| CNC workshops play an important role in discrete manufacturing companies.However,the CNC workshop has a series of problems such as large energy consumption,many information islands,and difficulty in controlling energy consumption.As one of the common workshops in discrete factories,reducing equipment energy consumption and managing energy-consuming equipment have important research significance for energy saving and emission reduction in CNC workshop.Based on this,this article relies on the Chongqing technology innovation and application development project "Datadriven factory green efficiency key technologies and applications"(project number:cstc2019jscx-mbdx0118),based on the energy-saving optimization and energy management of the CNC workshop of a large agricultural manufacturing enterprise in Chongqing For the research goal,carry out research on energy saving optimization and energy management system for CNC workshop,the main contents are as follows:(1)Through the analysis of multi-source heterogeneous data and statistical analysis of energy consumption in CNC workshops,a method for analyzing energy consumption characteristics of CNC workshops based on multi-source heterogeneous is proposed.Firstly,the complexity of energy consumption in CNC workshops and the characteristics of multi-source heterogeneous data are analyzed.Secondly,statistical analysis of the CNC workshop,including the analysis of the proportion of equipment energy consumption and the data analysis of the CNC workshop.Finally,the energy consumption characteristics of the CNC workshop is analyzed,which mainly includes the analysis of electricity and electricity costs,analogy analysis and time ratio analysis.The analysis of energy consumption characteristics of the CNC workshop not only provides a theoretical basis for digging out high-energy-consuming equipment in the actual production process,but also provides a theoretical basis and direction for the energy-saving optimization of the CNC workshop.(2)Through the analysis of energy-saving optimization problems in CNC workshops,an energy-saving optimization method for CNC workshops based on multi-island genetic algorithm is proposed.Firstly,by analyzing the main sources of energy consumption in the CNC workshop,it is found that milling machines account for the highest proportion of energy consumption.A multi-objective optimization model of milling power(P)and material removal rate(MRR)of CNC workshop milling machine equipment is constructed.The model mainly considers the trade-off relationship between milling power(P)and material removal rate(MRR).Secondly,the method of combining entropy weight and experiment is used to calculate the weight objectively,and the model is solved by the multi-island genetic optimization algorithm(MIGA).Finally,combined with case studies and compared with existing machining methods in CNC workshops,it is proved that the proposed method can provide theoretical support for energy saving in CNC workshops.(3)In order to verify the effectiveness of the proposed energy-consumption characteristic analysis and energy-saving optimization methods,and to solve the problems of multiple information islands and difficulty in controlling energy consumption in CNC workshops,an intelligent data collection and energy management system for CNC workshops was developed.Firstly,through the analysis of the multisource heterogeneous data and energy consumption characteristics of the CNC workshop,the types and equipment of data collection are determined,the multi-source heterogeneous data intelligent collection system architecture is built,and the data intelligent collection terminal is designed.Secondly,design the energy management system of the CNC workshop,which mainly includes hardware platform,software platform design,and system function design.Finally,through the application and verification in the CNC workshop of a large enterprise in Chongqing,the effectiveness of the system is proved.And based on the energy management system,the effectiveness and practicability of the analysis results of energy consumption characteristics and energysaving optimization methods are verified. |
