EXHIBITIONS
but only at particular times, after defined events – e.g. a threshold limit’s being violated – or for certain processes. Other approaches compress the data volume by forming process parameters. Still other approaches, in their turn, deliberately utilise the large quantities of data in order to identify patterns using appropriate mathematical algorithms. It depends very closely on the application involved which approach is the likeliest to be successful.
Can this quantity of data still be handled with the customary hardware or is a supercomputer needed – a quantum computer? Michael Königs: Present-day technology (if used correctly) will mostly suffice in our disciplines if individual or partial models are developed by experts. The broad, interdisciplinary interconnection and use of these models, however, swiftly brings the currently available hardware up against its limits. A cloud environment geared to these needs, in which both statistically and physically motivated individual models can be interlinked and executed with responsively fit-or- purpose computing resources, can provide the requisite connectivity and computing power here. But I don’t believe that an environment of this kind can be achieved only by using quantum computers.
Academics at the Fraunhofer Institute for Production Systems and Design Technology (IPK) have expressed the view it would be more sensible to make an intelligent preselection in the vicinity of the machine prior to storage, before then a downsized data record (“smart data”) is transferred to the cloud. What do you think of this approach? Alexander Epple: This is an idea I can basically go along with. At the WZL, too, we’re examining local data processing and interpretation, and thus refining the data to create smart data in the immediate vicinity of the machinery system concerned. The advantages of this local data pre-processing are obvious. However, there are also
firms who store and process all raw data unfiltered in a central system – like a cloud.
What is your conception of a cloud, and how can it be utilised? Alexander Epple: By “cloud”, we mean a model for locationally independent, on-demand network access to a shared pool of configurable IT resources that can be demand- responsively utilised and enabled again. These subsume not only network bandwidths and computer hardware, but also services and applications. In the context of big data, cloud platforms, precisely by virtue of this scalability, and the broad availability of analytical algorithms, offer good preconditions for downstream analysis of data quantities that are too large, too complex, too weakly structured or heterogeneous to be evaluated manually or using classical methods of data processing. Upstream data transmission may prove to be technically challenging, however. A local data acquisition system at the machine is comparatively simple to design if the data only have to be forwarded. This, of course, has substantial advantages in terms of maintenance and roll-out, but conversely poses tough challenges for the bandwidth required to transmit the data. Local data pre-processing and compression can reduce this. However, every data compression operation entails a loss of information that may be irrelevant for ongoing analyses but be totally crucial for future scenarios. Sometimes, you only realise afterwards that the information no longer available would actually have been helpful in order to interpret a particular phenomenon. Both these approaches have their
own advantages, and it will depend on the strategy of the application partner concerned as to which approach he decides to pursue. Quite generally, we are observing a certain amount of scepticism when it comes to storing data centrally in a cloud system. But there are also options for a local “company cloud”. Even data evaluation directly at just one
local machinery system can already offer major potentials for raising productivity.
Big Data raises productivity by 30 to 150 per cent How did you come to be collaborating with the experts at SAP, who together with Cisco and Huawei have developed a big data client that acquires and stores all data in the cycle of the CNC? Alexander Epple: The close collaboration was initiated by SAP, since they were looking for a research partner from the field of production technology, able to provide not only excellent basic R&D but also a lengthy track record in application-focused collaboration with industrial enterprises. We have supported SAP with customer-related projects in highly disparate fields. The results were a surprise even to us. For instance, at a German automaker, with SAP we have increased productivity by 30 per cent in the power train section, and substantially reduced the rejection rate. In the aerospace industry, we’ve likewise succeeded in raising productivity by almost 30 per cent, and at one German manufacturer of large machines by nearly 150 per cent.
What are the typical questions you’re confronted with? Alexander Epple: Machine operators, process developers or quality engineers are often worried that their expert knowledge will not be needed any more in the long term. However, we believe that all essential decisions will still have to be taken by experts. They are familiar with numerous boundary conditions that may not even be amenable to being imaged by means of data. Data evaluation has to support the operator in his/ her decision-making with appropriate visualisation of machinery or process states. Thanks to the new solutions, however, operators will in particular be spared the elaborate search and pre-processing work involved for individual pieces of process information, with its minimal contribution towards added value.
The relevant workload in the metalworking sector is high: could
www.internationalmetaltube.com
virtual prototyping or try-out be sufficiently improved with the aid of big data so that the number of real trials could be reduced or even eliminated entirely? Alexander Epple: Substantial reductions are indubitably possible, yes. In our view, learning from data with the support of models has huge potential.
What do you and your team expect from the EMO Hannover, the world’s premier trade fair for the metalworking sector. What role will big data play there? Alexander Epple: Due to our collaboration with numerous industrial partners, who will also be represented at the EMO Hannover, we already have a pretty definite idea: I believe it’s becoming progressively clearer that the use of big data in production operations can be substantially enhanced by incorporating specialist expertise. This eliminates the worries expressed by many specialist employees that their expert knowledge will soon be rendered superfluous by big data. I am hoping for more acceptance from the visitors and a certain amount of curiosity from a sector that otherwise tends to be rather conservative. So I’m looking forward to plenty of specific solutional approaches. The interview was conducted by Nikolaus Fecht, specialist journalist from Gelsenkirchen
Contact
Laboratory for Machine Tools and Production Engineering (WZL) Viktoria Haarmann Press and Public Relations Steinbachstraße 19 52074 Aachen, Germany Tel. +49 241 80-27554
v.haarmann@wzl.rwth-aachen.de www.wzl.rwth-aachen.de
IMT June 2017 31
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36
