Geometry Character and Prediction of Deposition Layers for Thin-walled Parts in GMA-based Additive Manufacturing

CHENG Sheng; LIU Zhengyong; XIONG Jun; WANG Tianshi; CHEN Shuai

doi:10.7512/j.issn.1001-2303.2024.08.09

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Geometry Character and Prediction of Deposition Layers for Thin-walled Parts in GMA-based Additive Manufacturing
Vol. 54, Issue 8, Pages: 71-77(2024)
Received：31 May 2024，

Published：25 August 2024
DOI： 10.7512/j.issn.1001-2303.2024.08.09
稿件说明：

移动端阅览

程圣，刘正勇，熊俊，等.薄壁构件GMA增材制造成形特性与尺寸预测［J］.电焊机，2024，54（8）：71-77. DOI： 10.7512/j.issn.1001-2303.2024.08.09.

CHENG Sheng, LIU Zhengyong, XIONG Jun, et al.Geometry Character and Prediction of Deposition Layers for Thin-walled Parts in GMA-based Additive Manufacturing[J].Electric Welding Machine, 2024, 54(8): 71-77. DOI： 10.7512/j.issn.1001-2303.2024.08.09.

摘要

采用熔化极气体保护电弧（Gas metal arc，GMA）作为热源，以H08Mn2Si焊丝作为填充材料，开展了多层单道薄壁构件堆积层尺寸特征研究。借助金相显微镜测量了堆积层尺寸，分析了堆积层尺寸特性并阐明其成形机制。结果表明，堆积层尺寸在前四层处于不稳定状态，波动较大。随堆积层数的增加，堆积层层高逐渐减小并趋于稳定，堆积层层宽先减小，随后逐渐增大并趋于稳定，层宽在第二个堆积层具有极小值。进一步设计了二次回归旋转组合试验方法，采集的试验数据作为训练样本，基于神经网络算法建立了堆积工艺参数（堆积电流、行走速度、堆积电压）与堆积层尺寸的非线性模型，经测试数据样本验证表明，模型预测精度较高，堆积层尺寸预测最大相对误差小于6.98%。根据堆积层尺寸预测模型，进行了封闭路径与非封闭路径薄壁构件的堆积成形，试验结果表明，该模型能够应用于薄壁构件GMA增材制造自适应分层切片过程。

Abstract

Geometry character of deposition layers for thin-walled parts in GMA-based additive manufacturing is researched using GMA as the heat source and H08Mn2Si wire as the additive material. Layer geometries are measured by an optical microscope. Geometry characters of deposition layers are analyzed

and its forming mechanism is also illustrated. The results show that the layer geometries in the previous four layers are unstable and have a larger fluctuation. With the increase of the number of deposition layers

the layer height decreases and then reaches a stable value. With the increase of the number of deposition layers

the layer width firstly decreases and then gradually increased to a stable value

and the layer width has a minimum value in the second layer. A series of experiments are carried out for collecting the input-output data by applying a central composite rotatable design. Nonlinear relations between deposition current

travel speed

arc voltage and layer geometry are developed based on neural network algorithms. Testing data show that the neural network model has a great prediction capability

and maximum relative errors between predicted and measured values are no more than 6.98%. According to the model

closed and open path thin-walled parts are fabricated

demonstrating the effectiveness of the model employed in the adaptive slicing process for thin-walled components.

关键词

Keywords

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