Energy Efficiency Evaluation Model And Process Optimization Of CNC Turning

Energy consumption in machining contributes a significant part of manufacturing cost,and has a great impact on the environment.Machine tools are widely used in the field of metal cutting,but their energy efficiency is very low.Therefore,the improvement of machine tool energy efficiency and energy-saving strategy has become a research hotspot of sustainable manufacturing.Specific energy consumption and surface roughness are important indicators for evaluating energy efficiency and cutting quality in machining.Accurate prediction of them is the basis for realizing process optimization.Although tool wear is inevitable,the effect of tool wear was seldom considered in the previous prediction models.Furthermore,most of the existing energy consumption models of CNC lathe are only suitable for specific machine tools,and cannot be applied to other machine tools with different specifications.To end this,this paper studied the energy efficiency and process optimization of CNC turning.First,an empirical model of net removal material specific energy consumption in CNC turning based on turning parameters and tool wear was developed.The model does not depend on specific machine tool components,and can predict the net removal material energy consumption before turning.The comparative experiments showed that the prediction accuracy of the model is 95%because the influence of tool wear is taken into account.In addition,the effects of turning parameters and tool wear on net removal material specific energy consumption were studied.With the increase of cutting depth,the net removal material specific energy consumption decreases.With the increase of spindle speed,the additional load loss power of spindle system increases,so the net removal material specific energy consumption increases.The net removal material specific energy consumption increases approximately linearly with tool wear.Second,the prediction models for machine tool specific energy consumption and surface roughness considering tool wear evolution were developed.The cutting depth,feed rate,spindle speed and tool flank wear were featured as input variables,and the orthogonal experimental results were used as training samples toestablish the prediction models based on support vector regression(SVR)algorithm.The proposed models were verified with wet turning 45 # steel experiments.The experimental results indicated that the improved models based on turning parameters and tool wear have higher prediction accuracy than the prediction models only considering turning parameters.The prediction accuracy of machine tool specific energy consumption model and surface roughness model is97.99% and 91.40%,respectively.Finally,a multi-objective optimization method for turning parameters was proposed,which combines orthogonal experimental design,grey correlation analysis and response surface method.Taking cutting quality,production efficiency and machine tool energy consumption as optimization objectives,surface roughness,material removal rate and specific energy consumption of machine tool are selected as evaluation criteria.The results of 304 stainless steel turning experiments showed that the optimized turning parameters can reduce the energy consumption while reducing surface roughness and increasing material removal rate.As such,the proposed optimization method realizes the trade-offs between cutting quality,production rate and energy consumption,and may provide useful guides on turning parameters selection.