High-dimensional Global Parameter Optimization Design Methodology for Engineering Boom Systems

doi:10.3969/j.issn.1004-132X.2026.09.012

Abstract

Abstract:

A high-dimensional global parameter optimization method was proposed for engineering boom systems to address unstable parametric geometry， the trade-off between simulation accuracy and efficiency， and accuracy loss in high-dimensional surrogate modeling. Adaptive parametric geometry modeling expanded the design space； system-level response reconstruction improved finite element efficiency while retaining accuracy； and the surrogate information transfer-based optimization（SITFO） algorithm integrated radial basis function（RBF） and Kriging models. For a 59 m concrete pump truck boom system， the optimized design reduces mass by 18.46% and maximum deformation by 10.6%， demonstrating the method's applicability to high-dimensional mechanical-system optimization.

Key words: boom system, parametric modeling, surrogate model, structural optimization

摘要：

针对参数化几何模型稳定性不足、仿真精度与效率难以兼顾以及高维代理模型精度损失等问题，提出面向工程臂架系统的高维全局参数优化方法。该方法以自适应参数化几何建模拓展设计空间，以系统级响应重构兼顾有限元计算精度与效率，并通过代理信息迁移融合径向基函数（RBF）与Kriging模型，构建SITFO代理辅助优化算法。以59 m混凝土泵车臂架系统为例，优化后质量减小18.46%，最大变形量减小10.6%，验证了该方法对高维机械系统优化的适用性。

关键词: 臂架系统, 参数化建模, 代理模型, 结构优化

CLC Number:

TH122

LI Mengcong, LI Peng, GAO Rongzhi, PANG Yong, GUO Zhenggang, SONG Xueguan, KAN Ziyun. High-dimensional Global Parameter Optimization Design Methodology for Engineering Boom Systems[J]. China Mechanical Engineering, 2026, 37(9): 2194-2204.

李梦聪, 李鹏, 高荣芝, 庞勇, 郭正刚, 宋学官, 阚子云. 面向工程臂架系统的高维全局参数优化设计方法[J]. 中国机械工程, 2026, 37(9): 2194-2204.

Figures/Tables 20

Fig.1 Common structures of multi-section boom systems in engineering

Fig.2 Approaches and drawbacks of implicit parametric modeling

Fig.3 Process of adaptive parametric modeling method

Fig.4 Effect of adaptive parametric geometry modeling under the same conditions

Fig.5 Principle and process of system-level response reconstruction method

Fig.6 Effect of system parameters on response reconstruction accuracy

Fig.7 Effect of component parameters on response reconstruction accuracy

Fig.8 Dynamic component auto-localization and modeling method

Fig.9 Principle and process of SITFO algorithm

Fig.10 Main parameters of boom sections in concrete pump truck boom system

Tab.1 Typical operating conditions of a 6-section boom concrete pump truck system

工况序号	工况内容
1	臂节1~5垂直，臂节6从0°~90°
2	臂节1~5展开从0°~89°，臂节6右水平
3	臂节1~4展开垂直，臂节5~6直展开，0°~90°
4	臂节1~4展开0°~90°，臂节5~6水平
5	臂节1~2从0°~90°，臂节3~6右水平
6	臂节1从0°~90°，臂节2~6右水平

Fig.11 Operating condition sweep analysis based on deformation and stress

Fig.12 Static analysis results of the 59 m concrete pump truck boom system

Fig.13 Effect of mesh density in finite element model on computational results

Fig.14 Convergence analysis of mesh density in finite element model

Fig.15 Experimental testing site of the 59 m concrete pump truck boom system

Tab.2 Detailed setting of design variables for boom system

尺寸参数（第i臂节，i=1，2，3，4，5，6）	长度	宽度	高度
尺寸参数（第i臂节，i=1，2，3，4，5，6）	L（i）	W（i）	H（i）
板厚参数	区域1	区域2	区域3
顶板厚度	T（i_1）	T（i_2）	T（i_3）
侧板厚度	T（i_4）	T（i_5）	T（i_6）
底板厚度	T（i_7）	T（i_8）	T（i_9）

Fig.16 Stress point selection method corresponding to the response decomposition strategy

Fig.17 Convergence curve of the optimal value of SITFO

Fig.18 Comparison of the performance of surrogate model-assisted optimization algorithms

References 23

[1]	MEIRINGER M， KUGI A， KEMMETMÜLLER W. Modelling and Calibration of a Five Link Elastic Boom of a Mobile Concrete Pump［J］. Mathematical and Computer Modelling of Dynamical Systems， 2023， 29（1）： 41-68.
[2]	REN Wu， LI Zhongwei， BI Yanping， et al. Modeling and Analysis of Truck Mounted Concrete Pump Boom by Virtual Prototyping［J］. Journal of Robotics， 2017， 2017（1）： 9182143.
[3]	LEE J H， KIM K S. Optimal Design of Boom for Telescopic Boom Type Forklift Truck［J］. Journal of the Korean Society for Precision Engineering， 2020， 37（6）： 457-464.
[4]	CELIK H K， RENNIE A E W， AKINCI I. Design and Structural Optimisation of a Tractor Mounted Telescopic Boom Crane［J］. Journal of the Brazilian Society of Mechanical Sciences and Engineering， 2017， 39（3）： 909-924.
[5]	KURESANGSAI P， COLE M O T. Kinematic Modeling and Design Optimization of Flexure-jointed Planar Mechanisms Using Polynomial Bases for Flexure Curvature［J］. Mechanism and Machine Theory， 2019， 132： 80-97.
[6]	REN Yafeng， YANG Weifeng， SUN Xiaohui， et al. Vibration Characteristics of Concrete Pump Trucks with Multiple Postures and Multiple Conditions Based on the Secondary Development of HyperWorks［J］. Processes， 2023， 11（5）： 1483.
[7]	WANNER J， SAWODNY O. A Lumped Parameter Model of the Boom of a Mobile Concrete Pump［C］∥2019 18th European Control Conference （ECC）.Naples， 2019： 2808-2813.
[8]	李鹏，王一棠，王佳茜，等. 数据驱动的混凝土泵车臂架疲劳载荷谱编制［J］. 中国机械工程， 2024， 35（10）： 1881-1889.
	LI Peng， WANG Yitang， WANG Jiaqian， et al. Data-driven Fatigue Load Spectrum Compiling of Concrete Pump Truck Booms［J］. China Mechanical Engineering， 2024， 35（10）： 1881-1889.
[9]	张攀峰，成凯，张辉，等. 基于ANSYS的混凝土泵车上车结构刚度灵敏度分析及优化设计研究［J］. 建筑机械， 2012（23）： 107-111.
	ZHANG Panfeng， CHENG Kai， ZHANG Hui， et al. Study of Rigidity Sensitivity Analysis and Optimal Design Based on ANSYS for Upper Structure of Concrete Pump Truck［J］. Construction Machinery， 2012（23）： 107-111.
[10]	孙武和. 混凝土泵车臂架结构有限元分析及参数计算程序应用［D］. 长春：吉林大学， 2009.
	SUN Wuhe. Finite Element Analysis and Parameter Calculation Program Application of Concrete Pump Truck Boom Structure ［D］.Changchun： Jilin University， 2009.
[11]	孙武和，周振平，徐怀玉，等. 基于ANSYS的混凝土泵车上车结构自动计算技术［J］. 建筑机械， 2013（5）： 94-97.
	SUN Wuhe， ZHOU Zhenping， XU Huaiyu， et al. Automatically Computing Technology Base on ANSYS for Boom Structure of the Truck-mounted Concrete Pump［J］. Construction Machinery， 2013（5）： 94-97.
[12]	闫海涛，成凯，徐怀玉，等. 基于遗传算法的混凝土泵车臂架铰点系统的优化［J］. 建筑机械， 2011（8）： 84-87.
	YAN Haitao， CHENG Kai， XU Huaiyu， et al. The Optimization of the Boom Hinge Point System for the Concrete Pump Truck Based on Genetic Algorithm［J］. Construction Machinery， 2011（8）： 84-87.
[13]	陈泽，张剑敏，吴斌兴. 基于拓扑优化的混凝土泵车一臂轻量化设计［J］. 中国工程机械学报， 2019， 17（3）： 238-242.
	CHEN Ze， ZHANG Jianmin， WU Binxing. Lightweight Design of Concrete Pump Truck One-boom Based on Topological Optimization［J］. Chinese Journal of Construction Machinery， 2019， 17（3）： 238-242.
[14]	赵远，张宇乐，陈晓伟. 混凝土泵车臂架机构铰点位置优化研究［J］. 起重运输机械， 2024（7）： 38-43.
	ZHAO Yuan， ZHANG Yule， CHEN Xiaowei. On Optimization of Hinge Point Position of Boom Mechanism of Concrete Pump Truck［J］. Hoisting and Conveying Machinery， 2024（7）： 38-43.
[15]	赵远. 混凝土泵车臂架结构性能分析及多目标优化［D］. 太原：太原科技大学， 2024.
	ZHAO Yuan. Performance Analysis and Multi-objective Optimization of Concrete Pump Truck Boom Structure［D］. Taiyuan： Taiyuan University of Science and Technology， 2024.
[16]	YANG Shuanqiang， HUANG Xinlong， SHEN Zhenhui， et al. Knowledge-based Structure Optimization Design for Boom of Excavator［J］. Mathematical Problems in Engineering， 2021， 2021（1）： 8869758.
[17]	LIN Jingliang， LI Haiyan， HUANG Yunbao， et al. Multi-objective Surrogate Modeling through Transfer Learning for Telescopic Boom Forklift［J］. IEEE Access， 2023， 11： 11629-11641.
[18]	BAI Jiangbo， YOU Feiyan， WANG Zhenzhou， et al. An Efficient Multi-objective Optimization Framework for Thin-walled Tubular Deployable Composite Boom［J］. Composite Structures， 2024， 327： 117713.
[19]	李鹏，李梦聪，肖立波，等. 基于多保真度代理模型的混凝土泵车臂架销轴轻量化设计［J］. 中国机械工程， 2025， 36（4）： 821-829.
	LI Peng， LI Mengcong， XIAO Libo， et al. Lightweight Design of Concrete Pump Truck Boom Pins Based on Multi-fidelity Surrogate Model［J］. China Mechanical Engineering， 2025， 36（4）： 821-829.
[20]	SHI Guohong. BIW Architecture Multidisciplinary Light Weight Optimization Design［J］. Journal of Mechanical Engineering， 2012， 48（8）： 110.
[21]	KUDELA J， MATOUSEK R. Recent Advances and Applications of Surrogate Models for Finite Element Method Computations： a Review［J］. Soft Computing， 2022， 26（24）： 13709-13733.
[22]	何庆，利璐，李晨钟，等. 基于Kriging模型的在役高速列车悬挂参数近似贝叶斯估计［J］. 机械工程学报， 2023， 59（12）： 139-148.
	HE Qing， LI Lu， LI Chenzhong， et al. Approximate Bayesian Estimation of Suspension Parameters of In-service High-speed Trains Based on Kriging Surrogate Model［J］. Journal of Mechanical Engineering， 2023， 59（12）： 139-148.
[23]	PANG Yong， ZHANG Shuai， JIN Yaochu， et al. Surrogate Information Transfer and Fusion in High-dimensional Expensive Optimization Problems［J］. Swarm and Evolutionary Computation， 2024， 88： 101586.