Study Of The Milling Performance Of Glass Fiber Reinforced Composites

Glass-fiber-reinforced plastic(GFRP)composite materials exhibit superior performance compared to other traditional materials,including light weight,good electrical insulation,long service life,high strength and high modulus,and good thermal insulation properties.Due to its excellent performance,GFRP has been widely used in various industries such as construction,chemical,wind power and aviation.In GFRP production and processing,milling and drilling are common methods,and milling is an important method for forming GFRP end products.However,problems such as low milling efficiency,high power consumption,high production costs,and poor surface quality often occur in GFRP milling.Therefore,this study aims to improve the milling performance of GFRP by addressing the problems in milling processing.The study used GFRP as the experimental object and selected spindle speed,feed rate,and milling depth as variable parameters,and milling force,milling temperature,and surface roughness of the machined surface as target responses.The study conducted GFRP milling experiments using diamond tools on a machining center,and collected data and images of GFRP using instruments such as a force gauge,thermal infrared imager,surface roughness profiler,and scanning electron microscope.The response surface analysis method was used to analyze the effects of spindle speed,feed rate,milling depth,and their interactions on milling force,milling temperature,and surface roughness.The study also used response surface optimization to obtain the optimal GFRP milling parameters,with minimum milling force,milling temperature,and surface roughness as the optimization objectives.The study then used BP neural network to establish the objective function and used particle swarm optimization to perform multi-objective optimization to obtain the optimal parameters.Finally,the results of the two optimization methods were compared to obtain a better GFRP milling performance optimization method and determine the optimal milling parameters for GFRP milling.The research results show that:(1)Through single-factor analysis of milling characteristics,it can be concluded that the magnitude of milling force is negatively correlated with spindle speed,and positively correlated with feed rate and milling depth;the high or low milling temperature is positively correlated with spindle speed,feed rate,and milling depth;the magnitude of surface roughness is negatively correlated with spindle speed,and positively correlated with feed rate and milling depth;(2)All established response mathematical models have high reliability and can be used for predicting and optimizing the actual milling force,milling temperature,and surface roughness of GFRP;(3)When optimizing the milling process parameters for GFRP using multiobjective optimization,the optimal parameter combination for GFRP milling obtained by the traditional response surface method is: spindle speed n = 16405 r/min,feed rate U = 100 mm/min,and milling depth h = 0.1 mm.The optimal parameter combination obtained by the particle swarm algorithm based on the BP neural network is: spindle speed n = 17469 r/min,feed rate U = 100 mm/min,and milling depth h = 0.1 mm;(4)After GFRP milling,the main surface defect is tearing,and the surface quality of the workpiece is significantly better under the optimal parameters compared to other parameters;(5)Under the optimal milling parameters,the accuracy of the response surface method optimized model is 90.06% as determined by the verification experiment,while the accuracy of the algorithm optimized model is 94.43%,which is an improvement of4.37%.Moreover,compared to the traditional response surface method,the actual target response values obtained by the algorithm optimization show that the milling force F is reduced by 2.52%,the milling temperature T is increased by 3.06%,and the surface roughness Ra is reduced by 2.63%.It can be seen that the optimal milling parameters obtained by the algorithm optimization are more accurate than those obtained by the traditional response surface method and have better overall milling performance;This study,based on the practical production problems of Leco Precision Tools Co.,Ltd.,has certain application value and can provide theoretical guidance for actual GFRP milling processes.