Cutting Parameters Optimization For High Efficiency And Low Carbon Under The Condition Of Cutting Stability

The low carbon manufacturing has become a research hotspot with the growing consciousness of environmental protection in various countries.Future manufacturing needs efficiency utilization of various energy and resources to reduce the carbon emission in manufacturing process and enhance the competitiveness of enterprise.Machining is the main process in manufacturing.And turning takes a proportion of machining for 20%-35%and plays an important role in energy conservation and emission reduction of manufacturing.This paper uses low carbon manufacturing as the research background,and analyzes the effect of turning process parameters on the specific cutting energy,processing time and carbon emission through Response Surface Methodology(RSM).The high efficiency and low carbon optimization model of turning process is established by Non-dominated Sorting Genetic Algorithm ?(NSGA-?).The optimized processing parameter is obtained by the model.The measuring and testing system of turning is established to verify the established model.The main contents of this article are as follows:(1)On the basis of RSM and Box-Behnken test design scheme,the influence law of turning process parameters on the specific cutting energy,processing time and carbon emission is respectively analyzed by ANOVA,Error analysis and RSM.The linear terms,quadratic terms and interaction terms which have significant impact on response value are obtained through significance analysis and regression fitting.The mathematical regression model between specific cutting energy and turning process parameters is established.This provides a foundation for the high efficiency and low carbon optimization design of turning.(2)To solve the problem of efficiency and carbon emission in turning,this paper establishes an high-efficiency and low-carbon optimization model of cutting parameters under constrain of process stability,which takes depth of cut,feed rate and cutting velocity as variables,specific cutting energy,processing time and carbon emission as optimal objectives,and power,cutting force,cutting parameters,work-piece surface quality and dynamic characteristics as constraints.(3)Base on the characteristics of the proposed multi-objective optimization model,this paper uses Non-dominated Sorting Genetic Algorithm ? to obtain the Pareto optimal solutions.The machining cost model that includes carbon tax is established to select optimal solution.The pr of minimal carbon emission,and specific cutting energy,processing time and processing ocessing parameterscost are obtained for the case of machining 45 steel bar by CAK40100vl NC machine.The model is verified by the proposed experimental measuring and testing system.