Pre-deformation for Assembly Performance of Machine Centers

The current research of machine center accuracy in workspace mainly focuses on the poor geometric error subjected to thermal and gravity load while in operation, however, there are little researches focusing on the effect of machine center elastic deformations on workspace volume. Therefore, a method called pre-deformation for assembly performance is presented. This method is technically based on the characteristics of machine tool assembly and collaborative computer-aided engineering （CAE） analysis. The research goal is to enhance assembly performance, including straightness, positioning, and angular errors, to realize the precision of the machine tool design. A vertical machine center is taken as an example to illustrate the proposed method. The concept of travel error is defined to obtain the law of the guide surface. The machine center assembly performance is analyzed under cold condition and thermal balance condition to establish the function of pre-deformation. Then, the guide surface in normal direction is processed with the pre-deformation function, and the machine tool assembly performance is measured using a laser interferometer. The measuring results show that the straightness deviation of the Z component in the Y-direction is 158.9% of the allowable value primarily because of the gravity of the spindle head, and the straightness of the X and Y components is minimal. When the machine tool is processed in pre-deformation, the straightness of the Z axis moving component is reduced to 91.2%. This research proposes a pre-deformation machine center assembly method which has sufficient capacity to improving assembly accuracy of machine centers.

Grain size effect on the assembly quality of micro-scaled barrel formed by microforming

In this research,a method employing micro-extrusion was designed to produce the micro-scaled barrel-shaped parts with complex geometrical features to study the feasibility of the proposed microforming method and its grain size effect on the formability of the complicated internal features in terms of deformation behavior,material evolution,accuracy of dimensions and final components quality.The results reveal that the deformation behavior is highly affected by grain size and becomes unpredictable with increased grain size.In addition,assembly parameters including feature dimension,tolerance and coaxiality also vary with grain size,and the variation of grain size needs to be accommodated by different assembly types,viz.,clearance fit or transition fit.From the microstructural evolution aspect,it was identified there were two dead zones and four shear bands,and the formation of these deformation zones was barely affected by the variation in grain size.Though bulges,cracks,and fracture induced voids were observed on the surface of the final components,tailoring the microstructure of the working material with finer grains could significantly avoid these defects.This study advances the understanding of forming microparts by extrusion processes and provides guidance for microforming of similar microparts.

Burr Contour Recognition and Coordinates Sequence Real⁃Time Generation for Robotic Deburring Efficiency Optimization

The phenomenon of burring is common in the manufacturing of metal parts.This phenomenon directly influences the assembly accuracy and service performance of the mechanical parts.In this work,we propose a vision⁃based method for two⁃dimensional planar workpiece.The proposed technique has the ability to recognize burr contour and generate the coordinate sequence in real⁃time along x and y directions.The robotic deburring efficiency is improved based on the quantitative information of the burr size.First,by utilizing the local deformable template matching algorithm,we match the standard workpiece contour with the workpiece contour to be processed and compute the corresponding pixels distance between the two contours.Second,we set the distance thresholds in order to divide the burr contours into different levels.We extract the coordinates of the burr contours and map them to the standard workpiece contour.As a result,the closed⁃loop robotic deburring path sequence is generated.Finally,on the basis of the quantitative information of burr size,we adjust the deburring speed in real⁃time during the deburring process.The experiments performed in this work show that the deburring time of the proposed method is reduced by 15.45%,as compared with the conventional off⁃line programming deburring methods.Therefore,for industrial mass production,the deburring efficiency is greatly improved.