Research On Process Parameters Optimization Decision Support System Of CNC Gear Hobbing For Low Carbon Manufacturing

Low carbon manufacturing model is of growing attention due to global warming and increasing shortage of resources and energy.Because of manufacturing industry with large energy consumption,low carbon manufacturing is an important direction to achieve the world 's energy saving and emission reduction.With the rapid development of the gear industry,CNC hobbing machine also increases.Therefore,research on low carbon operation support technology of CNC gear hobbing machine has important guiding significance for manufacturing industry to implement low carbon manufacturing.Hobbing process parameters optimization decision is of great significance because of its directly affect to processing quality,efficiency,cost,energy consumption and carbon emission.As a result,this paper is constructed to study the process parameters low carbon optimization decision support system of CNC gear hobbing,and thus provides theoretical guidance and method support for low carbon operation of CNC gear hobbing machine.First of all,the decision characteristics and carbon source of CNC gear hobbing machine are analyzed.Aiming at the requirement of low carbon gear hobbing and efficient process parameters decision,a method framework of low Carbon Optimization decision for CNC gear hobbing process parameters is proposed,which based on case-based reasoning and optimization algorithm.Based on the framework of the method,the architecture and function structure of the support system are designed in detail.Secondly,the required key technologies for the support system were researched.Aiming at the retrieval stage of case reasoning,a similar case retrieval method based on expression-based driving and improved K-means algorithm was proposed.This method constructed the decision knowledge template based on process manual and decision making experience,then the expression-based driving technique was used to carry out one-stage retrieval for process cases and the improved k-means algorithm was used to carry out two-stage retrieval,thus a similar process sample extraction set was obtained.Aiming at the optimization decision stage of case reasoning,a low carbon optimization decision method for CNC gear hobbing machine based on back propagation(BP)neural network and flower pollination algorithm(FPA)was proposed.A BP neural networks model was established based on the cases,which can predict the machining effect evaluation of hobbing processing.Serveal process solutions from similar process sample extraction set were obtained to construct process parameter constraints.At last,the low-carbon optimization model of the process parameters was solved by the FPA to obtain the optimal process parameters of low carbon processing.Finally,based on the above studies,a set of CNC hobbing process parameters optimization decision support system for low carbon manufacturing was designed and developed.The system can reduce the system carbon emissions,improves the efficiency of process parameters decision-making,and provides important basis and support tools for the CNC gear hobbing machine processing system of green low-carbon operation.