变倾角激光熔覆成形层高和层宽模型研究

Height and Width Model of Cladding Layer Formed by Laser Cladding with Variable Angle

激光熔覆成形悬臂结构和大倾角强化修复需要在倾斜基面上沉积。现有工艺参数与熔覆层形貌数学模型的研究一般是基于水平基面开展的,较少有人研究空间倾斜基面的倾角对成形形貌的影响。本文采用激光内送粉技术实现大倾角熔覆,同时引入空间倾角作为影响熔覆层形貌的工艺参数。通过进行单道正交试验确定了每个倾角下的合适功率与速度区间并取交集,确定了0°~135°倾角、800~1200 W激光功率、4~8 mm/s扫描速度为目标模型输入参数的适用范围。在此范围内开展薄壁墙堆积实验,同步利用CCD层高测量系统和定距提升闭环控制算法,实时测量层高数据并控制提升量,采用金相显微镜测量成形件的层宽数据。结果表明:在相同的功率与扫描速度下,层高随着倾角增大而先减后增,倾角达到90°时层高最小;层宽随着角度增大而先增后减,倾角达到90°时层宽最大。利用获取的250组数据建立BP神经网络模型,通过输入倾角等熔覆工艺参数,能够实现对熔覆层高度和宽度的预测。

Objective The fabrication of an overhang or large inclination structure with laser cladding must be completed on an inclined substrate.Regarding the parameters and morphology of laser cladding layer,previous studies have mainly been conducted on a horizontal base plane.Few studies have focused on the influence of different inclination angles of the base plane on forming morphology.The molten pool is often stretched or even displaced by gravity when conducting multilayer deposition with a large inclination,which affects the height and width of the single pass after solidification.A slight change in the height and width can affect the final forming accuracy;whereas,large changes in the height and width,particularly when the actual layer height is inconsistent with the preset layer height,will directly affect the forming quality and continuity.Therefore,this study explores the influence of different base plane inclinations on the height and width of a single track,and uses the base plane inclination as one of the inputs to establish a neural network prediction model.Methods First,a single-factor experiment method was used to scan a single layer to determine the working range of each process parameter and the change step of the parameters.The laser power was varied from 800 to 1200 W in steps of 100 W.The scanning speed was varied from 4 to 8 mm/s in steps of 1 mm/s.The angle was varied from 0°to 135°at the step rate of 15°.Thin-wall deposition experiments were then carried out at 10 selected angles,and five groups of deposition with different power parameters at each angle were considered.Each group of thin wall was deposited with 30 layers,and the process included five groups of selected scanning speeds,which was changed every six layers.A CCD layer height measurement system was used to collect layer height data in real time during the deposition process of the thin wall.Results and Discussions The thin wall was cut from the middle,and the width of each layer of the cut section was measured.The mean values of the last three layers of every six layers in the measured layer height data are valid(Fig.6).Finally,250 sets of height and width data were obtained.Based on this data(Figs.7 and 8),a BP neural network prediction model was established.The model considers the inclination of the cladding base plane,scanning speed,and power as the input,and the height and width of the cladding layer as the output.The data containing various angles,power,and speed information were regarded as the training set to enhance the comprehensiveness of the test set.The model was built using only the training set,and the remaining data were used as the test set.The test set was only used to test the predictive ability of the model and evaluate its generalization ability.Conclusions The influence of variable angle cladding of 0°‒135°on the single-pass morphology was studied.The experimental results show that the layer height first decreases and then increases with the change in the inclination angle,and that at 90°yields the lowest layer height,which can be attributed to the constant change in the angle between the gravity direction and the growth direction.The layer width first increases and then decreases with the angle change,reaching the highest value at 90°.The root mean square error of the two established neural network prediction models is controlled below 0.1,and the 90%confidence prediction accuracy A90%is 99%and 96%,respectively(Fig.11),showing an excellent prediction effect of the established model.