Automation For Information

There is a wealth of information already in the shop. Here is how to profit from it. (Sponsored Content)

Advertorial From: 4/22/2005 Modern Machine Shop

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"Making chips" is a common-enough expression for describing production metalworking, but chips are not the only byproduct of production. Another byproduct is information. In addition to making chips, you're also making data. And today, it's possible to put that data to use.

Here are examples of the kinds of data that are out there. At any given moment, your machine tool is either cutting or it's not. The machine is in service or it's not. And if the machine is cutting, then a particular tool is loaded in the spindle, and the spindle is experiencing a particular load.

All of this information has always existed, but in the past it was difficult to gather. The information was available only through observation, only by someone standing at the control. But now, CNC networking capability makes it possible to gather information remotely, transfer information with ease, and analyze information from throughout the plant on a single screen. Because it can simplify so much of the work of empowering and overseeing the manufacturing process, a shopfloor network might prove to be the most powerful productivity tool in the plant.

In fact, for the manufacturer that uses information well, the very nature of overall equipment effectiveness (see Overall Equipment Effectiveness: The Formula For Finding Your Plant's Hidden Potential) might undergo a fundamental change. Moving, monitoring and managing the shop's data can help to improve all three OEE components—availability, performance and quality. After that, the network makes it possible to track these components, continuously recalculating them from the most recent data. With this current information, OEE is no longer a static statistic, but instead it becomes a precise, real-time measure of the plant's performance—a measure that everyone in the plant can keep an eye on. And when this value is less than it should be, the plant's automatic data gathering can make it easy to see where the problem's root cause can be found.

What follows are some examples of information-related improvements.

Availability: Information Coming And Going

If a manufacturer's availability is low, then by definition, breakdown and setup losses are the problems that need to be addressed. Information coming from the machine can address the first sort of problem, while information sent to the machine addresses the second.

In the event of a breakdown, the first important piece of information is the existence of the problem. If this information has to travel by word of mouth, then response time will be slow. A better solution is to enable the CNC network to send a pager alarm that alerts a member of the maintenance staff at once. The technician can then use the same network to ensure that the response to that problem is efficient. Through remote access to the machine's operation and maintenance screens, the technician can diagnose the problem at a distance, and arrive at the problem with potential solutions in hand.

To reduce setup losses, a high speed network connection can ensure that part programs and tool, work and fixture offsets all arrive at the machine as they are needed. The operator doesn't have to enter any of this information by hand, and may not even have to call it up by hand, either. A part program delivered in this way is the most up-to-date version in the plant. Tool data delivered in this way can include the history of a particular tool's cutting life so far, even if the tool has moved from machine to machine throughout the shop.

Performance: Seeing Where The Cycle Is Slow

Improving performance is where the data generated at the CNC become the most useful. From moment to moment, every machine has a particular status that the CNC is able to report. The machine is cutting, the machine is idle, the machine is cutting at a reduced rate because of feedrate override and so forth. By sampling the data continuously, and by analyzing the data within software that is designed to provide a graphical, big-picture view of the information, a plant's engineers and managers can immediately see problem areas that otherwise might never have been apparent.

For example, perhaps one machine tool routinely runs the same part number more slowly than other machines just like it. Why is that? Or the operator may routinely have to resort to feedrate override during a particular portion of a feedrate override during a particular portion of a given program. What is wrong with the program in that area? Or, the time between pieces for a given part number may seem to vary widely. What is it about loading that particular part that is causing different operators to encounter different levels of difficulty?

Graphic displays of the day-by-day performance data not only reveal problems such as these, but they also reveal the relative magnitudes of all of the competing trouble areas. Engineering personnel can see exactly where they should focus their attention, by seeing where the greatest amounts of potential production capacity are going underused.

Quality: What Went Wrong And Why

That performance data collected from the CNCs can also be coupled to quality data, revealing an even bigger view of how well the plant is doing.

The most basic quality information is the number of good parts as a percentage of the total parts produced. Because this percentage is the definition of the quality score in OEE, tracking just this much information makes it possible to keep the OEE value up to date.

However, if a little more information can also be collected—such as which machines produced which parts, along with a description of every rejected part using basic numerical codes for common errors—then this information can start to be applied toward increasing the quality yield. The information gets incorporated into the same graphical view as the performance data, allowing the most common and serious quality problems to be visually identified.

Many plants already do something like this. Quality is one area where manufacturers tend to be accustomed to tracking and analyzing data—as in statistical process control. And SPC can continue to be one of the ways that quality data are used. OEE and automated information management simply take the same appreciation for the value of information that already exists in the quality area, and they extend this appreciation to the data being generated throughout production.

How Do I Do This?

To take control of shopfloor information, a manufacturer does not need to have the most modern CNCs. Nor does the manufacturer need to have CNCs that all come from the same company. Technology from a solutions-provider such as GE Fanuc can make it possible to upgrade many, many different CNCs (old and new, from various companies) so that they can all share a common network. That same solutions-provider can also supply the software capable of capturing, organizing and graphically presenting the data.

Perhaps the most striking thing about implementing a system such as this is how little the implementation costs compared to the potential return. Instead of purchasing new capacity or new machinery, the plant realizes a potentially larger amount of capacity that formerly was going to waste.

OEE makes the return apparent. Because this number describes how much of the manufacturer's potential capacity is generating profit, an increase in OEE indicates how much additional profit is coming in. Just multiply your sales × your gross margin × the increase in OEE percentage that you were able to achieve. The result is the additional profit.

Time is money. Information automation not only saves time, but also reveals where the time is being wasted. Using the calculation above, a manufacturer can see just how valuable it is to recapture some of this resource.

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