- 了解基本的制造或QA流程
- 熟悉电子表格或数字形式的报告
- 对数据驱动的质量控制方法感兴趣
受众
- 质量保证专家
- 生产主管
AI用于质量控制是利用计算机视觉和机器学习技术来识别生产过程中的缺陷、异常和偏差。
本次由讲师主导的培训（线上或线下）面向希望应用AI工具来自动化检测并提高制造环境中产品质量的初级到中级质量专业人员。
通过本次培训，参与者将能够：
- 了解AI在工业质量控制中的应用。
- 收集并标记来自生产线的图像或传感器数据。
- 使用机器学习和计算机视觉来检测缺陷。
- 开发用于异常检测和产量预测的简单AI模型。
课程形式
- 互动式讲座和讨论。
- 大量练习和实践。
- 在实验环境中进行实际操作。
课程定制选项
- 如需定制本课程，请联系我们安排。
人工智能在质量控制中的应用介绍
- 人工智能在制造质量流程中的概述
- 在检测、缺陷识别和合规性中的应用
- 人工智能驱动的质量保证的优缺点
收集和准备质量数据
- 用于质量保证的数据类型（图像、传感器、生产日志）
- 使用LabelImg标注视觉数据集
- 用于训练模型的数据存储和结构
Computer Vision在质量保证中的介绍
- 使用OpenCV进行图像处理的基础知识
- 工业图像的预处理技术
- 提取视觉特征进行分析
Machine Learning在异常检测中的应用
- 训练简单的分类器进行缺陷检测
- 使用卷积神经网络（CNNs）
- 无监督学习用于异常识别
使用人工智能模型进行产量Forecasting预测
- 回归技术介绍
- 构建模型以预测生产产量
- 评估和提高预测准确性
将人工智能与生产系统集成
- 检测模型的部署选项
- Edge AI与基于云的分析对比
- 自动化警报和质量报告
实际案例研究与最终项目
- 开发端到端的人工智能检测原型
- 使用样本质量保证数据集进行训练和测试
- 展示一个功能性的质量控制人工智能解决方案
总结与下一步
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