Many-objective Optimization Design for TriRocker Working Mechanism of Face-shovel Hydraulic Excavator

doi:10.3901/JME.2023.03.054

Abstract

Abstract: To solve the many-objective optimization problem of the working mechanism of hydraulic excavator, this paper researched the optimization design of new TriRocker shovel attachment by many-objective evolutionary algorithms. Firstly, the constrained many-objective optimization model of TriRocker working mechanism was established by taking the kinematic characteristics, digging forces, crowding force and the standards of digging map as the objective functions. Then, a modified constrained many-objective evolutionary algorithm was proposed with adaptive rotation-based simulated binary crossover to enhance the ability of dealing with complex constrained optimization problems and verified by the benchmark test functions. Finally, the proposed algorithm was applied in the optimization example of 70 t hydraulic excavator and compared with eight advanced many-objective evolutionary algorithms for further verification. The most satisfactory solution of hydraulic excavator was selected by technique for order preference by similarity to ideal solution. The results show that the proposed algorithm is significantly superior to other compared algorithms and could obtain the optimized scheme of TriRocker hydraulic excavator with market competitiveness.

Key words: face-shovel hydraulic excavator, TriRocker working mechanism, many-objective optimization, evolutionary algorithm, constrained optimization

CLC Number:

TH122
TU621

XU Gongyue, FENG Zemin, GUO Erkuo. Many-objective Optimization Design for TriRocker Working Mechanism of Face-shovel Hydraulic Excavator[J]. Journal of Mechanical Engineering, 2023, 59(3): 54-65.

References

[1] FALCON-CARDONA J G,COELLO C A C. Indicator-based multi-objective evolutionary algorithms:A comprehensive survey[J]. ACM Computing Surveys,2020,53(2):1-35.
[2] ZHANG Le,LI Tian,ZHANG Jiye,et al. Optimization on the crosswind stability of trains using neural network surrogate model[J]. Chinese Journal of Mechanical Engineering,2021,34(1):1-17.
[3] JIANG Zhipeng,GAO Dong,LU Yong,et al. Optimization of cutting parameters for trade-off among carbon emissions,surface roughness,and processing time[J]. Chinese Journal of Mechanical Engineering,2019,32(1):1-18.
[4] 张阳,吴宝海,夏卫红,等. 变切深工况下恒定切削力约束的多目标进给量优化方法[J]. 机械工程学报,2021,57(5):242-250.
ZHANG Yang,WU Baohai,XIA Weihong,et al. Multi-objective feed optimization with constant cutting force constraints under variable cutting depth[J]. Journal of Mechanical Engineering,2021,57(5):242-250.
[5] JIN Yaochu,MIETTINEN K,ISHIBUCHI H. Guest editorial evolutionary many-objective optimization[J]. IEEE Transactions on Evolutionary Computation,2018,22(1):1-2.
[6] EIBEN A E,SMITH J. From evolutionary computation to the evolution of things[J]. Nature,2015,521:476-482.
[7] ZHU Guangyu,DING Chen,ZHANG Weibo. Optimal foraging algorithm that incorporates fuzzy relative entropy for solving many-objective permutation flow shop scheduling problems[J]. IEEE Transactions on Fuzzy Systems,2020,28(11):2738-2746.
[8] CHAMPASAK P,PANAGANT N,PHOLDEE N,et al. Self-adaptive many-objective meta-heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial vehicle[J]. Aerospace Science and Technology,2020,100:105783.
[9] CAO Bin,DONG Weinan,LÜ Zhihan,et al. Hybrid microgrid many-objective sizing optimization with fuzzy decision[J]. IEEE Transactions on Fuzzy Systems,2020,28(11):2702-2710.
[10] 蔡敢为,黄一洋,田军伟,等. 一种新型正铲液压挖掘机工作机构的研究[J]. 机械工程学报,2021,57(13):132-143.
CAI Ganwei,HUANG Yiyang,TIAN Junwei,et al. Research on a new working mechanism of face-shovel hydraulic excavator[J]. Journal of Mechanical Engineering,2021,57(13):132-143.
[11] 徐弓岳,丁华锋,孙玉玉. 基于改进非支配排序遗传算法的正铲挖掘机工作装置优化设计[J]. 机械工程学报,2016,52(21):35-43.
XU Gongyue,DING Huafeng,SUN Yuyu. Optimization of face-shovel excavator's attachment based on improved NSGA-II[J]. Journal of Mechanical Engineering,2016,52(21):35-43.
[12] YANG Shuanqiang,HUANG Xinlong,SHEN Zhenhui,et al. Knowledge-based structure optimization design for boom of excavator[J]. Mathematical Problems in Engineering,2021(1):8869758.
[13] BI Qiushi,WANG Guoqiang,WANG Yongpeng,et al. Digging trajectory optimization for cable shovel robotic excavation based on a multi-objective genetic algorithm[J]. Energies,2020,13(12):3118.
[14] 张先萌,李翔龙,王友国. 基于响应面法的反铲液压挖掘机工作装置多目标优化设计[J]. 机械强度,2019,41(3):659-665.
ZHANG Xianmeng,LI Xianglong,WANG Youguo. Multi-objective optimization design of backhoe hydraulic excavator working device based on response surface methodology[J]. Journal of Mechanical Strength,2019,41(3):659-665.
[15] ZOU Zhihong,PANG Xiaoping,CHEN Jin. Comprehensive theoretical digging performance analysis for hydraulic excavator using convex polytope method[J]. Multibody System Dynamics,2019,47(2):137-164.
[16] XU Gongyue,DING Huafeng,FENG Zemin. Optimal design of hydraulic excavator shovel attachment based on multiobjective evolutionary algorithm[J]. IEEE/ASME Transactions on Mechatronics,2019,24(2):808-819.
[17] HUANG Peng,DING Huafeng,YANG Wenjian,et al. Connectivity calculation-based automatic synthesis of planar multi-loop mechanisms[J]. Journal of Mechanisms and Robotics-Transactions of the ASME,2021,13(4):041004.
[18] 曹宇. 新型正铲挖掘机构的构型和设计方法[D]. 秦皇岛:燕山大学,2014.
CAO Yu. Structural synthesis and design of new face-shovel excavator mechanism[D]. Qinhuangdao:Yanshan University,2014.
[19] 马利. 新型正铲挖掘机的动力学分析及虚拟样机参数化研究[D]. 秦皇岛:燕山大学,2015.
MA Li. Dynamic analysis and parametric research on virtual prototype of new face-shovel excavator[D]. Qinhuangdao:Yanshan University,2015.
[20] 丁华锋,曹宇,杨真真,等. 基于D-H法的多连杆正铲挖掘机运动学分析与包络图绘制[J]. 燕山大学学报,2014,38(3):197-203.
DING Huafeng,CAO Yu,YANG Zhenzhen,et al. Position kinematics analysis of multi-linkage face-shovel excavator and envelope plotting using D-H method[J]. Journal of Yanshan University,2014,38(3):197-203.
[21] 陈国俊. 液压挖掘机[M]. 武汉:华中科技大学出版社,2011.
CHEN Guojun. Hydraulic excavator[M]. Wuhan:Huazhong University of Science and Technology Press,2011.
[22] 陈进,庞晓平. 单斗液压挖掘机工作装置关键技术研究[M]. 北京:科学出版社,2014.
CHEN Jin,PANG Xiaoping. Key technology research of single bucket hydraulic excavator's working attachment[M]. Beijing:Science Press,2014.
[23] JAIN H,DEB K. An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach,part II:Handling constraints and extending to an adaptive approach[J]. IEEE Transactions on Evolutionary Computation,2014,18(4):602-622.
[24] PAN Linqiang,XU Wenting,LI Lianghao,et al. Adaptive simulated binary crossover for rotated multi-objective optimization[J]. Swarm and Evolutionary Computation,2021,60:100759.
[25] TIAN Ye,XIANG Xiaoshu,ZHANG Xingyi,et al. Sampling reference points on the pareto fronts of benchmark multi-objective optimization problems[C]//Proceedings of the 2018 IEEE Congress on Evolutionary Computation. Rio de Janeiro,Brazil:IEEE,2018:1-6.
[26] HUBAND S,HINGSTON P,BARONE L,et al. A review of multiobjective test problems and a scalable test problem toolkit[J]. IEEE Transactions on Evolutionary Computation,2006,10(5):477-506.
[27] LIU Zhizhong,WANG Yong. Handling Constrained multiobjective optimization problems with constraints in both the decision and objective spaces[J]. IEEE Transactions on Evolutionary Computation,2019,23(5):870-884.
[28] ZITZLER E,THIELE L,LAUMANNS M,et al. Performance assessment of multiobjective optimizers:An analysis and review[J]. IEEE Transactions on Evolutionary Computation,2003,7(2):117-132.
[29] TIAN Ye,CHENG Ran,ZHANG Xingyi,et al. PlatEMO:A MATLAB platform for evolutionary multi-objective optimization[J]. IEEE Computational Intelligence Magazine,2017,12(4):73-87.
[30] LIU Chao,ZHAO Qi,YAN Bai,et al. Adaptive sorting-based evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation,2019,23(2):247-257.
[31] TIAN Ye,ZHANG Tao,XIAO Jianhua,et al. A coevolutionary framework for constrained multiobjective optimization problems[J]. IEEE Transactions on Evolutionary Computation,2021,25(1):102-116.
[32] MING Mengjun,TRIVEDI A,WANG Rui,et al. A dual-population-based evolutionary algorithm for constrained multiobjective optimization[J]. IEEE Transactions on Evolutionary Computation,2021,25(4):739-753.
[33] TIAN Ye,ZHANG Yajie,SU Yansen,et al. Balancing objective optimization and constraint satisfaction in constrained evolutionary multiobjective optimization[J]. IEEE Transactions on Cybernetics,2020(8):1.
[34] JIAO Ruwang,ZENG Sanyou,LI Changhe,et al. Handling constrained many-objective optimization problems via problem transformation[J]. IEEE Transactions on Cybernetics,2021,51(10):4834-4847.
[35] LIU Zhizhong,WANG Yong,WANG Bingchuan. Indicator-based constrained multiobjective evolutionary algorithms[J]. IEEE Transactions on Systems,Man,and Cybernetics:Systems,2021,51(9):5414-5426.
[36] ZHOU Yalan,ZHU Min,WANG Jiahai,et al. Tri-goal evolution framework for constrained many-objective optimization[J]. IEEE Transactions on Systems,Man,and Cybernetics:Systems,2020,50(8):3086-3099.
[37] CHENG Ran,LI Miqing,TIAN Ye,et al. A Benchmark test suite for evolutionary many-objective optimization[J]. Complex & Intelligent Systems,2017,3(1):67-81.
[38] WOLPERT D H,MACREADY W G. No free lunch theorems for optimization[J]. IEEE Transactions on Evolutionary Computation,1997,1(1):67-82.
[39] JOYCE T,HERRMANN J M. A review of no free lunch theorems,and their implications for metaheuristic optimisation[M].. Springer International Publishing,2018.
[40] BEHZADIAN M,OTAGHSARA S K,YAZDANI M,et al. A state-of the-art survey of TOPSIS applications[J]. Expert Systems with Applications,2012,39(17):13051-13069.
[41] ZHANG Xingyi,TIAN Ye,CHENG Ran,et al. An efficient approach to nondominated sorting for evolutionary multiobjective optimization[J]. IEEE Transactions on Evolutionary Computation,2015,19(2):201-213.