MANF 454 - Industrial Automation

Credits: 5
Variable Credit Course: No

Lecture Hours: 55
Lab Hours: 0
Worksite/Clinical Hours: 0
Other Hours (LIA/Internships): 0

Course Description: Examines automation principles and applications in advanced manufacturing. Configure and integrate technologies such as robotic cells, sensors, SCADA systems, and digital twins to optimize manufacturing processes. Emphasizes data analytics, collaborative robotics, and rigorous safety protocols to enhance operational efficiency and ensure workplace safety.

Prerequisite: Admission to BASAMD program and Dept. Chair permission.
Meets FQE Requirement: No
Integrative Experience Requirement: No

Student Learning Outcomes

Evaluate advanced robotic and motion control systems to optimize throughput and ensure safe operation in manufacturing environments.
Formulate integrated control strategies by synthesizing technologies like PLC programming, SCADA systems, and sensor data to enhance productivity and reliability.
Analyze digital twin simulations to predict system performance, identify potential failures, and propose preventive measures for continuous improvement.
Design industrial automation workflows leveraging collaborative robotics and data analytics to improve ergonomic conditions and reduce downtime.

Course Contents

Robotics Integration in Manufacturing Cells - Investigates robot configuration, programming, and safety in automated workflows.
Sensors & Actuators for Process Monitoring - Reviews device selection, signal processing, and reliability in controlling production parameters.
Supervisory Control & Data Acquisition (SCADA) Systems - Explores real-time data capture, alarm management, and remote operation for manufacturing oversight.
Motion Control Systems & Drive Technologies - Examines servo motors, variable frequency drives, and motion profiles to synchronize automated processes.
Automation Safety Systems & Protocols - Addresses protective measures such as light curtains, emergency stops, and integrated safety PLCs.
Digital Twins & Virtual Commissioning - Applies simulation tools to predict system performance and troubleshoot before physical deployment.
Data Analytics in Automated Manufacturing - Utilizes production data to improve efficiency, detect anomalies, and support predictive maintenance.
PLC & MicroPython Programming - Implements code-based control logic for advanced machine operations, integrating sensors and actuators.
Collaborative Robotics & Human-Machine Interaction - Considers ergonomic design, safety, and efficiency when working with cobots.
Preventive Maintenance & Condition Monitoring - Uses sensors and diagnostic methods to schedule repairs, reducing downtime and extending equipment life.

Instructional Units: 5