Before data makes it into the MES, it first passes through:
- Measuring instruments and actuators (like sensors and valves)
- Control Systems (like PLCs)
- Supervisory Control Systems (like a SCADA)
On the line, data is gathered straight from the source. Flow meters, level switches, temperature sensors, and other instruments measure key values as components are being processed. Valves and pumps, among other actuators regulate your measuring instruments, enabling engineers to control variables on the line. In other words, actuators are used to change a measured parameter, like flow or pressure.
Programmable Logic Controllers, like PLCs, interact with sensors (and sometimes actuators). They can be thought of as “tellers” and “reporters”; they tell a machine or tool on the line what to do, and then report whether the command was executed. If it wasn’t, the PLC reports why. In both cases, it aggregates the data and sends it to MES or other systems for reporting.
Let’s look at a cylinder head rundown station. An engine block comes into the station with a newly located cylinder head sitting on top. Once the switch is made in the station saying the part is in position, the serial number and model number are read from one of any number of devices (cameras for barcodes, Radio frequency ID tags that ride on the part, etc). That information is then sent to the PLC. The PLC then sends that data to the DC fastening tool controllers, and the controllers acknowledge the model type and select the pre-configured program they will run. The PLC tells the carrier assembly holding the tools to lower into position and to slowly rotate the tools. This allows the socket head to engage the bolt it will fasten. The DC tool then spins and tightens the fasteners.
This produces a torque vs time and angle vs time process signature and records final torque and final angle values for each bolt. Those values, as well as the pass/fail status for each bolt, are sent to the PLC.
The PLC will then append that data to the serial number and model number, and send it up to a SCADA or a MES. The process signatures may be stored in other locations as well (several options) for reprocessing later.
MES are used to manage, track, control, document and improve operations or processes; however, the core capabilities of MES and SCADAs sometimes overlap.
On its own, a SCADA can acquire information on traceability, data acquisition, reporting, secure user management, maintenance management, or other process-level tasks. Operators or technicians can visualize their assembly lines as a whole, and make changes to processes when needed. A SCADA with extended MES functionality might be able to store real-time data at a granular level, and track production processes through its user interface. On the other hand, a MES system with custom extensions can allow operators to visualize production units in a way that is similar to a SCADA. Ultimately, SCADA and MES can be used separately or in tandem, and a manufacturer would opt for the system (or combination of systems) that best suits their needs.
MES is often connected to an organization’s enterprise resource planning (ERP), with the ERP system taking data from one or more manufacturing execution systems , centralizing all of the information about a business. Everything from procurement, to accounting, to maintenance—even scrap and rework rates—is all available in one place.
Through this hierarchy and complete MES integration, engineers are able to observe the entire product life cycle. Ideally, a component’s complete history could be followed from when the raw material enters the plant to final end-of-line test, through each and every operation and critical step in the process.
It’s here that machine learning can combine with MES to unlock relationships between different sources and signals, providing complete insight into a facility’s operations.
In a perfect world, data would be passed through this hierarchy flawlessly. However in reality, the data that makes it to MES varies from plant to plant. And so, the question arises: What data does a MES need to collect in order to apply machine learning?