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Course Outline

  • Introduction to AI and its applications in manufacturing
  • Product quality analysis using AI: defect and anomaly identification
  • Product and process optimization based on collected data
  • Predictive maintenance: AI models for anticipating equipment failures
  • Integrating AI results into dashboards and KPI reports
  • Best practices for making data-driven decisions in production

 Expected outcomes:

  • Understanding the principles of AI applied in quality analysis and product optimization
  • Ability to identify and interpret anomalies and defects in production
  • Applying AI for predictive maintenance and reducing downtime
  • Creating dashboards and reports for KPI monitoring and decision-making

Requirements

  • Understanding of: AI concepts for quality analysis, product optimization, and predictive maintenance
  • Experience with: production data, reporting systems, Excel, and visual KPI tools
  • Programming experience: basic knowledge of Python, R, or other languages for AI applications (optional)
  • Target audience: production engineers, quality specialists, operational managers, data analysts

 

  • Covered topics: product quality analysis and defect identification
  • Covered topics: product and process optimization using AI
  • Covered topics: predicting failures and making data-driven decisions
 7 Hours

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