Research On Intelligent Optimization Of Ceramic Wall And Floor Tile Edging Production Line

Edging production line is an important part of ceramic wall and floor tile production line.At present,the setting of edge grinding parameters is still determined by the operator’s experience.This setting method is more traditional and it is difficult to give full play to the maximum efficiency of the equipment.When the parameter setting is unreasonable,there are often phenomena such as uneven grinding,excessive grinding,high energy consumption and quality degradation.In view of the above problems,this paper takes the edging production line in the actual enterprise as the research object,combined with the computer intelligent algorithm,and takes the edging quality and energy consumption as the research goal to optimize the edging processing parameters,which are verified by experiments.The following work has been carried out:1.Through the investigation of actual enterprises,the main composition structure and processing principle of ceramic wall and floor tile edging production line are studied and analyzed,including main transmission parts,centering device,brick pushing device and grinding head assembly.According to the characteristics of edging processing,the main process parameters are determined,such as grinding depth,tool walking amount,transmission speed,grinding head pressure,grinding head speed and brick pushing speed.2.According to the principle of edge grinding,six factors and five levels orthogonal experiments are designed,and the influence laws of various process parameters on edge grinding quality and production energy consumption are analyzed.The optimal process parameter group with grinding depth of 12.25 mm,tool walking amount of 0.45mm/r,transmission speed of 100m/min,grinding head pressure of0.5MPa,grinding head speed of 2800r/min and brick pushing speed of 133m/min is obtained.Under this process parameter group,the optimal surface roughness and energy consumption are 7.246 respectively μm and 63.09Wh/m.3.Based on the edge grinding data and the principle of least square method,the regression mathematical model of edge grinding quality and production energy consumption is established,and NSGA2 genetic algorithm is used for multi-objective optimization.The surface roughness is 7.496 μ M and the energy consumption per unit length of edging is 62.83Wh/m.Through experimental processing and comparison,the results are better than the orthogonal experimental design,which verifies the feasibility of the optimization method.4.Using NSGA2 genetic algorithm to optimize the network structure,the BP-GA neural network prediction model of edging quality and energy consumption of edging production line is established,and 0.0128 is obtained μ M and 0.2166Wh/m.Compared with the prediction results of traditional BP neural network,it is concluded that BP-GA neural network has better prediction effect and prediction accuracy,which provides theoretical guidance for the intelligent production of edging production line.