| The rapid development of economy and the improvement of people’s material life have led to the constant consumption of energy and resources and the deteriorating environment.The development of industrialization requires the use of a large amount of fossil fuels such as coal and oil,which results in the emission of a large amount of greenhouse gases such as CO2,forming the "greenhouse effect" and causing environmental deterioration.Energy and environmental issues have gradually become a worldwide problem across politics,economy and society.Manufacturing industry as a high energy consumption industry,high efficiency and low carbon manufacturing is the key to explore sustainable development.As a common cold machining method of metal surface,NC milling plays an indispensable role in machining.There are often short tool life and high carbon emissions in CNC milling.Modeling and optimization can not only improve tool life,reduce carbon emissions,but also improve production efficiency and reduce processing costs,which is of great significance for the low-carbon development of manufacturing industry.Specific research contents are as follows:Firstly,the influence factors of NC milling tool life and carbon emission were analyzed,and the experiment of NC milling tool life and carbon emission data acquisition was designed.By analyzing the parameters involved in the tool life function of NC milling,the main factors affecting the tool life are clarified.The carbon emission assessment boundary of NC milling system was defined from input/output point of view.The carbon emission of NC milling system was divided into material carbon emission and electric energy carbon emission.The carbon emission of electric energy can be divided into standby stage,no-load stage and cutting stage.The three stages are analyzed and the corresponding carbon emission assessment model is established.According to the influencing factors and carbon emission evaluation model,the CNC milling tool life and carbon emission data acquisition experiments were designed,and the CNC milling tool life and carbon emission data were collected under different parameter combinations.Secondly,based on the collected data of NC milling tool life and carbon emission under different parameter combinations,the prediction model of tool life and carbon emission based on BP neural network was established.Aiming at the disadvantage that BP neural network is easy to fall into the local minimum,genetic algorithm(GA)is used to optimize it,so as to establish the GA-BP neural network prediction model.The results of BP neural network and GA-BP neural network model optimization are compared,and the results show that the prediction model based on GA-BP neural network is more accurate.Then,NSGA-Ⅱ algorithm was used to solve the low carbon optimization model of milling parameters.The tool life and carbon emission prediction model constructed by GABP neural network was used as the objective function,and the low carbon optimization model of cutting parameters was established to optimize the maximum tool life and the minimum carbon emission,and was solved by NSGA-Ⅱ.The solution set was sorted by TOPSIS method,the optimal milling parameter combination was determined,and the feasibility of the optimization results was verified by experiments.Finally,MATLAB APP Designer module is used to develop a multi-objective humanmachine interface for CNC milling parameters optimization based on tool life and carbon emissions.The interface mainly includes data input module,optimization parameter setting module and output module.By using this interface,users can realize the numerical control milling parameter optimization method proposed in this paper,which provides a theoretical basis for guiding the current low-carbon development of manufacturing industry. |
