Burning Brightly

As many shorthanded shops have already discovered, the labor pool is getting shallower all the time. Across the country, positions are going unfilled because the once-steady stream of new machinists coming out of vocational schools and apprenticeship programs is drying up.

Faced with a shortage of skilled hands to operate their machines, shops are turning to technology and automation to fill the gaps in their ranks. In the area of die-sinker EDM, suppliers have responded with machine tools and controls that run unattended once they are set up. Not only can these systems follow the programming of their human operators, they also can offer programming suggestions and modify their programming in-process to respond to changing conditions. In many ways, these systems are performing the tasks human operators performed in years past. And as a bonus, the automated systems can perform around-the-clock without fatigue or boredom compromising their performance.

Setting Up the Burn
The most sophisticated die-sinker EDMs need human input only during an operation’s preliminary stages. Programming systems with conversational interfaces can make it relatively simple for the operator to communicate with the machine’s controls. By answering a series of questions, the operator can supply the system with such data as the workpiece’s surface area, the required finish, the flush conditions, the contact area between the electrode and the workpiece, and the shrinkage of the operation as well as other necessary information.

If the machine is equipped with workhandling, c-axis, and toolchanging capabilities, the system can be programmed for multiple workpieces, multiaxis burns, or multiple electrodes using this same simple interface. When one job, burn, or electrode completes its cycle, a change of workpiece, axis, or electrode can be programmed into the system to prepare the EDM for the next cycle.

The programming system can use the raw data supplied by the operator to calculate the initial parameters for the job. Machines equipped with artificial intelligence (AI) can actually apply their own judgment to determine the optimal starting parameters. An AI system is programmed with a set of rules that will guide it in setting the machine correctly for a given combination of materials and conditions. For example, the system might determine from measured spark-cycle amplitudes that there is a problem with gap stability. The system will then turn to AI technology to make the adjustments that will increase the burn’s efficiency.

EDM manufacturers create AI systems using data and rules they have developed through research. This research information is supplemented with input the manufacturers have solicited from skilled operators, so that the system might choose the same initial settings a shop’s best EDM person might select. Such a system can be updated to reflect changes in technology or user priorities.

Once it has been programmed, a fully automated die-sinker EDM can handle the rest of the operation on its own. After a new part is loaded, it locates the actual position of the workpiece and the electrode using a probe fitted into its toolholder. These positions are recorded in its memory for use by the automatic programming system.

After the system notes the position of the workpiece, the automatic toolchanger replaces the probe with the electrode needed for the burn. The programming system knows how many electrodes it will need to complete the job. AI is used to determine the correct depth the process should reach before changing electrodes.

The Brains of the Operation
In the past, a human operator was needed not only to set up a die-sinker EDM, but also to watch over the process and make any adjustments that were necessary to ensure a good burn. For an EDM to operate without such supervision, it must be able to monitor its own process and make the appropriate adjustments itself. Today’s fully automated EDMs do this with the aid of fuzzy-logic controls (Figure 1).

Figure 1: A fuzzy-logic system draws conclusions about the stability of the die-sinker EDM process from data collected by sensors. The system then uses these conclusions to determine the best course of action.

Fuzzy logic uses nonlinear interpretation of incoming data to mimic human thought processes. The sources of these data are the programming system that developed the initial parameters and signals coming from the burn itself. A fuzzy-logic system uses a three-step process to control the burn:

1. The system receives and analyzes a variety of electrical and mechanical signals that have been collected by the EDM’s sensors. These sensors are built into the machine, and they collect and transmit the data automatically.
2. By applying the rules and membership functions with which it has been programmed to this incoming data, the fuzzy-logic system is able to infer the degree of gap stability. A membership function is a measurement of the gap’s degree of stability. In a very stable gap, most of the spark’s energy is being used to erode the workpiece. The gap becomes less stable when debris from the erosion process contaminates the gap, channeling an increasing amount of the spark’s energy away from the workpiece.
3. The system chooses a parameter change from a decision table, which lists and prioritizes the actions that can be taken. These actions include changes in amperage, jump speed, jump distance, off-time, or power-supply changes in accordance with internal parameters. The system is programmed to select the change that will most likely maintain an optimal discharge frequency under the conditions it has inferred from incoming data.

EDM manufacturers have tried to program their fuzzy-logic systems to think like an experienced operator. The rules that are used to infer process conditions were developed through extensive factory research. Hours of test burns were performed to determine the membership functions to which the rules are applied. The manufacturers also observed skilled operators to learn how they reacted to different burn conditions, and then programmed the fuzzy-logic systems to respond in a similar manner.

Automated Productivity
With EDMing expertise programmed into their machines, shops have been able to increase production despite a shortage of manpower. Jim Sutter, EDM manager for Die Makers, Monroe City, MO, says that having an EDM that can run unattended frees up the operator for other tasks, such as part programming and machining electrodes.

“We can have one worker operating two machines,” Sutter says. “While one machine runs unattended, he programs the other.”

Die Makers uses an EDM to cut dies for the automotive and computer industries. Sutter says Die Makers bought its first CNC die-sinker EDM for these tasks in 1985. The machine gave the shop a limited capacity for unattended machining, but it wasn’t too long before Die Makers had to purchase an EDM with more power and control to run new orbit patterns.

In the years since, Die Makers has purchased even more automated EDMs to keep up with customer demand. Sutter claims that the company’s ability to EDM parts unattended has increased its capacity and allowed the shop to win more work from customers such as the Big Three automakers.

Seicor, Keller, TX, also found automation necessary to meet its customers’ expectations. The company, a wholly owned joint venture of Seimens and Corning, manufactures fiber-optic cable molding. Its customers include telecommunications companies such as AT&T, Southwestern Bell, and Northern Telecom.

These customers demand just-in-time delivery, with a fast turnaround between the time parts are ordered and the time they are received. To fill orders this quickly, Seicor has found it necessary to keep its EDMs running even after its operators go home for the day. The unattended shift has allowed the company to reduce its EDM backlog from 1000 hours to about 200 hours.

“Our largest gain in operation has been in faster machine time and overnight unattended machining,” says Seicor’s EDM specialist, Dan Knutson. Knutson says the faster burn times and shorter cycle times are due to the EDM’s fuzzy-logic system. He also believes that an automated toolchanger is a necessity for unattended EDMing. “A CNC without a toolchanger is just ridiculous,” Knutson says. “Why have a machine that operates itself if you still have to change the tools after each burn cycle?”

EDM automation will never completely replace the need for skilled personnel in the machine shop, but it can replace some of the expertise lost through attrition. With machines that can think for themselves, shorthanded shops now can assign one worker to run up to six EDMs, eliminating the need for an operator at every station.

About the Author
David Doyle is a senior applications specialist at Mitsubishi EDM, MC Machinery Systems Inc., Wood Dale, IL.