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Course Outline
Day 1: 09:00 - 16:00 (7h)
Core Concepts of Artificial Intelligence
- Defining AI, machine learning, and deep learning
- Learning paradigms: supervised, unsupervised, and reinforcement
- Separating AI myths from industrial realities
AI within Smart Manufacturing Contexts
- Defining the characteristics of a "smart" factory
- The role of AI in Industry 4.0 and industrial automation
- Introduction to enabling technologies such as IoT, edge computing, and digital twins
Primary Applications in Manufacturing
- Ensuring equipment reliability through predictive maintenance
- Enhancing quality assurance and detecting anomalies
- Improving yield through process optimization
Navigating the Data Lifecycle
- Sensing and gathering industrial data
- Addressing data preparation and quality standards
- Fundamental concepts in data-informed decision-making
Day 2: 09:00 - 16:00 (7h)
Planning and Strategy for AI Projects
- Pinpointing high-impact use cases
- Assembling the appropriate team and defining success metrics
- Addressing common challenges with effective mitigation strategies
Industry Case Studies and Applications
- Real-world examples from automotive, food, pharmaceutical, and heavy industries
- Key takeaways from digital transformation experiences
- Success factors and potential pitfalls to circumvent
Getting Started: A Roadmap
- Steps to initiate an AI initiative
- Considering technology choices and vendor selection
- Addressing scalability, ethics, and workforce adaptation
Recap and Future Actions
Requirements
- Familiarity with fundamental industrial processes or plant operations
- A keen interest in digital transformation or innovation strategies
- Ease in engaging with topics related to technology adoption
Target Audience
- Operations managers
- Plant executives
- Technical leads
14 Hours
Testimonials (2)
All in general
Daniele Donzelli - ITT ITALIA S.r.l.
Course - CANoe for CAN Compact Training
PLC basic knowledge