Research On Data-driven High-speed Dry Hobbing Process Parameter Optimization Decision System

Under the strategic background of sustainable development and green manufacturing,high-speed dry cutting gear hobbing,as a green and environmentally friendly and efficient gear processing technology,has occupied an important position in the gear manufacturing industry.This process has no cutting fluid and high cutting speed during the cutting process.Inappropriate process parameters will seriously affect the energy consumption,quality and tool life of the tool.Optimizing the process parameters to achieve the comprehensive optimization of the high-speed dry hobbing process is of great significance to the transformation and upgrading of the gear manufacturing industry and the realization of sustainable development.To this end,this paper studies the datadriven high-speed dry hobbing process parameter optimization decision-making system.Firstly,for high-speed dry hobbing process parameter optimization and decisionmaking,on the basis of the influencing factors and process data integrated management analysis in the process of the process,the data-driven high-speed dry hobbing process parameter optimization decision-making system requirements were completed.Analyze,put forward an overall system plan including system equipment layer,data layer,optimization decision-making layer,human-computer interaction layer,system support layer,and design the system function structure.Then,the key technology of the data-driven high-speed dry hobbing process parameter optimization decision-making system is studied.Aiming at the problem of multi-source heterogeneous gear hobbing process data integration management,a component-based gear hobbing process data integration method is proposed.This method firstly performs standardized expression and unified management of hobbing process knowledge based on the ontology,and then designs different components,to complete the data integration of various data sources;for the data-driven similar process case retrieval problem,a composite retrieval method based on knowledge-driven and improved fuzzy C-means algorithm is proposed.This method first completes the rough retrieval based on knowledge,and then Based on the improved fuzzy C-means algorithm to complete the in-depth retrieval;for the data-driven process parameter optimization decision-making problem,a gear hobbing process parameter optimization decisionmaking method based on NSGA-III and AHP-TOPSIS is proposed.This method is searched in similar process examples On the basis of,with the minimum energy consumption,minimum quality error,and maximum tool life as the optimization goals,the NSGA-III algorithm based on the reference point is used to find the best,and the AHP-TOPSIS combination method is used to sort the advantages and disadvantages of the parameter solution set to achieve data Driven high-speed dry hobbing process parameters multi-objective optimization decision,which improves the efficiency and rationality of parameter decision-making.Finally,on the basis of the above research,a data-driven high-speed dry hobbing process parameter optimization decision-making system was developed.It can realize the functions of production task management,equipment information management,workpiece information database management,tool information database management,similar process instance retrieval management,process parameter optimization decision management,system management and so on.The multi-objective optimization decision of high-speed dry hobbing process parameters is realized,which has important practical value.