Research On The Method Of Information Extraction And Path Optimization Based On STEP Hole Mold

In the field of CNC machining,the processing of hole group accounts for a large proportion.With the booming machinery manufacturing industry and the popularity of digital technology,the traditional method of manually formulating the processing path is difficult to achieve the actual production,it is an inevitable trend to shorten the tool idle travel,reduce the processing time and the production cost,improve the processing efficiency.Therefore,numerical control programming to optimize the processing of the hole group processing path is a key issue.Based on the extraction of the geometric information of STEP neutral documents,two intelligent optimization algorithms are proposed to optimize the hole group processing path for different type hole group molds.Firstly,the inheritance relationship between the neutral file entities of the hole molds is analyzed,the geometric information extraction scheme of the hole group is designed,and the mathematical model non-uniform rational B-spline curve is analyzed which is describing the geometric information.The feasibility of the geometric information extraction scheme is verified by the specific STEP neutral file engineering example.Secondly,aiming at the problem of ordinary hole group processing,an improved genetic algorithm combining nearest neighbor,genetic algorithm and taboo search is proposed.The nearest neighbor algorithm is used for selecting a series of good initial populations,taboo search is introduced into the genetic algorithm,and some new individuals are randomly introduced in the evolution process to search optimal solution.Based on the drilling characteristics of the hole group,a mathematical model similar to the tsp is established and the drilling problem is solved using improved algorithm.The tire experiment results show that the path length of the improved algorithm is 5.31% shorter than that of the CAM system algorithm,77.88% shorter than the X-path method's,77.63% shorter than the Y-path method's,and 4.52% shorter than the nearest neighbor algorithm.When the parameters are the same,it is 14.65% shorter than the path length of the genetic algorithm,and the average running time of the improved genetic algorithm is 63.60% shorter than genetic algorithm's.So the improved algorithm can save processing time costs and improve the NC machining of hole group effectively.Aiming at the problem of super large-scale hole group drilling,an adaptive hierarchical spectrum clustering and genetic algorithm is proposed.Firstly an adaptive similarity matrix is constructed in the algorithm,and it is applied to the spectral clustering algorithm to realize the preliminary clustering of large-scale hole groups.when hole group subset's size is more than the threshold after clustering,clustering is performed by the above adaptive spectral clustering algorithm until the size of each hole group subset is smaller than the threshold,it is called hierarchical clustering;secondly,the improved genetic algorithm combining the nearest neighbor and taboo search is used to find the shortest loop between classes,solving the GTSP problem;finally,the improved genetic algorithm is used to solve the optimal solution within each class of hole group,and the shortest loop of the GTSP between the classes and the TSP optimal solution within the class are obtained,and the optimal solution of the processing path problem is also obtained,and the applicability and advancement of the algorithm are verified by specific numerical experiments.