Process planning and contour-based error compensation for precision grinding of miniature scalpels

Miniature scalpels are mainly used in microsurgeries such as ophthalmic and cardiovascular surgeries.The size of a miniature scalpel is only a few millimeters,and the precision of the blade shape is high,which makes production of miniature scalpels extremely difficult.This study proposes a new sharpening process for grinding miniature scalpels on a four-axis machine tool.A post-processing algorithm for a four-axis grinding machine based on a kinematics model is established.We then propose a corresponding parameter calibration method for the parameters used in the kinematics model.Because of possible errors in the parameter calibration,a contour-based error compensation method is proposed for accurate adjustments to the edge shape following grinding.This can solve the problem of large deviations between the actual edge shape after grinding and the ideal edge shape.The effectiveness of the proposed process planning and error compensation method is verified experimentally,and the grinding process parameters of the miniature scalpel are optimized to improve its surface processing quality.The sharpness of the optimized miniature scalpel is less than 0.75 N,and the blade shape is symmetrical,which meets the technical requirements of miniature scalpels.

A hybrid biogeography-based optimization method for the inverse kinematics problem of an 8-DOF redundant humanoid manipulator

The redundant humanoid manipulator has characteristics of multiple degrees of freedom and complex joint structure, and it is not easy to obtain its inverse kinematics solution. The inverse kinematics problem of a humanoid manipulator can be formulated as an equivalent minimization problem, and thus it can be solved using some numerical optimization methods. Biogeography-based optimization （BBO） is a new biogeography inspired optimization algorithm, and it can be adopted to solve the inverse kinematics problem of a humanoid manipulator. The standard BBO algorithm that uses traditional migration and mutation operators suffers from slow convergence and prematurity. A hybrid biogeography-based optimization （HBBO） algorithm, which is based on BBO and differential evolution （DE）, is presented. In this hybrid algorithm, new habitats in the ecosystem are produced through a hybrid migration operator, that is, the BBO migration strategy and Did/best/I/bin differential strategy, to alleviate slow convergence at the later evolution stage of the algorithm. In addition, a Gaussian mutation operator is adopted to enhance the exploration ability and improve the diversity of the population. Based on these, an 8-DOF （degree of freedom） redundant humanoid manipulator is employed as an example. The end-effector error （position and orientation） and the ＇away limitation level＇ value of the 8-DOF humanoid manipulator constitute the fitness function of HBBO. The proposed HBBO algorithm has been used to solve the inverse kinematics problem of the 8-DOF redundant humanoid manipulator. Numerical simulation results demonstrate the effectiveness of this method.