A model of bead size based on the dynamic response of CMT-based wire and arc additive manufacturing process parameters

材料科学 有孔小珠 沉积(地质) 实验设计 响应面法 工艺优化 成形性 过程(计算) 熔融沉积模型 生物系统 机械工程 复合材料 计算机科学 3D打印 工程类 数学 环境工程 统计 生物 操作系统 机器学习 古生物学 沉积物
作者
Xuewei Fang,Chuanqi Ren,Lijuan Zhang,Changxing Wang,Ke Huang,Bingheng Lu
出处
期刊:Rapid Prototyping Journal [Emerald Publishing Limited]
卷期号:27 (4): 741-753 被引量:16
标识
DOI:10.1108/rpj-03-2020-0051
摘要

Purpose This paper aims at fabricating large metallic components with high deposition rates, low equipment costs through wire and wire and arc additive manufacturing (WAAM) method, in order to achieve the morphology and mechanical properties of manufacturing process, a bead morphology prediction model with high precision for ideal deposition of every pass was established. Design/methodology/approach The dynamic response of the process parameters on the bead width and bead height of cold metal transfer (CMT)-based AM was analyzed. A laser profile scanner was used to continuously capture the morphology variation. A prediction model of the deposition bead morphology was established using response surface optimization. Moreover, the validity of the model was examined using 15 groups of quadratic regression analyzes. Findings The relative errors of the predicted bead width and height were all less than 5% compared with the experimental measurements. The model was then preliminarily used with necessary modifications, such as further considering the interlayer process parameters, to guide the fabrication of complex three-dimensional components. Originality/value The morphology prediction of WAAMed bead is a critical issue. Most research has focused on the formability and defects in CMT-based WAAM and little research on the effect of process parameters on the morphology of the deposited layer in CMT-based WAAM has been conducted. To test the sensitivities of the processing parameters to bead size, the dynamic response of key parameters was investigated. A regression model was established to guide the process parameter optimization for subsequent multi-layer or component deposition.

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