Research On Optimal Allocation Of Machining Allowance For Complex Blanks Based On Objects Measurement

With the development of the manufacturing industry, the conventional positioning method ofcrossed check, CMM measuring and artificial adjustment, which is usually adopted by complexblanks during the follow-up machining operation, has been gradually exposed some disadvantagessuch as low processing efficiency and small deviations may result in a shortage of maching allowanceor tolerance. In order to solve the positioning problem of complex blanks with small machiningallowance, an optimal allocation technology of comprehensive use of3D measurement, point cloudsegmentation and surface matching, which is based on objects measurement for complex blanks, isrealized in this theses. More specifically, firstly the whole complex blanks are measured in full fieldby3D measurement technology. Secondly, the measured data are aligned to the nominal CAD modelsby the initial matching and unconstrained matching. Then with the aid of an elaborately builtsegmentation guide and the application of local differential information, the measured data are dividedinto segmentations with process structure features. Finally, the alignment between the segmentationdata and the nominal CAD models with considering complex constraints is performed. Based on thealignment, an optimal machining localization datum on the blanks can be determined, achievingoptimal allocation of machining allowance.The key techniques of the method have been deeply studied in this thesis, including3D digitalmeasurement, point cloud automatic segmentation and surface constrained macthing, etc. On thebases of the above key algorithms and techniques, an allowance analysis system embedded CATIAplatform is realized. The main research contents and innovative points are summarized as follow:⑴By obtaining the whole complex blanks’ shape data, a comprehensive measurement schemeincluding photographic measurement, structured light measurement and contact measurement isdesigned. And then the measured data are aligned to the nominal CAD models by the pre-registrationof the initial matching and unconstrained matching.⑵Based on an elaborately built segmentation guide, an algorithm of point cloud automaticsegmentation is proposed to realize that all of machining surfaces and tolerance surfaces are separatedfrom the point cloud and that each of point cloud segmentation can be correctly corresponding to thespecial surface of CAD models.⑶In order to speed up the efficiency of the constrained registration algorithm, the methods ofaligning the measured data of blank parts to the nominal CAD models under complex allowance constraints have be deeply analysed, so a fast calculation method for the closest point of the measureddata of blank parts to the nominal CAD models and a hierarchical and fast iterative method ofconstrained registration are proposed. With the aid of the alignment, an optimal machininglocalization datum on the blanks can be determined.⑷An allowance analysis system, embedded CATIA V5R18platform, is independentlydesigned and realized based on the full-text algorithms and techniques. The performance of thesystem is verified through some specific examples.The research achievements in this thesis have successfully overcome the shortcomings whichexist in the traditional positioning method and can effectively meet the high efficiency and goodprecision requirements of workpiece localization for complex blanks with small machining allowance.