Robowler:Design and development of a cricket bowling machine ensuring ball seam position

The art of cricket bowling is complex and arduous owing to the run-up and ball release time energy requirement to achieve speed and variations. Therefore, human bowlers cannot bowl for extended periods and numerous mechanical bowling machines have been built to help batsmen improve their skills during practice sessions. However, most of these existing machines are designed for spherical balls ignoring the distinguishing physical feature of a cricket ball: the raised equatorial seam, which makes it less of a sphere. The bowlers are known to often benefit from this seam in their pursuit to taking the batsmen's wicket by imparting swing, spin and bounce variations along-with other bowling variables. This lack of the seam consideration creates a void between human and mechanical bowling. In this work, we present design and development of an automatic bowling machine termed as ROBOWLER to make mechanical bowling more realistic. This machine ensures ball seam position as well as fulfills other constraints. Ball pitching and seam position accuracy results underscore the suitability of this design to enhance the capabilities of mechanical bowling.

ZERO TRANSMISSION AND ITS APPLICATION IN HIGH SPEED CNC MACHINE TOOLS

In order to realize high speed machining,the special requirements for the transmission and sturctrue of CNC machine tool have to be satisfied.A high speed spindle unit driven by a built-in motor is developed.An oil-water heat exchange system is used for cooling the spindle motor.The spindle is supported by Si_4N_3 ceramic ball angular contact bearings. An oil-air lubricator is used to lubricate and cool the spindle bearings.Some special structures are taken for balancing the spindle.

Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills

Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.