基于机器学习的混凝土<strong>3D</strong>打印沉积线状态优化

doi:10.3785/j.issn.1006-754X.2025.04.183

工程设计学报

2025, Vol. 32

Issue (4): 488-498 DOI: 10.3785/j.issn.1006-754X.2025.04.183

优化设计

基于机器学习的混凝土3D打印沉积线状态优化

马宗方¹(

),戴溢阳¹,贺静¹(

),宋琳¹,刘超²

^1.西安建筑科技大学信息与控制工程学院，陕西西安 710055
^2.西安建筑科技大学土木工程学院，陕西西安 710055

Machine learning-based optimization of deposition line state in concrete 3D printing

Zongfang MA¹(

),Yiyang DAI¹,Jing HE¹(

),Lin SONG¹,Chao LIU²

^1.College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
^2.College of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China

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摘要：

混凝土3D打印作为一种快速、精确的增材制造技术，逐渐在建筑行业中展现出独特优势。然而，混凝土3D打印涉及多种工艺参数（如打印速度、挤出速度、层高和线间距离等），且这些参数之间存在复杂的耦合关系，导致沉积线状态的质量难以精确控制。为解决这一问题，提出了一种基于白鲸优化（beluga whale optimization, BWO）算法、支持向量机（support vector machine, SVM）和集成学习算法AdaBoost的机器学习模型（BWO-SVM-AdaBoost），用于探究混凝土3D打印工艺参数与沉积线状态之间的关系。BWO-SVM-AdaBoost模型以包含400个样本的数据集为基础进行训练与测试，其在训练集和测试集上的预测精度分别达到了99.76%和95.19%。与此同时，利用贝叶斯优化算法对不佳的打印工艺参数进行进一步优化，生成了最优的参数组合，有效地提升了打印构件的精度和稳定性。研究结果为混凝土3D打印工艺参数的系统优化提供了一种新颖且有效的方法，可为实现高质量的打印结构奠定理论基础。

关键词： 混凝土3D打印; 沉积线; 机器学习; 贝叶斯优化

Abstract:

Concrete 3D printing, as a rapid and precise additive manufacturing technology, is gradually demonstrating unique advantages in the construction industry. However, concrete 3D printing involves a variety of process parameters (such as printing speed, extrusion speed, layer height and line spacing), and there are complex coupling relationships among these parameters, making it difficult to precisely control the quality of the deposition line state. To address this problem, a machine learning model (BWO-SVM-AdaBoost) based on the beluga whale optimization (BWO) algorithm, support vector machine (SVM) and ensemble learning algorithm AdaBoost was proposed to investigate the relationship between the process parameters of concrete 3D printing and the deposition line state. The BWO-SVM-AdaBoost model was trained and tested based on a dataset consisting of 400 samples, and its prediction accuracy on the training set and the test set reached 99.76% and 95.19%, respectively. Furthermore, the Bayesian optimization algorithm was applied to refine suboptimal printing process parameters and the optimal combination of parameters was generated, thereby effectively enhancing the precision and stability of printed components. The research results provide a novel and effective approach for the systematic optimization of concrete 3D printing process parameters, which can lay a theoretical foundation for achieving high-quality printed structures.

Key words: concrete 3D printing deposition line machine learning Bayesian optimization

收稿日期: 2024-12-06 出版日期: 2025-09-01

CLC:

TP3-05

基金资助: 国家自然科学基金面上项目(62276207)

通讯作者: 贺静 E-mail: mazf@xauat.edu.cn;hejing0811@xauat.edu.cn

作者简介: 马宗方（1980—），男，教授，博士，从事信息融合、机器学习和智慧城市等研究，E-mail: mazf@xauat.edu.cn

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引用本文:

马宗方,戴溢阳,贺静,宋琳,刘超. 基于机器学习的混凝土3D打印沉积线状态优化[J]. 工程设计学报, 2025, 32(4): 488-498.

Zongfang MA,Yiyang DAI,Jing HE,Lin SONG,Chao LIU. Machine learning-based optimization of deposition line state in concrete 3D printing[J]. Chinese Journal of Engineering Design, 2025, 32(4): 488-498.

链接本文:

https://www.zjujournals.com/gcsjxb/CN/10.3785/j.issn.1006-754X.2025.04.183 或 https://www.zjujournals.com/gcsjxb/CN/Y2025/V32/I4/488

图1 混凝土3D打印挤出过程

图2 混凝土3D打印的分层与成形过程示意

图3 混凝土3D打印沉积线状态优化总体框架

表1 混凝土3D打印工艺参数取值

图4 双线构件沉积线状态观测示意及实验平台

图5 沉积线状态分类示意

表2 混凝土3D打印实验数据（部分）

图6 BWO-SVM-AdaBoost模型的构建流程

图7 SVM分类的超平面示意图

图8 基于AdaBoost的分类器训练过程示意

图9 基于贝叶斯优化的混凝土3D打印工艺参数优化流程

表3 3D打印沉积线各状态类别的数量

表4 不同机器学习模型的分类预测性能比较

图10 BWO-SVM-AdaBoost模型的分类预测结果

图11 基于贝叶斯优化的沉积线状态优化结果

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