NSBC - Level II Automation Engineer

Description of Duties:

Responsible for engineering, developing, optimizing, and maintaining Level 2 computer automation systems to support steel production operations in reversing cold mills. Apply cold rolling theory, thermal physics, modern physics, multivariable calculus, ordinary differential equations, chemistry, and electrical engineering principles to develop and optimize mill process setup models for reversing cold mill automation systems. Coil tracking system for managing coil movements on walking beams, roll change events, coil schedule planning, coil order planning, and roll data management. Mill production environment system controlling entry payoff reel, entry tension reel, mill stand controller, hydraulic cylinder systems, mill motors, automatic strip gauge control, x-ray gauge systems, stackable work and backup rolls, and exit tension reel. Profile and flatness setup models monitoring/tracking strip gauge deviation, width deviation, and surface shape distribution (head/body/tail) based on ASTM standards and customer tolerances. Thermal crown calculation models considering work roll cooling, lubrication, rolling temperature (atomic recrystallization), friction between rolls and materials, and work roll diameter stock loss. Rolling force and strip deformation models covering strip gauge reduction pass schedules, hydraulic roll force, work roll bending and shifting, entry/exit reel tensions, and other mechanical forces affecting strip behavior. Apply Computing Principles for Electrical Engineers in Level 2 automation systems, utilizing advanced programming skills to develop and implement software solutions for steel mill operations. Implement and maintain reliable supervisory control and data acquisition (SCADA) systems for real-time high-speed mill data collection, diagnostics, and mill control in coordination with Level 1 (PLC) systems through software Human-machine interfaces (HMIs), databases, product quality and process parameter historians to maximize safety, quality, and production throughput. Apply machine learning, artificial intelligence, and statistics in manufacturing for strategic production planning, mill process data analysis, incoming coil quality forecasting, anomaly detection, and risk prediction of possible mill wreckage using historical data logs. Implement introduction to Linear Algebra for Engineering and matrix theory and underlying concepts. Build Human-machine interfaces (HMIs) to deliver production planning schedules, production reports, downtime logging, quality performance tracking, incident data logging, safety tools, energy consumption monitoring, automatic email/text notifications, in support of continuous mill process improvement. Utilize Database system architecture; conceptual database models; semantic, object-oriented, logic based, and relational databases; user and program interfaces. Maintain the Cold Mill Level 2 automation infrastructure by administering SQL databases, managing system resources, and upgrading server and software environments. Utilize Probability for Electrical and Computer Engineers to provide rigorous coverage of probability, discrete and continuous random variables, functions of multiple random variables, covariance, correlation, and introduction to statistics. Incorporate Machine Learning I: Supervised Methods in manufacturing and statistics for mill process data analysis. Service and maintain industrial communication and distributed systems, including configuring IP addresses, virtual local area networks (VLANs), switches, and TCP/UDP communication protocols. Collaborate with cross-functional teams, cold mill operations, cold mill electrical and mechanical, IT, and quality departments to ensure cold mill automation systems meet cold mill/plant production targets, cost and profit goals, system reliability, safety standards, cybersecurity requirements, and customer quality expectations. 

Minimum Qualifications:

Must have a Master’s degree in Electrical Engineering or related field. Coursework must include: Computing Principles for Electrical Engineers; Probability for Electrical and Computer Engineers; Linear Algebra for Engineering; Database Systems; and Machine Learning I: Supervised Methods.  Must submit resume and transcript.

Additional Details

• PREVAILING WORKING CONDITIONS: N/A
• HOURS: 40/week
• EMPLOYER TRAINING PROVIDED: N/A
• OTHER COMPARABLE WAGES TERMS & CONDITIONS OF EMPLOYMENT: N/A
• APPLICANTS ARE TO REPORT TO: Please mail resume and transcript to Human Resources, 1455 Hagan Avenue, Huger, SC 29450 or apply online at www.nucor.com/careers.