By Markus Schmolz, Schwäbische Werkzeugmaschinen
Nowadays, machine tools can connect to a comprehensive data acquisition regimen via the Internet that goes beyond just status-oriented maintenance information. Maintenance, production and management groups can use the acquired data. This means, for example, a manufacturer can make accurate statements about the total cost of ownership (TCO) of the machines it uses.
Online or Offline?
There's been considerable discussion for several years about the appropriateness of online services in the machine tool environment. Some companies already use these technologies to bring transparency not only to the associated states of their machines but also to the production processes.
Other companies still ask, "Why should we investigate the value of machine-status monitoring? Does it make sense to spend money for such services? Isn't it dangerous for a company to connect operating machines to the Internet? Can the machine status data be used to make decisions for production processes, beyond the obvious maintenance requirements?"
Schwäbische Werkzeugmaschinen (SW), a major German machine tool manufacturer in Schramberg-Waldmössingen in the German Black Forest, has a positive view of the benefits these initiatives provide. For many years, the company has provided comprehensive monitoring of its machines' data. "What is new is that this global data acquisition allows us not only to determine the ideal intervals for use-dependent maintenance, but also to provide customer-specific, matched solutions with very useful background information for the production," says Peter Siegel, responsible for the development of online services at SW. "This gives the management an operational transparency that it doesn't have otherwise."
Productivity and High Accuracy
Schwäbische was founded in 1994 and since 2004 has been the competence center within the EMAG group (www.emag.com) for the machining of prismatic workpieces. The company, with about 330 employees, produces about 160 machine tools annually, including high-performance one- to four-spindle machining centers and special machines (Figure 1). About 70-80% of SW's customers come from the automotive industry, with other application areas including medical technology and the hydraulics industry. SW sells its machine tools worldwide, with Europe as its main focus.
A special feature of SW machines is multi-spindle capability coupled with very high precision, in particular the multi-spindle five-axis machining centers. Customers who do not want to produce anything other than "parts, parts and more parts" can expect high productivity combined with high accuracy. For example, the machines normally have 12-19 numerical control (NC) axes and a work space of about 600x600x600 mm for a single-spindle machine, and 600x600x1200 mm for multi-spindle machines.
A Comprehensive View
More than 65% of the SW machines sold today connect to the Internet. "However, by no means do all customers use the resulting information fully," Siegel says. "Many still envision reactive maintenance service as the only possible use, and only when a fault has occurred."
Nowadays, however, it is possible to use the data to react in advance and not just to the operational status of the machines. The current state-of-the-art allows the machine's operational data to be recorded continuously (Figure 2). As a consequence, the user can draw conclusions about performance.
This capability requires that all relevant data be gathered. Consequently, several years ago, SW equipped its spindles with sensors that could check spindle status without removing them from the machining head.
Meanwhile, the global data recording allows useful conclusions to be obtained for maintenance, production and management. "Our services provide the maintenance department with a powerful tool that makes its work simpler and faster," Siegel says. "The maintenance staff no longer needs to wait for our alerts and updates. They can work with the data themselves."
For production analysis, the same data source can be used to determine, for example, how often the "JOG" operation has been selected, how long the setup took, if and for how long the machine stopped without the presence of a malfunction, or how long the response time was to a malfunction.
"All this data makes it possible to determine the causes of qualitative problems, why processes are instable or why the number of produced parts falls suddenly," Siegel says. "Ultimately, a company can achieve process optimization and/or productivity increases."