Feature Recognition For Difficult To Machine Complex Parts And Multi-axis Machining Process Research

The rapid development of the aerospace manufacturing industry places higher demands on the digitization,intelligence and parts manufacturing precision,which brings greater challenges and opportunities to the research on difficult and complex parts in CNC machining technology.Such as large cabin components,as an important part of large aircraft,rockets and other aerospace vehicle models.From blanks to cabin products,CNC machining technology runs through the entire manufacturing process.For feature recognition of difficult-to-machine complex parts,a hierarchical recognition algorithm for intersection features of large complex component blank models based on feature matrix is proposed.It realizes the multi-level complex intersection feature recognition based on the geometric topological data information.The threshold segmentation method is used to identify and eliminate the large complex components pseudo-features and data in the blank model,which optimizes it finally.Constructing the attribute adjacency graph of the blank model,and using the layered recognition method to perform layered processing on the intersecting features of the component.Converting multiple types of single features into feature matrix form.Establishing an open feature matching library for accurate recognition of model intersection features.For the modeling of cutting force in the five-axis machining process during machining,a milling cutting force prediction model considering tool deformation feedback is proposed.In dynamic machining,considering the tool posture,tool deformation and other influencing factors,the milling cutter arc cutting micro-element instantaneous undeformed chip thickness and cutting state edge are determined.In the calculation of tool deformation,an iterative calculation method is used to estimate the amount of flexible deformation.Building a mutual feedback framework of tool deformation and cutting force to achieve more accurate cutting force prediction.The model was used to study the internal relationship between tool deformation feedback and cutting force.For surface roughness five-axis machining process modeling,this paper puts forward the experimental data is obtained by orthogonal test design for the simulation calculation,after multi-axis nc machining of the workpiece surface roughness has carried on the precise measurement,and the cutting tool cutting position as one of the influence factors of surface roughness,adopt the method of multiple linear regression analysis of surface roughness prediction model is established.For the typical difficult to machine materials,there will be a large cutting resistance,which will lead to a large tool deviation and vibration,and affect the surface quality of the workpiece.Therefore,the control variable experiment of rake angle and roll angle is designed to verify that different tool attitude position deviation will affect the surface quality.