Organization: Raytheon Missile Systems, Tucson, AZ
Date: October 2013 – March 2014
Background: As part of a factory modernization campaign, a system was needed to track machine utilization rates to drive equipment preventive maintenance schedules over the traditional calendar-based scheduling method. The new lean-method would lower machine maintenance cost by servicing equipment only when needed. A machine monitoring system was developed to acquire data on coolant run-time of factory CNC machines via programmable logic controllers (PLCs). The data sample rate was set to 10 seconds, with the data being stored locally for machine shop management to track and assign preventive maintenance work orders according original-equipment manufacturers (OEM) maintenance thresholds.
Objective: To make the ultra-precision fabrication factory smarter and more efficient by implementing a system to track CNC (computer numerical control) machine utilization rates; the acquired data on machine run-time was used to create machine preventive maintenance orders that was based on actual tool usage as opposed to the traditional-method of a calendar-based system.
My Position: Project Engineer
My Role: As the project engineer working alongside with a team of four automation engineers, we defined system functional and technical requirements. I worked with organizational supply chain to obtain vendor quotes, and worked with management to down-select a supplier. I oversaw the design of the project, then the follow on installation of the system. I also performed the initial prove-in of the system, trouble-shooting, and set the data-acquisition parameters (sample rate, filtering, and data retention).
Technical Fundamentals: Control Systems
Skills: Six-sigma methodology, automation, root-cause analysis, data acquisition, supply-chain, and project management
Technology Tools: Allen-Bradley PLCs, FactoryTalk View – HMI Software, FactoryTalk Analytics for Devices, Studio 5000 (ladder logic)
Results / Outcome: Achieved an estimated 70% annual savings in labor and material costs associated with preventive maintenance.