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The new learning drill

Fraunhofer-Gesellschaft Zur Forderung Der Angewandten Forschung E.V. : 28 August, 2002  (New Product)
Today's powerful drills penetrate most concrete walls like a knife through butter. But not all concrete is the same: Depending on the application, different grades are used and they exhibit great differences, also in strength. A garden path made of exposed aggregate concrete is not in the same league as a strongroom which is protected by means of steel-reinforced heavy concrete.
To improve drilling performance, it would be a good idea if the hammer drill 'understood' about the different types of concrete and rock and then adjusted the two variables on which drilling performance depends: the rotational speed of the bit (hopefully sharp) and the frequency of the percussive mechanism. In order to optimally coordinate these variables and achieve maximum drill penetration, engineers at the Fraunhofer Institute for Information and Data Processing IITB have developed an intelligent drive control system which automatically adjusts to different bits, building materials, rock types and contact pressures applied by the user. An industrial enterprise which is cooperating with the Institute is testing this patented concept with prototypes of its high-performance hammer drills in practical trials.

'Man, drill and wall form a complete mechanical system,' explains Dr. Helge-Björn Kuntze. 'Users press and move the drill in different ways and the strength of the wall varies from case to case. As the dynamic behavior responds relatively sensitively to changes in these parameters, we have equipped the electronic drive control with a learning neuro-fuzzy component.' Several sensors in the drill measure impact and rotational speed, electric power and longitudinal and lateral acceleration as input variables. The electronic system compares these data with stored standard situations and almost instantaneously recognizes current situation parameters such as bit type, rock type and contact pressure. If the system fails to find an exactly matching situation, it applies the parameters that come closest to the present operating conditions.

In a second step the control system has to find and set the optimal working point. 'This match is based on real drilling tests performed on standard blocks of concrete and rock,' explains the head of the IITB's measurement, control and diagnosis systems unit. 'The best working points for every situation are stored in operating maps implanted in the machine's brain.' The aim now is to transfer the concept of the learning drill to larger-scale applications. As a result, tunneling work, for example, could soon make faster progress.
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