Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Journal of Machine Engineering

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Controlled turning process for the production of friction-reduced cylinder liners with a defined free-form geometry

Denkena Berend, Bergmann Benjamin, Handrup Miriam, Schmidt Christopher

Journal of Machine Engineering

2021

Vol. 21, No. 3

47--59

Friction in the piston-cylinder system of combustion engines has a great influence on fuel consumption. To reduce the friction of combustion engines, free-form cylinder liners and microstructured cylinder liners have proven to be advantageous. However, the combination of both processes is not industrially realized today because of an increased manufacturing effort due to a higher number of process steps. To save resources in form of honing oil in the production, the free-form can be machined by a dry turning process instead of form honing. A combination with the microstructuring process in a single manufacturing step would furthermore reduce non-productive time. This paper presents a piezo-actuated hybrid tool that can carry out both processes. The tool wear and the behavior during free-form fine machining of cylinder liners are investigated. A process control system is introduced that controls the cylinder liner geometry by adapting the process parameters during free-form turning.

Material identification during turning by neural network

Denkena Berend, Bergmann Benjamin, Handrup Miriam, Witt Matthias

Journal of Machine Engineering

2020

Vol. 20, No. 2

65--76

A design concept for high-performance components involves the combination of different materials in hybrid workpieces. Different material properties and chemical compositions influence the machining quality of hybrid workpieces. To achieve a constant workpiece and process quality, it is necessary to adjust the process parameters to the individual material. Thus, it is mandatory to classify the material during machining for the relevant range of process parameters. This paper examines teaching strategies for neural networks to determine the machined material in process by a small amount of cross points. For this purpose, different training sets are compared. Process parameters with different cutting speeds, feeds and with constant and varying depth of cut are examined. In addition, the signal sources necessary for robust material classification are compared and investigated. The investigation is performed for the cylindrical turning of friction welded EN AW-6082/20MnCr5 shafts. The study shows that an F1 score of 0.99 is achieved at a constant cutting depth, provided that only the corner points of the process window and the machine control signals are used for training. With an additional variation of the cutting depth, the classification rate is significantly improved by the use of external sensors such as the acceleration sensor.