Process Optimization For Energy Consumption And Surface Quality In Stainless Steel Milling

CNC machine tools are widely used,but there are problems such as large energy consumption,low efficiency and long processing cycle.Therefore,selecting reasonable process planning schemes to reduce processing energy consumption and improve processing efficiency has become a research hotspot in the field of green manufacturing.The work hardening phenomenon in the cutting process of hard-to-cut materials will aggravate tool wear,reduce energy efficiency and affect the machined surface quality of parts.Therefore,stainless steel materials are selected as the object of research in this paper,and the machine tools specific energy consumption and surface roughness are used as the evaluation standard to optimize the process of energy consumption and surface quality of hard-to-cut materials.Firstly,a specific energy prediction model of machine tool considering tool wear and surface hardness is proposed based on the analysis of the machine tool energy consumption characteristics.The model can be used to predict the energy consumption of hard-to-cut materials,which is verified by the milling experiment of 304 stainless steel designed by Taguchi method.The results show that the accuracy of the specific energy prediction model of the machine tool is above98%.In addition,the influence of cutting parameters and tool wear on the machine tool specific energy is analyzed.The specific energy of machine tool decreases with the increase of milling depth,milling width,feed rate and milling speed,and increases approximately linearly with the increase of tool wear and surface hardness.The model provides a reference for cutting energy consumption prediction and energy saving parameter selection of hard-to-cut materials in actual production.Then,to solve the problem that the accuracy and surface quality of in hard-to-cut materials cutting process are difficult to control,a prediction model of surface roughness considering hardness is proposed.The model is established based on response surface method and the validity of the model is verified by stainless steel milling experiments.The results show that the model has a high prediction accuracy of 96.7%for surface roughness.The conclusion that the most significant parameter for surface roughness is surface hardness is obtained by exploring the influence of milling parameters and surface hardness on surface roughness.The model provides guidance for improving the processing efficiency and evaluating the processing quality in stainless steel parts actual production.Finally,a multi-objective optimization model based on the lowest processing energy consumption and the best surface quality is proposed.The specific energy and surface roughness of the machine tool are selected as the optimization objectives.The NSGA-II algorithm is combined with orthogonal experimental design and response surface method to optimize,and the Pareto solution of the corresponding optimal cutting parameters is obtained.The experimental results of the empirical parameters and the optimal parameters are verified.Finally,the energy-saving parameter combination in stainless steel milling is obtained:a_p=0.6mm,a_e=4mm,f_z=0.08mm/z,v_c=130m/min.The result provides a process planning scheme for high-efficiency,energy-saving and high-quality cutting for difficult-to-cut materials.