Research On Prediction Techniques Of Carbon Emission During NC Milling Processes

Low-carbon manufacturing （LCM） has received increasing attention ofscientists from various countries, business R&D sectors and academia. As NCequipment is the base of manufacturing industry, low-carbon capability of it largelydetermines the completion of the targets of Chinese LCM and will helps ease severeresource constraints, high carbon emissions that our country is currently facing. Thispaper focuses on quantitative prediction and optimization modeling of carbonemissions during NC milling process. And it also proposes some LCM strategiesbased on the model, laying the foundation for the LCM technology of NCmachining.Firstly it makes comprehensive and systematic analyses on various impactfactors involved in three dimensions in NC machining process-energy, resourcesand waste, then revealed the composition and characteristics of carbon emissions. Italso analyzes the carbon emission mechanism, gives a qualitative analysis of theimpact of the factors and finally identifies key influencing factors. Through Cuttingempirical formula, theoretical formulas and mathematical relationships, amulti-dimensional carbon emission calculation model called ERWC model isconstructed. And through quantitative analysis on the impact of cutting parameters,it comes to a conclusion consistent with the qualitative analyses.With the results of prior analysis, it could preliminarily propose somemanufacturing strategies of LCM during NC machining process. However it stillneeds to determine specific parameter values by mathematical method, realizingconstrained multi-objective optimization with carbon emissions and production timeper unit volume of metal as the objective function. Finally it adopts GeneticAlgorithm to operate the optimization, and develops a system with Visual Basic6.0as the interface development tool and SQL Server2008as the database managementtool, integrating the prediction and optimization model of carbon emissions into thesystem which could make good communication with users.After realizing prediction and optimization by the system, an experimentalstudy is launched. It combines three sets of aluminum alloy cutting tests to verify thesystem. And it includes cutting force and machine power measurement experimentspre-and post-the cutting parameters optimization. By processing and calculating theexperimental data, it can make a comparison between pre-and post-optimization.Then it can be concluded that the prediction and the optimization model couldassure lower carbon emissions after parameter optimization while productivity isless affected. Therefore, both of the models are feasible.