire physical characteristic data from the welding area, followed by feature extraction and pattern recognition using deep learning models. Experimental results demonstrate that intelligent recognition methods based on convolutional neural networks can significantly enhance the accuracy of defect detection while substantially reducing analysis time. Furthermore, this study proposes a multimodal data fusion strategy that effectively addresses the limitations of single detection technologies under complex operating conditions. The primary contribution of this research lies in the deep integration of intelligent technologies with traditional non-destructive testing methods, providing new insights into the precise evaluation of welding defects and laying a theoretical and technical foundation for the intelligent upgrading of the industrial sector.
Keywords：Welding Defect; Non-Destructive Testing; Intelligent Recognition; Deep Learning; Multi-Modal Data Fusion
目 录
摘要 I
Abstract II
一、绪论 1
(一) 焊接缺陷检测的研究背景与意义 1
(二) 无损检测技术与智能化识别的现状分析 1
(三) 本文研究方法与技术路线 2
二、焊接缺陷无损检测技术原理与分类 2
(一) 常见焊接缺陷类型及其特征 2
(二) 主要无损检测技术原理分析 3
(三) 不同检测技术的适用性与局限性 3
(四) 检测技术的选择与优化策略 4
三、智能化识别在焊接缺陷检测中的应用 4
(一) 智能化识别的基本理论与方法 4
(二) 数据采集与处理在智能化识别中的作用 5
(三) 机器学习算法在缺陷识别中的应用 5
(四) 深度学习模型对复杂缺陷的识别能力 6
四、焊接缺陷无损检测与智能化识别的实践案例分析 6
(一) 实际工程中焊接缺陷的特点与挑战 6
(二) 典型案例中无损检测技术的应用效果 7
(三) 智能化识别在实际检测中的优势体现 7
(四) 提高检测效率与准确性的改进措施 8
结 论 9
参考文献 10
