Research progress in heat source and path planning of directed energy deposition-arc
-
摘要:
电弧增材制造技术因其优异特性在科研和工业装备领域备受关注，在航空航天和武器装备等高端装备制造领域显示出巨大的应用潜力.电弧增材热源是用于熔化金属丝材的重要能量源，是电弧增材制造的核心之一，而电弧热源的稳定性和控制精度直接影响到金属丝材的熔化质量，热输入量决定构件的成形效率. 轨迹规划则是电弧增材制造中另一个至关重要的核心技术，直接影响构件的成形质量和性能. 文中概述了近年来多电极耦合电弧、主动约束电弧和多能场复合电弧等电弧增材热源以及轨迹规划方面的研究进展，并对未来研究方向做出展望.
Abstract:Directed energy deposition-arc (DED-Arc) technology has garnered significant attention in both scientific research and industrial equipment fields due to its exceptional characteristics. It shows immense application potential in equipment manufacturing sectors such as aerospace and weaponry. The heat source, a core component of DED-Arc, is used for melting metal wires. The stability and control precision of the heat source directly affect the melting quality of the metal wires, while the amount of heat input determines the forming efficiency. Path planning is another critical core technology, directly influencing the forming quality and properties of the components. This paper outlines the recent research progress in heat sources of DED-Arc, including multi-electrode coupled, actively constrained, and multi-energy field composite arcs, as well as advancements in path planning. Additionally, it provides prospects for future research directions.
-
Keywords:
- additive manufacturing /
- directed energy deposition-arc /
- heat source /
- path planning
-
0. 序言
残余应力和变形是焊接过程中常见的问题，会对焊接工件的质量和性能产生重要影响，因此，预测焊接残余应力和变形已经成为焊接领域中的重要研究内容. 焊接工艺参数，焊后残余应力及变形之间是一种非常复杂的非线性映射关系，在厚板焊接中对焊接残余应力与变形影响较大的填充层焊接工艺参数主要有焊接电流、电弧电压、焊接速度以及层间温度[1-3]. 焊接残余应力与变形的预测是高度非线性问题，很难用简单的数学模型来描述，需要通过各种方法和经验对其中的权重和因子进行计算.
人工神经网络可实现复杂非线性系统的建模和预测，具有强大的联想能力、容错能力以及自适应学习能力，近年来在焊接质量预测中得到了广泛关注[4-7]，其中BP神经网络是人工神经网络一种常用的深度学习模型，可以在没有大量数据的情况下进行预测，BP神经网络具有较强的自适应学习能力和非线性拟合能力，已有诸多学者将其应用于非线性领域的预测中，例如Shanavas等人[8] 通过建立反向传播神经网络模型，来预测铝合金搅拌摩擦焊焊缝的抗拉强度和伸长率，模型预测误差小于5%；马思群等人[9] 采用BP神经网络的方法对铝合金车体的焊接残余应力进行预测，预测误差在8%以内.
传统的BP神经网络在训练过程中容易出现梯度消失或梯度爆炸等问题，导致模型训练困难或预测效果不佳，遗传算法是一种优化算法，可用于解决复杂问题时寻找最优解，近年来遗传算法在神经网络优化方面得到了广泛应用，研究表明遗传算法可以在一定程度上解决BP神经网络训练困难的问题，从而提高模型的预测精度[10-11]，例如张天鹏等人[12] 基于GA-BP神经网络来预测FSW接头的性能，结果表明，实际值与预测值之间的误差在5%左右，具有较高的预测精度；关子奇等人[13] 采用GA遗传算法来优化BP神经网络模型来预测焊接熔池照度，试验结果表明，优化后的GA-BP神经网络模型对焊接熔池照度的预测更为精准；张永波等人[14] 基于GA-BP神经网络对带钢焊接质量进行预测，结果表明，与传统的BP神经网络模型相比，通过遗传算法对BP神经网络的权值和阈值进行优化，改良后的模型无论是在精度上还是在收敛速度上都有了大幅度的提升.
文中探讨基于遗传算法优化的BP神经网络在316L中厚板TIG焊的焊接残余应力和变形预测中的应用. 首先，采用正交试验的方法获取焊接工艺参数，残余应力和变形之间的典型样本数据；然后，介绍传统BP神经网络的原理和在焊接残余应力及变形预测中的应用；其次，介绍遗传算法的基本原理和应用优势，并探讨其在BP神经网络优化中的作用；最后，结合具体的焊接试验对两种神经网络模型的预测结果分别进行验证和误差计算，并对基于遗传算法优化的BP神经网络，在焊接残余应力和变形预测中的预测精度以及该方法的收敛速度等优缺点进行讨论.
1. 试验设计
1.1 试验方法
试验使用的焊机为松下YC-500WX5，焊丝牌号为TGS-316L，保护气体为全氩气，焊接试板材料为316L不锈钢，尺寸为300 mm × 300 mm × 20 mm，采用U形7.5°坡口，钝边为1.5 mm，装配间隙为2.5 mm，施焊20层，每层为多道焊，每层焊厚不超过2 mm. 打底层采用的焊接电流为60 A，电弧电压为12 V，焊接速度为50 mm/min，盖面层采用的焊接电流为180 A，电弧电压为17 V，焊接速度为80 mm/min.
为了测量不同填充层焊接工艺参数对焊接变形的影响，在考虑焊接工装夹的情况下，最终采用反变形的方法来测量计算每块试验样板最终的角变形，如图1所示，并通过点焊的方式将每块试板的反变形角度统一固定为20°，此外，为了焊接时能够避免起弧和落弧留在试板上，造成对接板件边缘焊接缺陷，保证所有试板宽度范围内的焊缝都能够达到相同的强度，在每对焊接试板的焊缝两端各放置一块引弧板，通过点焊连接，见图1(b).
![]()
图 1 焊接试板反变形示意图Figure 1. Welding indication. (a) Sketch of striking arc, ending arc and spot welding position; (b) Sketch of the anti-deformation of the welding test plate对焊缝填充层焊接工艺参数，采用正交试验设计L16(44)因素水平表，并以填充层采用的焊接电流、电弧电压、焊接速度及层间温度为因素，经过综合分析试验材料的板厚、材质以及焊接顺序等确定焊接工艺参数的水平值，具体参数见表1.
表 1 因素水平表L16(44)Table 1. Factor level table for L16(44)水平
编号焊接电流
I/A电弧电压
U/V焊接速度
v/(mm·min−1)层间温度
T/℃1 A1(140) B1(14) C1(50) D1(80) 2 A2(165) B2(16) C2(90) D2(120) 3 A3(190) B3(18) C3(120) D3(160) 4 A4(215) B4(20) C4(150) D4(200) 1.2 残余应力及变形测量
试板上的残余应力并不是处处相同，测量时采用焊缝方向和垂直于焊缝方向上均匀取点的方法进行测量，图2为沿焊缝横向和纵向的残余应力测试点分布示意图，其中测试点有13个，沿垂直焊缝方向的残余应力测试点分别在距焊缝中心20，50，80，110，140 mm处；而沿着焊缝方向的测试点，即A，B，C，D，E，F，G，H，I，每个点之间间距为30 mm. 由于盲孔法测残余应力的局限性，文中所测残余应力均为试板近表面残余应力，将测试结果进行比较，得出焊缝近表面横纵向最大残余应力.
![]()
图 2 试板的残余应力测试取点示意图(mm)Figure 2. Sketch of the point selection for residual stress testing of the test plate如图3所示，试验采用盲孔法测量316L试板焊缝近表面的残余应力，使用角度尺等工具测量试板焊接角变形，并将测得的数据作为样本数据集应用于焊接残余应力和变形预测模型的训练.
![]()
图 3 盲孔法测量残余应力示意图Figure 3. Schematic diagram of the blind hole method for measuring residual stress1.3 正交试验
按照L16(44)正交试验的方法测得的试验结果见表2，在打底层和盖面层的焊接参数相同的条件下，填充层不同的焊接工艺参数对焊接残余应力和角变形的影响程度是显著不同. 横向最大残余应力第6组最大，为444.5 MPa；第18组最低，为123.9 MPa，相差320.6 MPa；纵向最大残余应力第10组最大，为494.3 MPa；第17组最小，为136.7 MPa，相差357.6 MPa；角变形第14组最大，为9.84°；第一组最小，为6.18°，相差3.66°.
表 2 正交试验结果Table 2. Results of the orthogonal experiment序号 焊接电流
I/A电弧电压
U/V焊接速度
v/(mm·min−1)层间温度
T/℃横向最大残余应力
σx-max/MPa纵向最大残余应力
σy-max/MPa角变形
θ/(°)1 140 14 50 80 124.4 386.7 6.18 2 140 16 90 120 287.6 275.8 7.56 3 140 18 120 160 237.3 241.9 6.93 4 140 20 150 200 413.3 187.6 7.42 5 165 14 90 200 397.6 284.3 7.12 6 165 16 50 160 444.5 365.8 7.88 7 165 18 120 120 315.3 397.2 9.33 8 165 20 150 80 274.3 426.3 8.52 9 190 14 120 120 356.1 468.4 8.78 10 190 16 150 80 333.1 494.3 8.81 11 190 18 50 200 297.6 412.6 9.24 12 190 20 90 160 183.7 471.8 8.38 13 215 14 150 160 244.2 356.1 9.56 14 215 16 120 200 174.3 337.1 9.84 15 215 18 90 120 211.3 387.5 9.36 16 215 20 50 80 193.5 434.8 9.21 17 163 17 120 88 163.1 136.7 8.41 18 175 18 110 165 123.9 256.8 8.65 19 185 15 100 95 263.4 313.7 8.93 20 210 19 135 145 309.8 351.8 9.43 2. 神经网络预测
2.1 BP神经网络
BP神经网络是一种按照误差逆向传播算法训练的多层前馈神经网络，是应用最广泛的神经网络模型之一[15]，具有任意复杂的模式分类能力和优良的多维函数映射能力. 在网络结构上，BP神经网络由输入层、隐含层、输出层组成；在计算方法上，BP神经网络以网络误差平方为目标函数，利用梯度下降法来计算目标函数的最小值，因此，BP神经网络在解决非线性拟合的问题上具有巨大的优势[16] .
BP神经网络的主要特点是信号前向传播，误差反向反馈传播，设定期望输出，网络根据预测输出与期望输出的误差对比结果进行反向传播，调整各神经元的权值和阈值，整个过程反复进行，直到网络样本输出误差小于期望误差精度或训练达到最大迭代次数为止[17] .
图4为神经网络结构示意图，x1，x2，x3，x4是神经网络模型的输入, y1，y2，y3是网络的输出，整个神经网络模型可以看成一个非线性函数，网络的输入x1，x2，x3，x4是自变量，网络的输出y1，y2，y3为因变量，自变量和因变量构成了函数映射关系. 假设
$ f $ 为激活函数的映射关系，将该神经网络的隐藏层神经元激活函数设定为Sigmoid函数, 将输出层神经元激活函数设定为Purelin线性函数.Sigmoid函数公式为
$$ f\left(x\right)=\frac{1}{1 + {e}^{-x}} $$ (1) Purelin线性函数公式为
$$ f\left(x\right)=x $$ (2) 故此时前向传播公式为
$$ {u}_{j}=f\left({\sum }_{i=1}^{n}{v}_{ij}{x }_{i} + {\theta }_{j}^{u}\right)，j={\mathrm{1,2},\cdots ,}m $$ (3) $$ y=f\left({\sum }_{j=1}^{m}{w}_{j}{u}_{j} + {\theta }^{y}\right) $$ (4) 为了推演方便，先拿第k个对象作为目标，假设
$ {y}^{\left(k\right)} $ 为经过神经网络预测出的值，$ {y}^{{\left(k\right)}^{'}} $ 为真实的输出值, 为了得到真实值与预测值之间误差尽可能小的目标，将目标函数设定为$$ J=\sum _{k}^{\infty }{\left({y}^{\left(k\right)}-{y}^{{\left(k\right)}^{'}}\right)}^{2} $$ (5) 式中假设
$ {v}_{ij} $ 为第i个输入变量与第j个隐藏神经元的权重，$ {\mathrm{\theta }}_{j}^{u} $ 为隐藏层u第j个神经元的阈值，或者称为偏置项，$ {w}_{j} $ 为第j个神经元与y连接的权重，$ {\mathrm{\theta }}^{y} $ 为y的偏置，$ {u}_{j} $ 为隐藏层第j个神经元，$ {v}_{ij} $ ，$ {\theta }_{j}^{u} $ ，$ {w}_{j} $ ，$ {\theta }^{y} $ 为需要确定的网络参数. 可以采用梯度下降法对网络参数和目标函数$ J $ 进行权重更新和优化，其中$ \mu $ 为学习率.$$ {\nu }_{ij}^{'}={v}_{ij}-\mu \frac{dJ}{d{v}_{ij}} $$ (6) $$ {w}_{j}^{'}={w}_{j}-\mu \frac{dJ}{d{w}_{j}} $$ (7) $$ {\theta }_{j}^{u'}={\theta }_{j}^{u}-\mu \frac{dJ}{d{\theta }_{j}^{u}} $$ (8) $$ {\theta }^{y'}={\theta }^{y}-\mu \frac{dJ}{d{\theta }^{y}} $$ (9) 式中：
$ {v}_{ij}\mathrm{'} $ ，$ {\theta }_{j}^{u'} $ ，$ {{w}_{j}}' $ ，$ {\theta }^{y'} $ 为更新以后的网络参数，通过不断迭代更新网络参数，当样本训练输出误差小于期望误差或者达到最大迭代次数时，得到的网络参数就是训练之后的最佳神经网络模型.目标函数对各神经网络中参数的微分由反向梯度下降公式求得
$$ \frac{\partial {J}^{\left(k\right)}}{\partial {w}_{j}}=2\left({y}^{\left(k\right)}-{y}^{\left(k\right)'}\right){u}_{j} $$ (10) $$ \frac{\partial {J}^{\left(k\right)}}{\partial {\theta }^{y}}=2\left({y}^{\left(k\right)}-{y}^{\left(k\right)'}\right) $$ (11) $$ \frac{\partial {J}^{\left(k\right)}}{\partial {\nu }_{ij}}=2\left({y}^{\left(k\right)}-{y}^{\left(k\right)'}\right){w}_{j}{u}_{j}\left(1-{u}_{\dot{j}}\right){x}_{i}^{\left(k\right)} $$ (12) $$ \frac{\partial {J}^{\left(k\right)}}{\partial {\theta }_{j}^{u}}=2\left({y}^{\left(k\right)}-{y}^{\left(k\right)'}\right){w}_{j}{u}_{j}\left(1-{u}_{\dot{j}}\right) $$ (13) 在BP神经网络训练学习过程中，网络的初始权值对学习效果和收敛速度有着至关重要的影响，合适的初始值可以使网络模型较快收敛到最优解，如果初始权值和阈值选取不当，会导致网络的收敛速度慢并且容易陷入局部最小值，由于BP神经网络的初始权值是随机赋予的，因此非常容易出现收敛速度慢且易陷入局部最小值的问题[18] .
采用遗传算法来优化BP神经网络的初始权值和阈值，以提高网络输出的准确性和速度，从而加速BP神经网络的收敛速度，解决局部极值问题，再通过BP神经网络的反向传播算法来寻找整个网络参数的最优解，以实现更好的预测效果.
2.2 GA-BP神经网络
遗传算法是一种模拟自然界遗传机制，以达尔文的生物进化论为理论基础，通过数学利用仿真运算的过程搜索最优解算法. 利用遗传算法，首先需要创建初始种群，选择种群规模为
$ M $ ，取网络所有权值和阈值组成$ N $ 维向量，随机生成一个$ M\cdot N $ 的矩阵作为初始种群，其中N=inputnumber·hiddennumber + hiddennumber + hiddennumber·outputnumber + outputnumber，适应度是遗传算法中衡量个体性能的主要指标，根据适应度的大小对个体进行筛选，因此，适应度是遗传算法的动力. 由于遗传算法的复杂度主要取决于适应度函数的复杂度，因此适应度函数的设计应尽可能简单，以减少算法的计算时间.遗传算法首先需要对初始值进行编码，由于BP神经网络采用了Sigmoid函数作为激活函数，其权值和阈值介于−1 ~ 1，因此采用实数编码会比二进制编码更合适. 误差与适应度为相反的关系，当适应度越好时，预测的结果越准确，误差值越小，因此采用均方误差的倒数作为适应度函数.
适应度函数的公式为
$$ F\left(x\right)=\frac{1}{J\left(x\right)} $$ (14) 遗传算法主要包括选择操作、交叉操作和变异操作，在进行选择操作时，个体被选中的概率与适应度相关，当个体的适应度越大，被选中的可能性就越大，因此最常用到的方法就是轮盘赌法，每个个体被选择的概率为
$$ {p}_{i}=\frac{{F}_{i}}{\displaystyle\sum _{i=1}^{N}{F}_{i}} $$ (15) 式中：
$ {F}_{i} $ 为个体$ i $ 的适应度函数值；$ N $ 为种群个体数量.在进行交叉操作时，将第
$ k $ 条染色体$ {a}_{k} $ 和第$ l $ 条染色体$ {a}_{l} $ 同时在$ j $ 位置进行交换，交换方式如下$$ {a}_{kj}={a}_{kj}\left(1-b\right) + {a}_{lj}b $$ (16) $$ {a}_{lj}={a}_{lj}\left(1-b\right) + {a}_{kj}b $$ (17) 式中：b是[0,1]的随机数.
在进行变异操作时，通过选择第
$ i $ 个个体的第$ j $ 个基因$ {a}_{ij} $ 进行变异，具体操作方法如下$$ {a}_{ij}=\left\{\begin{array}{c}{a}_{ij} + {(a}_{ij}-{a}_{\max})\cdot f(g),r > 0.5\\ {a}_{ij} + {(a}_{\min}-{a}_{ij})\cdot f(g),r\leqslant 0.5\end{array}\right. $$ (18) $$ f\left(g\right)=r{\cdot \left(1-\frac{g}{{G}_{\max}}\right)}^{2} $$ (19) 式中：
$ {a}_{\max} $ 与$ {a}_{\min} $ 分别是基因$ {a}_{ij} $ 的上限和下限；$ r $ 是0 ~ 1之间的随机数；$ g $ 是当前迭代次数；$ {G}_{\max} $ 是最大进化次数.在传统的BP神经网络之前，使用遗传算法进行编码、选择、交叉和变异操作，以优化网络的初始权值和阈值，这种改进避免了传统生物神经网络随机初始化带来的问题，例如网络陷入局部最小值. 遗传算法可以确定最佳的初始权值和阈值，从而解决了传统BP神经网络的局限性，图5为GA-BP神经网络的算法流程图.
3. 仿真试验验证
3.1 数据预处理
经过正交试验L16(44)共获得16组残余应力和角变形的实测数据，再分别采用不同的焊接工艺参数随机进行4组试验，一共获得20组试验数据. 试验取前16组数据作为训练样本，用于GA-BP神经网络的学习和训练，取后4组随机的试验数据作为验证样本，用于验证神经网络的预测精度和泛化能力.
考虑到输入层各个参数之间差异比较大，故对输入层的样本数据采用Mapminmax函数进行归一化处理，将数值变换到0 ~ 1，目的在于消除因量纲不同对预测结果造成的影响，具体消除方法如下
$$ {x}^{*}=\frac{\left(x-{x}_{\min}\right)({y}_{\max}-{y}_{\min})}{{x}_{\max}-{x}_{\min}} + {y}_{\min} $$ (20) 式中：
$ {y}_{\max} $ 和$ {y}_{\min} $ 为设定的上下限，分别为1和0；$ x $ 是待归一化的样本数据；$ {x}_{\max} $ 和$ {x}_{\min} $ 分别为样本数据的最大值和最小值；$ {x}^{*} $ 是样本数据归一化处理后得到的结果.3.2 仿真试验
为了验证改进后的遗传算法对焊接残余应力和变形有良好的预测效果，因此需要进行仿真试验，数据集为上述预处理后的数据，在Matlab2021b中对预测模型进行训练，结果表明，利用遗传算法优化后的BP神经网络模型，对焊接残余应力和变形的预测在预测精度和泛化能力方面比传统的BP神经网络效果要好.
文中的仿真试验，通过焊接电流、电弧电压、焊接速度以及层间温度来预测焊接之后的残余应力和变形，因此输入层节点为4个，输出层节点为3个，关于隐含层的节点数根据经验公式手动设定
$$ m_1=\sqrt{a + b} + k $$ (21) 式中：
$ m_1 $ 为隐含层节点数；$ a $ 和$ b $ 分别为输入层和输出层的节点数；$ k $ 为1 ~ 10之间的常数.3.3 参数设定
采用神经网络对模型训练时，隐藏层设定为两层，在Matlab中的基本参数设置如下.
(1) net.trainParam.epochs=100，迭代次数为100次.
(2) net.trainParam.lr=0.1，学习率为0.1.
(3) net.trainParam.goal=0.001，训练目标误差为0.001.
设置遗传算法的迭代次数为50，种群规模为20，发生交叉的初始概率为0.6，变异概率为0.4.
3.4 仿真结果及分析
使用Matlab2021b软件分别对BP神经网络和GA-BP神经网络进行训练，预测结果与实际值比较结果如图6所示.
![]()
图 6 BP和GA-BP神经网络训练和预测结果Figure 6. Training and prediction results of BP and GA-BP neural networks. (a) maximum lateral residual stress of the training sample； (b) maximum longitudinal residual stress of training sample； (c) training sample deformation angle； (d) test the maximum lateral residual stress of the sample； (e) longitudinal maximum residual stress of the sample is tested； (f) test sample deformation angle通过对比优化前后神经网络模型的预测输出以及期望输出分析神经网络的预测效果，从图6中可以看出，BP神经网络模型的预测较不稳定，在个别样本点的预测误差较大，在少数样本点以及测试点的预测精度较高，因此BP神经网络模型的数据拟合能力和预测性都不够稳定. 对比图6中这6张图不难看出，无论是训练样本还是测试样本，GA-BP神经网络的预测值比BP神经网络更接近于实际测量值，拟合程度更高，预测效果更好.
BP和GA-BP神经网络适应度曲线如图7所示，GA-BP神经网络的收敛速度和训练效率都明显高于BP神经网络，通过对比两种模型的适应度曲线，可以看出BP网络的曲线波动更大，GA-BP网络的曲线更加平滑，这表明优化后的网络模型收敛更快，训练更加稳定，优化后网络的最佳适应度值更低，模型的预测结果与实际结果之间的差异更小，预测结果更好.
![]()
图 7 适应度变化曲线Figure 7. Curves of fitness changes. (a) BP neural network; (b) GA-BP neural network神经网络预测平均相对误差见表3，BP神经网络对横、纵向残余应力和角变形的训练样本预测误差分别为12.15%，12.52%，6.8%，GA-BP神经网络的训练样本预测误差分别为2.51%，2.01%，1.28%，由此可见优化后的网络模型预测效果显著提高. BP神经网络的测试集的预测误差分别为14.56%，14.35%，8.53%，GA-BP神经网络的预测误差分别为2.38%，2.84%，1.32%，优化后的网络模型无论是在训练集还是测试集上的表现都优于传统的BP神经网络. GA-BP神经网络预测模型在预测结果上有了一定的改善，在预测精度上明显优于传统的BP神经网络模型，平均相对误差在3%以内.
表 3 神经网络预测平均相对误差Table 3. Average relative error of neural network prediction输出结果 BP GA-BP 训练样本 测试样本 训练样本 测试样本 横向最大残余应力 12.15% 14.56% 2.51% 2.38% 纵向最大残余应力 12.52% 14.35% 2.01% 2.84% 角变形 6.8% 8.53% 1.28% 1.32% 试验结果表明，根据正交试验和预测值不难发现相对于电弧电压，焊接电流对残余应力和角变形的影响更大，随着电流电压的增大，残余应力呈现上升趋势，同时变形也在增大.
通过对比表中的平均相对误差表值，可以得出GA-BP模型比BP网络模型的误差小，预测的精度更高，通过采用随机试验的方式验证模型的预测精度和误差，可以发现模型的泛化能力很好，并且优化后模型的泛化能力比传统BP神经网络模型表现更加优异，通过对比两组模型的训练迭代次数和适应度曲线图，可以发现GA-BP的收敛速度要明显好于BP，因此GA-BP的收敛性更好，总体效果更好.
4. 结论
(1) BP神经网络的预测相对误差在15%以内，而GA-BP神经网络的预测相对误差能控制在3%以内，因此GA-BP神经网络比传统的BP神经网络预测效果更好，拟合能力更突出，并且模型的收敛速度更快，训练过程更稳定.
(2) GA-BP神经网络模型对横、纵向残余应力以及角变形的真实值随机测试预测误差分别为2.38%，2.84%，1.32%，结果表明，GA-BP神经网络能够精准得反映多层多道焊填充层焊接工艺参数与焊后残余应力之间的非线性映射关系，相对误差小，具有较强的预测能力和泛化能力，该方法对奥氏体不锈钢中厚板多层多道焊填充层焊接工艺参数具有指导作用，并对焊后残余应力和变形控制提供了一种新的思路和方法，在焊接过程中发挥了重要作用.
-
![]()
图 1 不同电弧增材热源示意图[19]
Figure 1. Schematic diagram of different DED-Arc heat sources. (a) GMAW; (b) GTAW; (c) PAW
![]()
图 2 以工件熔池为耦合载体的热源
Figure 2. Heat source with the workpiece molten pool as the coupling carrier. (a) schematic diagram of Tandem; (b) schematic diagram of triple-wire of bimetallic electrode open arc + single wire; (c) schematic diagram of high-speed triple-wire gas metal arc welding; (d) schematic diagram of parallel multi-electrode tungsten inert gas arc welding
![]()
图 3 以焊枪为耦合载体的热源
Figure 3. Heat source with the welding torch as the coupling carrier. (a) schematic diagram of pulse bypass coupled GMAW; (b) schematic diagram of the double-wire triple-arc process
![]()
图 4 以电弧自身为耦合载体的热源
Figure 4. Heat source with the arc itself as the coupling carrier: (a) schematic diagram of cross coupled arc; (b) schematic diagram of bypass coupled arc
![]()
图 5 等离子弧增材
Figure 5. Plasma arc additive manufacturing. (a) schematic diagram of variable polarity plasma arc; (b) schematic diagram of bypass coupled plasma arc
![]()
图 6 强制拘束型电弧增材[39]
Figure 6. Compulsively constricted DED-Arc. (a) schematic diagram of a special welding torch; (b) schematic diagram of the process
![]()
图 7 缆式多丝电弧增材[46]
Figure 7. Cable-type multi-wire DED-Arc. (a) schematic diagram of the process; (b) cable-type welding wire
![]()
图 8 激光-电弧复合增材
Figure 8. Laser-arc hybrid additive manufacturing. (a) schematic diagram of the process; (b) schematic diagram of laser-assisted AC double-wire indirect arc
![]()
图 9 超声-电弧复合增材示意图[51]
Figure 9. Schematic diagram of ultrasonic-arc hybrid additive manufacturing
![]()
图 10 磁场-电弧复合增材[56]
Figure 10. Magnetic field-arc hybrid additive manufacturing. (a) schematic diagram of the process; (b) schematic diagram of the effect of the magnetic field on the arc
![]()
图 11 热丝-电弧复合增材
Figure 11. Hot wire-arc hybrid additive manufacturing. (a) schematic diagram of hot wire TIG; (b) schematic diagram of resistance hot wire
![]()
图 13 不同网格结构成形策略[73]
Figure 13. Forming strategies of different grid structures. (a) sequence diagram of path planning for angular grid structures; (b) sequence diagram of path planning for orthogonal grid structures; (c) schematic diagram of path planning for equal grid structures; (d) algorithm solution flowchart
-
[1] 韩启飞, 符瑞, 胡锦龙, 等. 电弧熔丝增材制造铝合金研究进展[J]. 材料工程, 2022, 50(4): 62 − 73. doi: 10.11868/j.issn.1001-4381.2021.000343 Han Qifei, Fu Rui, Hu Jinlong, et al. Research progress in wire arc additive manufacturing of aluminum alloys[J]. Journal of Materials Engineering, 2022, 50(4): 62 − 73. doi: 10.11868/j.issn.1001-4381.2021.000343
[2] Xu J, Gu X, Ding D, et al. A review of slicing methods for directed energy deposition based additive manufacturing[J]. Rapid Prototyping Journal, 2018, 24(6): 1012 − 1025. doi: 10.1108/RPJ-10-2017-0196
[3] Wu B, Qiu Z, Dong B, et al. Effects of synchronized magnetic arc oscillation on microstructure, texture, grain boundary and mechanical properties of wire arc additively manufactured ti6al4v alloy[J]. Additive Manufacturing, 2022, 54: 102723. doi: 10.1016/j.addma.2022.102723
[4] Ma Yan, Cuiuri Dominic, Shen Chen, et al. Effect of interpass temperature on In-situ alloying and additive manufacturing of titanium aluminides using gas tungsten arc welding[J]. Additive Manufacturing, 2015, 8: 71 − 77. doi: 10.1016/j.addma.2015.08.001
[5] Zhou Xiangman, Tian Qihua, Du Yixian, et al. Investigation of the effect of torch tilt and external magnetic field on arc during overlapping deposition of wire arc additive manufacturing[J]. Rapid Prototyping Journal, 2020, 27(1): 24 − 36.
[6] Tang Shangyong, Wang Guilan, Song Hao, et al. A novel method of bead modeling and control for wire and arc additive manufacturing[J]. Rapid Prototyping Journal, 2021, 27(2): 311 − 320. doi: 10.1108/RPJ-05-2020-0097
[7] Yu Xueqi, Bai Xingwang, Shi Xiqiao, et al. Microstructures and properties of wire and arc additively manufactured steel matrix composites with addition of wc by gravity-driven side powder feeding[J]. Journal of Manufacturing Processes, 2022, 81: 236 − 249. doi: 10.1016/j.jmapro.2022.07.004
[8] 张金田, 王杏华, 王涛, 等. 电弧增材制造单道单层工艺特性研究[J]. 材料导报, 2020, 34(24): 24132 − 24137. doi: 10.11896/cldb.19110204 Zhang Jintian, Wang Xinghua, Wang Tao, et al. Study on the processing characteristics of single-bead and single-layer in the WAAM[J]. Materials Reports, 2020, 34(24): 24132 − 24137. doi: 10.11896/cldb.19110204
[9] 杨海欧, 王健, 王冲, 等. 电弧增材制造TC4钛合金宏观晶粒演化规律[J]. 材料导报, 2018, 32(12): 2028 − 2031,2046. doi: 10.11896/j.issn.1005-023X.2018.12.016 Yang Haiou, Wang Jian, Wang Chong, et al. Macrostructure evolution of TC4 titanium alloy fabricated by wire and arc additive manufacturing[J]. Materials Reports, 2018, 32(12): 2028 − 2031, 2046. doi: 10.11896/j.issn.1005-023X.2018.12.016
[10] 林鑫, 黄卫东. 应用于航空领域的金属高性能增材制造技术[J]. 中国材料进展, 2015, 34(9): 684 − 688 + 658. doi: 10.7502/j.issn.1674-3962.2015.09.06 Lin Xin, Huang Weidong. High performance metal additive manufacturing technology applied in aviation field[J]. Materials China, 2015, 34(9): 684 − 688 + 658. doi: 10.7502/j.issn.1674-3962.2015.09.06
[11] Yang X, Liu J, Wang Z, et al. Microstructure and mechanical properties of wire and arc additive manufactured AZ31 magnesium alloy using cold metal transfer process[J]. Materials Science and Engineering: A, 2020, 774: 138942. doi: 10.1016/j.msea.2020.138942
[12] Zhou Y, Lin X, Kang N, et al. Influence of travel speed on microstructure and mechanical properties of wire + arc additively manufactured 2219 aluminum alloy[J]. Journal of Materials Science & Technology, 2020, 37: 143 − 153.
[13] 赵昀, 卢振洋, 陈树君, 等. 薄壁结构冷金属过渡增材制造工艺优化[J]. 西安交通大学学报, 2019, 53(8): 82 − 89. doi: 10.7652/xjtuxb201908011 Zhao Yun, Lu Zhenyang, Chen Shujun, et al. Optimization of manufacturing process for thin-walled structures based on cold metal transfer[J]. Journal of Xi’an Jiaotong University, 2019, 53(8): 82 − 89. doi: 10.7652/xjtuxb201908011
[14] Li Fang, Chen Shujun, Shi Junbiao, et al. Evaluation and optimization of a hybrid manufacturing process combining wire arc additive manufacturing with milling for the fabrication of stiffened panels: 12[J]. Applied Sciences, 2017, 7(12): 1233. doi: 10.3390/app7121233
[15] Zhao Yun, Li Fang, Chen Shujun, et al. Unit block–based process planning strategy of waam for complex shell–shaped component[J]. The International Journal of Advanced Manufacturing Technology, 2019, 104(9): 3915 − 3927.
[16] Xiong Jun, Zhang Guangjun. Adaptive control of deposited height in gmaw-based layer additive manufacturing[J]. Journal of Materials Processing Technology, 2014, 214(4): 962 − 968. doi: 10.1016/j.jmatprotec.2013.11.014
[17] Chen Shujun, Zhang Suolai, Huang Ning, et al. Droplet transfer in arcing-wire gtaw[J]. Journal of Manufacturing Processes, 2016, 23: 149 − 156. doi: 10.1016/j.jmapro.2016.05.014
[18] Lin J J, Lü Y H, Liu Y X, et al. Microstructural evolution and mechanical properties of Ti-6Al-4V wall deposited by pulsed plasma arc additive manufacturing[J]. Materials & Design, 2016, 102: 30 − 40.
[19] 马驰, 刘永红, 纪仁杰, 等. 电弧增材制造综述: 技术流派与展望[J]. 电加工与模具, 2020(4): 369 − 374. Ma Chi, Liu Yonghong, Ji Renjie, Li Changlong, et al. Review of wire and arc additive manufacturing: Technology genre and prospect[J]. Electromachining & Mould, 2020(4): 369 − 374.
[20] Zhao Yun, Li Fang, Chen Shujun, et al. Direct fabrication of inclined thin-walled parts by exploiting inherent overhanging capability of CMT process[J]. Rapid Prototyping Journal, 2019, 26(3): 499 − 508. doi: 10.1108/RPJ-03-2019-0081
[21] 陈和, 唐君才, 魏占静, 等. Tri-Arc与Tandem双丝电弧焊焊接工艺特性的对比[J]. 电焊机, 2021, 51(10): 102 − 106,156. doi: 10.7512/j.issn.1001-2303.2021.10.17 Chen He, Tang Juncai, Wei Zhanjing, et al. Comparison of welding process characteristics between Tri-Arc and Tandem dual wire arc welding[J]. Electric Welding Machine, 2021, 51(10): 102 − 106,156. doi: 10.7512/j.issn.1001-2303.2021.10.17
[22] Hiroshi Arita, Tomokazu Morimoto, Shigeo Nagaoka, et al. Development of advanced 3-electrode mag high-speed horizontal fillet welding process[J]. Welding in the World, 2009, 53(5): 35 − 43.
[23] 林航. 船用钢三丝高速GMAW焊焊接工艺研究[D]. 上海: 上海交通大学, 2009. Lin Harrison. Research of triple-wire high speed GMAW welding technology on ship plates[D]. Shanghai: Shanghai Jiao Tong University, 2009.
[24] 宋永伦. 高性能焊接电弧的研究与应用[J]. 电焊机, 2013, 43(3): 1 − 5. doi: 10.7512/j.issn.1001-2303.2013.03.01 Song Yonglun. Research and development of high performance welding arc[J]. Electric Welding Machine, 2013, 43(3): 1 − 5. doi: 10.7512/j.issn.1001-2303.2013.03.01
[25] 杨涛, 张生虎, 高洪明, 等. TIG-MIG复合焊电弧特性机理分析[J]. 焊接学报, 2012, 33(7): 25 − 28,60. Yang Tao, Zhang Shenghu, Gao Hongming, et al. Analysis of mechanism for TIG-MIG hybrid arc properties[J]. Transactions of the China Welding Institution, 2012, 33(7): 25 − 28,60.
[26] 刘焜. 双丝电弧增材制造铜铝合金的组织与性能[D]. 温州: 温州大学, 2021. Liu Kun. Microstructure and mechanical properties of Cu-Al alloy fabricated by dual wire additive manufacturing[D]. Wenzhou : Wenzhou University, 2021.
[27] 朱明. 双丝旁路耦合电弧高效GMAW过程稳定性与熔滴过渡行为的控制研究[D]. 兰州: 兰州理工大学, 2014. Zhu Ming. The research on controlling of stability and metal transfer behavior inconsumable DE-GMAW process[D]. Lanzhou: Lanzhou University of Technology, 2014.
[28] 耿正, 魏占静, 韩雪飞, 等. 高熔敷率低热输入的Tri-arc双丝电弧焊接方法[J]. 金属加工(热加工), 2014(22): 36 − 39,42. Gen Zheng, Wei Zhanjing, Han Xuefei, et al. Tri-Arc double-wire arc welding method with high deposition rate and low heat input[J]. MW Metal Forming, 2014(22): 36 − 39,42.
[29] 陈树君, 张亮, 门广强, 等. 多电极耦合电弧形态研究[J]. 北京工业大学学报, 2015, 41(11): 1705 − 1710. doi: 10.11936/bjutxb2015070054 Chen Shujun, Zhang Liang, Men Guangqiang, et al. Study on behavior of multi-electrode coupled arc[J]. Journal of Beijing University of Technology, 2015, 41(11): 1705 − 1710. doi: 10.11936/bjutxb2015070054
[30] 董善文. 斜交耦合电弧焊电弧特性及熔滴过渡行为研究[D]. 北京: 北京工业大学, 2020. Dong Shanwen. Study on arc characteristics and droplet transfer behavior of skew-coupling arc welding[D]. Beijing : Beijing University of Technology, 2020.
[31] Dong S, Jiang F, Xu B, et al. Influence of polarity arrangement of inter-wire arc on droplet transfer in cross-coupling arc welding[J]. Materials, 2019, 12(23): 3985. doi: 10.3390/ma12233985
[32] Lu Z, Dong S, Jiang F, et al. Analysis of electrical characteristics of inter-wire arc in cross-coupling arc[J]. Chinese Journal of Mechanical Engineering, 2019, 32(1): 22 − 31. doi: 10.1186/s10033-019-0340-z
[33] 卢振洋, 刘峰, 蒋凡, 等. 电弧热丝变极性等离子弧增材制造铝合金成型尺寸预测[J]. 稀有金属材料与工程, 2019, 48(2): 524 − 530. doi: 10.12442/j.issn.1002-185X.20180465 Lu Zhenyang, Liu Feng, Jiang Fan, et al. Forming size prediction of additive manufacturing Al alloy by arc-heated wire VPPA welding[J]. Rare Metal Materials and Engineering, 2019, 48(2): 524 − 530. doi: 10.12442/j.issn.1002-185X.20180465
[34] 黄健康, 杨茂鸿, 李挺, 等. 旁路耦合微束等离子弧增材制造[J]. 上海交通大学学报, 2016, 50(12): 1906 − 1909,1914. Huang Jiankang, Yang Maohong, Li Ting, et al. Additive manufacturing by double electrode micro-plasma arc welding[J]. Journal of Shanghai Jiao Tong University, 2016, 50(12): 1906 − 1909,1914.
[35] 樊丁, 李楠, 黄健康, 等. 旁路耦合微束等离子弧增材制造自适应高度控制系统[J]. 焊接学报, 2019, 40(11): 1 − 7. doi: 10.12073/j.hjxb.2019400279 Fan Ding, Li Nan, Huang Jiankang, et al. Double electrode micro plasma are additive manufacturing control system based on adaptive height adjustment[J]. Transactions of the China Welding Institution, 2019, 40(11): 1 − 7. doi: 10.12073/j.hjxb.2019400279
[36] 罗震, 张禹, 贾鹏. Ti-6Al-4V钛合金微束等离子弧堆焊增材制造工艺研究[J]. 焊接, 2016(4): 13 − 16. doi: 10.3969/j.issn.1001-1382.2016.04.004 Luo Zhen, Zhang Yu, Jia Peng. Additive manufacturing of Ti-6Al-4V titanium alloy parts based on micro-plasma arc surfacing[J]. Welding & Joining, 2016(4): 13 − 16. doi: 10.3969/j.issn.1001-1382.2016.04.004
[37] Luo Jinle, Chen Xizhang, Vladislav B Deev, et al. Powder plasma arc additive manufacturing of (AlTi)2x(CoCrNi)100–2x medium-entropy alloys: microstructure evolution and mechanical properties[J]. Journal of Alloys and Compounds, 2024, 970: 172474. doi: 10.1016/j.jallcom.2023.172474
[38] Wang Yanhu, Sergey Konovalov, Chen Xizhang, et al. Research on plasma arc additive manufacturing of inconel 625 Ni–Cu functionally graded materials[J]. Materials Science and Engineering: A, 2022, 853: 143796. doi: 10.1016/j.msea.2022.143796
[39] Liu Wenqiang, Jia Chuanbao, Guo Meng, et al. Compulsively constricted waam with arc plasma and droplets ejected from a narrow space[J]. Additive Manufacturing, 2019, 27: 109 − 117. doi: 10.1016/j.addma.2019.03.003
[40] Qi Zewu, Cong Baoqiang, Qi Bojin, et al. Properties of wire + arc additively manufactured 2024 aluminum alloy with different solution treatment temperature[J]. Materials Letters, 2018, 230: 275 − 278. doi: 10.1016/j.matlet.2018.07.144
[41] Qi Zewu, Qi Bojin, Cong Baoqiang, et al. Microstructure and mechanical properties of wire + arc additively manufactured 2024 aluminum alloy components: as-deposited and post heat-treated[J]. Journal of Manufacturing Processes, 2019, 40: 27 − 36. doi: 10.1016/j.jmapro.2019.03.003
[42] Cao Qianhui, Qi Bojin, Zeng Caiyou, et al. Achieving equiaxed microstructure and isotropic mechanical properties of additively manufactured AZ31 magnesium alloy via ultrasonic frequency pulsed arc[J]. Journal of Alloys and Compounds, 2022, 909: 164742. doi: 10.1016/j.jallcom.2022.164742
[43] Wang Xiaowei, Yang Dongqing, Huang Yong, et al. Microstructure and mechanical properties of as-deposited and heat-treated 18Ni (350) maraging steel fabricated by gas metal arc-based wire and arc additive manufacturing[J]. Journal of Materials Engineering and Performance, 2021, 30(9): 6972 − 6981. doi: 10.1007/s11665-021-06102-7
[44] 陈树君, 贾亚洲, 肖珺, 等. 脉冲激光驱动的GMAW短路过渡行为控制[J]. 焊接学报, 2018, 39(9): 1 − 5. Chen Shujun, Jia Yazhou, Xiao Jun, et al. Pulsed laser controlled short-circuiting metal transfer in GMAW[J]. Transactions of the China Welding Institution, 2018, 39(9): 1 − 5.
[45] 王立伟, 陈树君, 肖珺, 等. 熔滴主动靶向的激光间接电弧复合增材制造技术初探[J]. 焊接学报, 2017, 38(3): 71 − 74. Wang Liwei, Chen Shujun, Xiao Jun, et al. Droplet-targeting laser hybrid indirect are for additive manufacturing technology-A preliminary study[J]. Transactions of the China Welding Institution, 2017, 38(3): 71 − 74.
[46] Shen Q, Kong X, Chen X. Fabrication of bulk Al-Co-Cr-Fe-Ni high-entropy alloy using combined cable wire arc additive manufacturing (CCW-AAM): microstructure and mechanical properties[J]. Journal of Materials Science & Technology, 2021, 74: 136 − 142.
[47] 王立伟. 激光辅助交流双丝间接电弧熔滴过渡行为与机理[D]. 北京: 北京工业大学, 2017. Wang Liwei. Behavior and mechanism of laser assisted metal transfer in alternating current inter-wire indirect arc[D]. Beijing: Beijing University of Technology, 2020.
[48] 肖珺, 王立伟, 陈树君, 等. 工艺参数对交流双丝间接电弧弧长和熔滴尺寸的影响[J]. 焊接, 2016(4): 46 − 49. doi: 10.3969/j.issn.1001-1382.2016.04.011 Xiao Jun, Wang Liwei, Chen Shujun, et al. Effect of welding parameters on AC twin-wire indirect are length and droplet size[J]. Welding & Joining, 2016(4): 46 − 49. doi: 10.3969/j.issn.1001-1382.2016.04.011
[49] 陈彦宾, 苗玉刚, 李俐群, 等. 铝合金激光-钨极氩弧双面焊的焊接特性[J]. 中国激光, 2007(12): 1716 − 1720. doi: 10.3321/j.issn:0258-7025.2007.12.023 Chen Yanbin, Miao Yugang, Li Liqun, et al. Characteristies of laser TIG double side welding for aluminum alloys[J]. Chinese Journal of Lasers, 2007(12): 1716 − 1720. doi: 10.3321/j.issn:0258-7025.2007.12.023
[50] 雷正龙, 陈彦宾, 李颖, 等. 铝合金CO2激光-TIG电弧复合焊接试验研究[J]. 航天制造技术, 2012(4): 35 − 37. Lei Zhenglong, Chen Yanbin, Li Ying, et al. Experimental study on CO2-laser-TIG arc hybrid welding of Al alloy[J]. Aerospace Manufacturing Technology, 2012(4): 35 − 37.
[51] 孙清洁. 超声−TIG电弧复合焊接方法及电弧行为研究[D]. 哈尔滨: 哈尔滨工业大学, 2010. Sun Qingjie. Research on ultrasonic-arc behaviors and ultrasonic assisted TIG welding method[D]. Harbin : Harbin Institute of Technology, 2010.
[52] 范成磊, 陈琪昊, 林三宝, 等. 超声在电弧焊接中的应用[J]. 精密成形工程, 2018, 10(1): 57 − 66. doi: 10.3969/j.issn.1674-6457.2018.01.007 Fan Chenglei, Chen Qihao, Lin Sanbao, et al. Application of ultrasonic in arc welding[J]. Journal of Netshape Forming Engineering, 2018, 10(1): 57 − 66. doi: 10.3969/j.issn.1674-6457.2018.01.007
[53] Chen C, Fan C, Lin S, et al. Influence of pulsed ultrasound on short transfer behaviors in gas metal arc welding[J]. Journal of Materials Processing Technology, 2019, 267: 376 − 383. doi: 10.1016/j.jmatprotec.2018.12.033
[54] Chen C, Fan C, Liu Z, et al. Microstructure evolutions and properties of Al–Cu alloy joint in the pulsed power ultrasonic-assisted GMAW[J]. Acta Metallurgica Sinica (English Letters), 2020, 33(10): 1397 − 1406. doi: 10.1007/s40195-020-01066-4
[55] 陈树君, 张晓亮, 华爱兵, 等. 旋转磁场作用下的MAG焊电弧运动特征[J]. 电焊机, 2009, 39(6): 1 − 4. doi: 10.3969/j.issn.1001-2303.2009.06.001 Chen Shujun, Zhang Xiaoliang, Hua Aibing, et al. Study on are movement characteristies of MAG welding in a rotating magnetic field[J]. Electrie Welding Machine, 2009, 39(6): 1 − 4. doi: 10.3969/j.issn.1001-2303.2009.06.001
[56] Wang Y, Chen X, Shen Q, et al. Effect of magnetic field on the microstructure and mechanical properties of inconel 625 superalloy fabricated by wire arc additive manufacturing[J]. Journal of Manufacturing Processes, 2021, 64: 10 − 19. doi: 10.1016/j.jmapro.2021.01.008
[57] 王扬帆. CMT电弧增材制造Inconel 625合金组织和性能研究[D]. 温州: 温州大学, 2020. Wang Yangfan. Research on Inconel 625 alloy microstructure and properties using wire arc additive manufacturing based on the cold metal transfer(CMT) [D]. Wenzhou: Wenzhou University, 2020.
[58] 刘健, 贺智涛, 牛继承. 船用高强钢厚板窄间隙热丝TIG焊接工艺研究[J]. 材料开发与应用, 2018, 33(3): 10 − 15. Liu Jian, He Zhitao, Niu Jicheng. Research on narrow gap hot wire TIG welding of thick plate of high strength steel for shipbuilding[J]. Development and Application of Materials, 2018, 33(3): 10 − 15.
[59] 陈树君, 苑城玮, 蒋凡, 等. 电阻加热金属丝材熔滴过渡的产热机制与熔化行为研究[J]. 金属学报, 2018, 54(9): 1297 − 1310. doi: 10.11900/0412.1961.2018.00035 Chen Shujun, Yuan Chengwei, Jiang Fan, et al. Study on heat generation mechanism and melting behavior of droplet transition in resistive heating metal wires[J]. Acta Metallurgical Sinica, 2018, 54(9): 1297 − 1310. doi: 10.11900/0412.1961.2018.00035
[60] 苑城玮, 陈树君, 蒋凡, 等. 电阻加热金属丝材塑性变形电−热−机械响应分析[J]. 焊接学报, 2020, 41(12): 1 − 6. doi: 10.12073/j.hjxb.20200921001 Yuan Chengwei, Chen Shujun, Jiang Fan, et al. Electro-thermal-mechanical response analysis of plastic deformation of resistance heating metal wire[J]. Transactions of the China Welding Institution, 2020, 41(12): 1 − 6. doi: 10.12073/j.hjxb.20200921001
[61] 王猛. 增材制造直接分层和路径规划技术研究[J]. 机械工程与自动化, 2018(6): 34 − 35,38. doi: 10.3969/j.issn.1672-6413.2018.06.012 Wang Meng. Research on direct slicing and path planning technology of additive manufacturing[J]. Mechanical Engineering & Automation, 2018(6): 34 − 35,38. doi: 10.3969/j.issn.1672-6413.2018.06.012
[62] 李冉. 电弧增材制造分层算法与路径规划方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2018. Li Ran. Research on slicing algorithm and path planning method of wire and arc additive manufacturing[D]. Harbin : Harbin Institute of Technology, 2018.
[63] 卜星. 基于机器人的模具电弧增材再制造路径规划及工艺[D]. 南京: 南京航空航天大学, 2018. Bu Xing. Path planning and process design of wire and arc additive remanufacturing using industrial robot for mould repairing[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2018.
[64] Shi Junbiao, Li Fang, Chen Shujun, et al. Effect of in-process active cooling on forming quality and efficiency of tandem gmaw–based additive manufacturing[J]. The International Journal of Advanced Manufacturing Technology, 2019, 101(5): 1349 − 1356.
[65] 赵昀. CMT冷金属过渡铝合金增材制造研究[D]. 北京: 北京工业大学, 2017. Zhao Yun. Investigation of additive manufacture of aluminum parts based on CMT[D]. Beijing: Beijing University of Technology, 2017.
[66] 朱忠良, 赵凯, 郭立杰, 等. 大型金属构件增材制造技术在航空航天制造中的应用及其发展趋势[J]. 电焊机, 2020, 50(1): 1 − 14. doi: 10.7512/j.issn.1001-2303.2020.01.01 Zhu Zhongliang, Zhao Kai, Guo Lijie, et al. Application and development trend of additive manufacturing technology of large-scale metal component in aerospace manufacturing[J]. Electric Welding Machine, 2020, 50(1): 1 − 14. doi: 10.7512/j.issn.1001-2303.2020.01.01
[67] 梁少兵, 王凯, 丁东红, 等. 电弧增材制造路径工艺规划的研究现状与发展[J]. 精密成形工程, 2020, 12(4): 86 − 93. doi: 10.3969/j.issn.1674-6457.2020.04.009 Liang Shaobing, Wang Kai, Ding Donghong, et al. Research status and development of wire arc additive manufacturing path planning[J]. Journal of Netshape Forming Engineering, 2020, 12(4): 86 − 93. doi: 10.3969/j.issn.1674-6457.2020.04.009
[68] 侯文彬, 夏明栋, 徐金亭. 增材制造中复杂区域的分割填充扫描算法[J]. 计算机集成制造系统, 2017, 23(9): 1853 − 1859. Hou Wenbin, Xia Mingdong, Xu Jinting. Region segmentation and scanning algorithm in additive manufacturing[J]. Computer Integrated Manufacturing Systems, 2017, 23(9): 1853 − 1859.
[69] 王德鹏. 3D打印分层与路径规划算法的研究与应用[D]. 合肥: 合肥工业大学, 2019. Wang Depeng. Research and application of 3D printing layering and path planning algorithms[D]. Hefei: Hefei University of Technology, 2019.
[70] 王占礼, 晁艳艳, 胡艳娟, 等. 改进的Hilbert曲线在FDM路径规划中的应用[J]. 机械设计与制造, 2016(3): 186 − 188,192. doi: 10.3969/j.issn.1001-3997.2016.03.051 Wang Zhanli, Chao Yanyan, Hu Yanjuan, et al. Application of improved Hilbert curve in the path planning of FDM[J]. Machinery Design & Manufacture, 2016(3): 186 − 188,192. doi: 10.3969/j.issn.1001-3997.2016.03.051
[71] 欧立松. 面向三维打印的几何模型后处理技术研究[D]. 南京: 南京航空航天大学, 2015. Ou Lisong. Research on post-processing technology of geometric model for 3D printing[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2015.
[72] Zhao Yun, Jia Yazhou, Chen Shujun, et al. Process planning strategy for wire-arc additive manufacturing: thermal behavior considerations[J]. Additive Manufacturing, 2020, 32: 100935. doi: 10.1016/j.addma.2019.100935
[73] Shi Junbiao, Li Fang, Chen Shujun, et al. T-GMAW based novel multi-node trajectory planning for fabricating grid stiffened panels: an efficient production technology[J]. Journal of Cleaner Production, 2019, 238: 117919. doi: 10.1016/j.jclepro.2019.117919
[74] 李鑫磊, 张广军. 电弧增材制造中空间曲面等距路径规划算法[J]. 焊接学报, 2021, 42(7): 14 − 20,98. doi: 10.12073/j.hjxb.20201126001 Li Xinlei, Zhang Guangjun. Research on space equidistant path planning algorithm of complex curved surface for are additive manufacturing[J]. Transactions of the China Welding Institution, 2021, 42(7): 14 − 20,98. doi: 10.12073/j.hjxb.20201126001
[75] 牛其华. 基于体素的电弧增材制造曲面分层及路径规划方法研究[D]. 武汉: 华中科技大学, 2019. Niu Qihua. Research of curved layer and path planning method based on voxel for wire arc additive manufacturing[D]. Wuhan: Huazhong University of Science & Technology, 2019.
下载:
计量
- 文章访问数: 142
- HTML全文浏览量: 40
- PDF下载量: 73
