Positioning error compensation of an industrial robot using neural networks and experimental study

Due to the characteristics of high efficiency,wide working range,and high flexibility,industrial robots are being increasingly used in the industries of automotive,machining,electrical and electronic,rubber and plastics,aerospace,food,etc.Whereas the low positioning accuracy,resulted from the serial configuration of industrial robots,has limited their further developments and applications in the field of high requirements for machining accuracy,e.g.,aircraft assembly.In this paper,a neural-network-based approach is proposed to improve the robots’positioning accuracy.Firstly,the neural network,optimized by a genetic particle swarm algorithm,is constructed to model and predict the positioning errors of an industrial robot.Next,the predicted errors are utilized to realize the compensation of the target points at the robot’s workspace.Finally,a series of experiments of the KUKA KR 500–3 industrial robot with no-load and drilling scenarios are implemented to validate the proposed method.The experimental results show that the positioning errors of the robot are reduced from 1.529 mm to 0.344 mm and from 1.879 mm to 0.227 mm for the no-load and drilling conditions,respectively,which means that the position accuracy of the robot is increased by 77.6%and 87.9%for the two experimental conditions,respectively.

Effect of non-spherical particles on burning behavior during aluminum combustion

The shape of an aluminum particle is assumed to be spherical or an equivalent sphere during the combustion process.Such an assumption lacks objectivity and leads to unreasonable approximations of burning efficiency and performance.To investigate the influence of non-spherical particles on burning behavior,this study focused on a theoretical and experimental investigation of the combustion of nanoscale aluminum ellipsoidal particles.Models for prolate and oblate spheroids in aluminum combustion were established to explore combustion properties such as mass release rate,linear burning rate,burning rate,and burnout time.To validate the theoretical results,combustion experiments were conducted on three samples.Reasonable agreement between the results of numerical simulation and experimental findings was obtained in terms of the particle burning characteristics.It was found that particle morphology(such as prolate or oblate spheroid shape)and size play a significant role in the combustion performance of nanosized aluminum particles.

Perfect digital holographic imaging with high resolutionusing a submillimeter-dimension CCD sensor

In order to improve the resolution of digital holography with a common-dimension charge-coupled device (CCD) sensor, the point spread functions are briefly derived for the commonly used and practical post-magnification, pre-magnification, and image-plane digital holographic microscopic systems. The ultimate resolutions of these systems are analyzed and compared. The results show that the ultimate lateral resolution of pre-magnification digital holography is superior to that of post-magnification digital holography in the same conditions. We also demonstrate that the ultimate lateral resolution of image-plane digital holography has no correlation with the photosensitive dimension of the CCD sensor, and it is not significantly related to the pixel size of the sensor. Moreover, both the ultimate resolution and the imaging quality of image-plane digital holography are superior to that of pre- and post-magnification digital holographic microscopy. High-resolution imaging, whose resolution is close to the ultimate resolution of the microscope objective, can be achieved by image-plane digital holography even with a submillimeter-dimension sensor. This system, by which perfect imaging can be achieved, is optimal for commonly used digital holographic microscopy. Experimental results demonstrate the correctness of the theoretical analysis.