人工智能技术在机械制造质量检测中的应用
摘 要
随着制造业向智能化转型，传统机械制造质量检测面临效率低、精度差等问题，人工智能技术的引入为解决上述问题提供了新思路。本研究旨在探讨人工智能技术在机械制造质量检测中的应用，以提高检测效率和精度。通过分析机械制造质量检测流程，选取深度学习算法作为核心方法，构建基于卷积神经网络的缺陷检测模型，该模型能够自动识别零部件表面微小缺陷。同时，引入迁移学习优化模型训练过程，减少数据标注工作量并提升模型泛化能力。实验结果表明，所提出的方法在多种复杂环境下均能实现高精度、高速度的缺陷检测，检测准确率达到95%以上，较传统方法有显著提升。此外，本研究还开发了智能检测系统平台，实现了从数据采集到结果反馈的一体化流程，为实际生产提供实时监控与预警功能。
关键词：人工智能技术 机械制造质量检测 深度学习算法
Abstract
With the transformation of the manufacturing industry to intelligence, the traditional machinery manufacturing quality testing is faced with problems such as low efficiency and poor accuracy. The introduction of artificial intelligence technology provides a new idea to solve the above problems. This study aims to investigate the application of AI technology in the quality detection of mechanical manufacturing to improve the detection efficiency and precision. By analyzing the mechanical manufacturing quality detection process and selecting the deep learning algorithm as the core method, we can build a defect detection model based on the convolutional neural network, which can automatically identify the minor defects on the surface of the parts. At the same time, transfer learning is introduced as an optimized model training process to reduce the workload of data annotation and improve the model generalization ability. The experimental results show that the proposed method can achieve high precision and high speed defect detection in various complex environments, and the detection accuracy reaches more than 95%, which is significantly improved compared with the traditional method. In addition, this study also developed the intelligent detection system platform, which realizes the integrated process from data collection to result feedback, and provides real-time monitoring and early warning function for the actual production.
Keyword:Artificial Intelligence Technology Machine Manufacturing Quality Inspection Deep Learning Algorithm
目 录
1绪论 1
1.1机械制造质量检测的背景与意义 1
1.2人工智能技术应用的研究现状 1
1.3本文研究方法概述 1
2人工智能技术基础及适用性分析 2
2.1人工智能技术原理简介 2
2.2机械制造质量检测需求分析 3
2.3人工智能技术与检测需求匹配 3
3智能算法在质量检测中的应用 4
3.1图像识别技术的应用实践 4
3.2数据挖掘技术的质量评估 4
3.3机器学习模型的构建与优化 5
4实施案例与效果评价 6
4.1典型企业应用案例分析 6
4.2检测效率与准确性的提升 6
4.3应用中遇到的问题及对策 7
结论 7
参考文献 9
致谢 10
摘 要
随着制造业向智能化转型，传统机械制造质量检测面临效率低、精度差等问题，人工智能技术的引入为解决上述问题提供了新思路。本研究旨在探讨人工智能技术在机械制造质量检测中的应用，以提高检测效率和精度。通过分析机械制造质量检测流程，选取深度学习算法作为核心方法，构建基于卷积神经网络的缺陷检测模型，该模型能够自动识别零部件表面微小缺陷。同时，引入迁移学习优化模型训练过程，减少数据标注工作量并提升模型泛化能力。实验结果表明，所提出的方法在多种复杂环境下均能实现高精度、高速度的缺陷检测，检测准确率达到95%以上，较传统方法有显著提升。此外，本研究还开发了智能检测系统平台，实现了从数据采集到结果反馈的一体化流程，为实际生产提供实时监控与预警功能。
关键词：人工智能技术 机械制造质量检测 深度学习算法
Abstract
With the transformation of the manufacturing industry to intelligence, the traditional machinery manufacturing quality testing is faced with problems such as low efficiency and poor accuracy. The introduction of artificial intelligence technology provides a new idea to solve the above problems. This study aims to investigate the application of AI technology in the quality detection of mechanical manufacturing to improve the detection efficiency and precision. By analyzing the mechanical manufacturing quality detection process and selecting the deep learning algorithm as the core method, we can build a defect detection model based on the convolutional neural network, which can automatically identify the minor defects on the surface of the parts. At the same time, transfer learning is introduced as an optimized model training process to reduce the workload of data annotation and improve the model generalization ability. The experimental results show that the proposed method can achieve high precision and high speed defect detection in various complex environments, and the detection accuracy reaches more than 95%, which is significantly improved compared with the traditional method. In addition, this study also developed the intelligent detection system platform, which realizes the integrated process from data collection to result feedback, and provides real-time monitoring and early warning function for the actual production.
Keyword:Artificial Intelligence Technology Machine Manufacturing Quality Inspection Deep Learning Algorithm
目 录
1绪论 1
1.1机械制造质量检测的背景与意义 1
1.2人工智能技术应用的研究现状 1
1.3本文研究方法概述 1
2人工智能技术基础及适用性分析 2
2.1人工智能技术原理简介 2
2.2机械制造质量检测需求分析 3
2.3人工智能技术与检测需求匹配 3
3智能算法在质量检测中的应用 4
3.1图像识别技术的应用实践 4
3.2数据挖掘技术的质量评估 4
3.3机器学习模型的构建与优化 5
4实施案例与效果评价 6
4.1典型企业应用案例分析 6
4.2检测效率与准确性的提升 6
4.3应用中遇到的问题及对策 7
结论 7
参考文献 9
致谢 10
