Course Description:
 

Artificial intelligence is rapidly transforming modern manufacturing operations by enabling smarter decision-making, improved asset reliability, enhanced product quality, optimized production performance, advanced automation, and more resilient manufacturing systems. Manufacturers across Michigan's automotive, aerospace, defense, industrial equipment, metal fabrication, plastics, and advanced manufacturing sectors are increasingly adopting AI technologies to remain competitive in an environment characterized by workforce challenges, supply chain complexity, rising operational costs, and increasing customer expectations.

This course provides manufacturing engineers with a comprehensive examination of how artificial intelligence technologies are being applied throughout the manufacturing lifecycle. Participants will explore the technical foundations of AI-enabled manufacturing systems, including machine learning, computer vision, predictive analytics, Industrial Internet of Things infrastructure, smart factory architectures, and digital manufacturing platforms. The course explains how these technologies integrate with traditional manufacturing engineering principles to support operational excellence, quality improvement, reliability enhancement, and data-driven decision-making.

The course begins with an introduction to artificial intelligence within manufacturing environments, examining the evolution of industrial automation, Industry 4.0 concepts, machine learning technologies, computer vision systems, generative AI applications, and the growing role of data-driven engineering. Participants will gain an understanding of how AI differs from traditional automation and how modern manufacturing organizations are leveraging advanced analytics to improve operational performance.

Participants will then examine the manufacturing data foundations necessary to support artificial intelligence initiatives. Topics include Industrial Internet of Things technologies, sensor systems, programmable logic controllers, supervisory control and data acquisition systems, Manufacturing Execution Systems, Enterprise Resource Planning platforms, data governance, interoperability, cloud computing, edge computing, cybersecurity considerations, and smart factory infrastructure development. Emphasis is placed on the importance of reliable, secure, and high-quality data as the foundation of successful AI implementations.

The course explores AI-driven predictive maintenance and reliability engineering practices that enable manufacturers to anticipate equipment failures before they occur. Participants will learn how machine learning models utilize vibration analysis, temperature monitoring, electrical measurements, lubrication monitoring, acoustic analysis, anomaly detection, and Remaining Useful Life estimation to improve equipment reliability, reduce unplanned downtime, and support asset management objectives.

A comprehensive examination of AI applications for quality control and defect detection follows. Participants will study computer vision technologies, machine learning-based inspection systems, predictive quality analytics, Statistical Process Control integration, metrology applications, root cause analysis techniques, quality management system considerations, and methods for improving defect detection accuracy while maintaining engineering oversight and product quality assurance requirements.

The course also addresses production optimization and process improvement applications. Participants will learn how artificial intelligence supports throughput enhancement, bottleneck identification, scheduling optimization, inventory management, workforce planning, supply chain resilience, energy management, and continuous improvement initiatives. The integration of AI with lean manufacturing principles, Theory of Constraints methodologies, and enterprise manufacturing systems is examined in detail.

Industrial robotics, automation systems, and human-machine collaboration are explored through an examination of machine vision, robotic guidance systems, adaptive automation, autonomous mobile robots, collaborative robots, workforce augmentation technologies, functional safety requirements, risk assessment methodologies, and robotic system integration practices. Particular attention is given to balancing automation capabilities with human expertise and engineering accountability.

The course further examines digital twins and AI-enabled manufacturing simulation technologies. Participants will learn how real-time operational data, predictive analytics, simulation models, and virtual manufacturing environments support production planning, capacity analysis, predictive maintenance, energy optimization, workforce training, virtual commissioning, and strategic decision-making. The importance of data quality, model validation, cybersecurity, and engineering oversight within digital twin environments is emphasized throughout the discussion.

The final module focuses on cybersecurity, risk management, governance, and responsible AI use within manufacturing operations. Topics include industrial cybersecurity frameworks, operational technology security, AI-specific cybersecurity threats, model validation, data integrity, governance structures, risk management practices, explainability, transparency, human oversight requirements, and the responsible deployment of artificial intelligence technologies within engineering environments.

Throughout the course, Professional Judgment Alerts highlight critical situations in which manufacturing engineers must carefully evaluate AI-generated recommendations and maintain appropriate oversight of decisions affecting safety, quality, reliability, cybersecurity, environmental compliance, regulatory obligations, and operational performance. These alerts reinforce the principle that artificial intelligence serves as a powerful engineering tool but does not replace professional responsibility, engineering accountability, or sound technical judgment.

Three detailed case studies provide practical applications of course concepts within real-world manufacturing environments. The first case study examines the implementation of AI-driven predictive maintenance within a Michigan automotive manufacturing facility, demonstrating how machine learning models, condition monitoring systems, and engineering validation processes can improve equipment reliability and reduce downtime. The second case study explores the deployment of computer vision quality inspection systems within a precision manufacturing operation, illustrating the importance of dataset development, validation procedures, quality management integration, and engineering oversight. The third case study examines the implementation of a facility-wide digital twin within an industrial manufacturing environment, highlighting the integration of artificial intelligence, simulation technologies, production optimization, predictive analytics, cybersecurity controls, and operational decision support.

Upon completion of this course, participants will possess a practical understanding of how artificial intelligence technologies are being deployed throughout modern manufacturing operations, the infrastructure required to support successful implementation, the risks and limitations associated with AI systems, and the critical role of engineering judgment in ensuring safe, reliable, secure, and effective manufacturing performance.
 

Learning Objectives:
 

Upon completion of this course, participants will be able to:

1. Distinguish between traditional manufacturing automation systems and artificial intelligence technologies used in modern manufacturing environments.

2. Identify the manufacturing data, sensor, connectivity, and smart factory infrastructure required to support AI-enabled manufacturing applications.

3. Evaluate how artificial intelligence supports predictive maintenance, reliability engineering, and asset management decision-making.

4. Assess the use of computer vision, machine learning, and quality analytics technologies for defect detection, process monitoring, and manufacturing quality improvement.

5. Analyze opportunities to apply artificial intelligence to production optimization, scheduling, throughput improvement, inventory management, energy efficiency, and continuous improvement initiatives.

6. Evaluate the role of artificial intelligence in industrial robotics, automation systems, collaborative robotics, and human-machine interaction within manufacturing operations.

7. Assess how digital twins and AI-enabled simulation technologies support manufacturing planning, predictive analytics, operational optimization, and strategic decision-making.

8. Recognize cybersecurity, risk management, governance, and responsible AI practices necessary for the safe, reliable, and effective deployment of artificial intelligence within manufacturing environments.

9. Apply engineering judgment principles when evaluating AI-generated recommendations affecting manufacturing safety, quality, reliability, productivity, cybersecurity, and regulatory compliance.