- 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 for Quality Control 是利用電腦視覺和機器學習技術來識別生產過程中的缺陷、異常和偏差。
這門由講師指導的培訓（線上或線下）面向初級到中級的品質專業人員，他們希望應用 AI 工具來自動化檢查並提高製造環境中的產品品質。
在培訓結束時，學員將能夠：
- 了解 AI 如何在工業品質控制中應用。
- 收集並標記來自生產線的圖像或感測器數據。
- 使用機器學習和電腦視覺來檢測缺陷。
- 開發簡單的 AI 模型來進行異常檢測和產量預測。
課程形式
- 互動式講座和討論。
- 大量練習和實踐。
- 在即時實驗室環境中進行實際操作。
課程定制選項
- 如需為本課程定制培訓，請聯繫我們安排。
AI在品質控制中的介紹
- AI在製造品質流程中的概述
- 在檢查、缺陷檢測和合規性中的應用
- AI驅動的品質保證的優點與限制
收集與準備品質數據
- 用於品質保證的數據類型（圖像、傳感器、生產日誌）
- 使用LabelImg標記視覺數據集
- 用於訓練模型的數據存儲與結構
Computer Vision在品質保證中的介紹
- 使用OpenCV進行圖像處理的基礎
- 工業圖像的預處理技術
- 提取視覺特徵進行分析
Machine Learning用於異常檢測
- 訓練簡單分類器進行缺陷檢測
- 使用卷積神經網絡（CNNs）
- 無監督學習進行異常識別
使用AI模型進行產量Forecasting
- 回歸技術的介紹
- 建立模型預測生產產量
- 評估與提高預測準確性
將AI與生產系統集成
- 檢查模型的部署選項
- Edge AI與基於雲的分析對比
- 自動化警報與品質報告
實用案例研究與最終項目
- 開發端到端的AI檢查原型
- 使用樣本品質數據集進行訓練與測試
- 展示一個功能性的品質控制AI解決方案
總結與下一步
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