Optimal design of a hydraulic excavator working device based on parallel particle swarm optimization

Li, Xin; Wang, Guoqiang; Miao, Shijun; Li, Xuefei

doi:10.1007/s40430-017-0798-5

Cited by 20 publications

(7 citation statements)

References 30 publications

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“…Qiu et al [14] proposed a new modeling strategy based on the combination of hinge point size variable and working device hinge point force, studied with a variety of alternative models, and optimized the structure design of excavator with a bionic intelligent optimization algorithm, achieving excellent results. Li et al [15] proposed the optimal design of a hydraulic excavator working device, based on the parallel particle swarm optimization (PPSO), through the establishment of the kinematics and dynamics analysis model of hydraulic excavator. e improved parallel PPSO algorithm is used to develop the optimal design for the excavator working device.…”

Section: State Of the Artmentioning

confidence: 99%

Knowledge-Based Structure Optimization Design for Boom of Excavator

Yang

Huang

Shen

et al. 2021

Mathematical Problems in Engineering

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During the design optimization of the excavator boom, there are many design variables and complicated processes. The original optimization methods mainly focused on the optimization of mathematical models, and they lacked consideration in the use of domain knowledge, design-specification knowledge, expert experience knowledge, and historical examples. In order to comprehensively utilize the domain knowledge and expert experience knowledge, this study uses the optimization process analysis, uses knowledge expression and coding processing technology to encode the boom structure, builds an optimal design coding system based on knowledge guidance, and realizes the automatic optimization design of the boom structure. In the process of constructing the knowledge-oriented optimization system, to realize the reuse of the knowledge of the boom structure design in the numerical optimization iteration, a knowledge processing flowchart of the boom structure design is constructed. The concept of “shape distance” is proposed to judge the similarity feature matrix of the boom structure coding. To evaluate whether the stress distribution is uniform, a fast prediction model based on stress characteristic regions is constructed. The research results show that, under the comprehensive consideration of the four working conditions, the knowledge-guided optimization of the boom structure can avoid the deformity in the optimization process, accelerate the calculation speed of the optimization model, and improve the optimization quality of the model.

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Section: State Of the Artmentioning

confidence: 99%

Knowledge-Based Structure Optimization Design for Boom of Excavator

Yang

Huang

Shen

et al. 2021

Mathematical Problems in Engineering

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“…As can be seen in Equations ( 11) and (12), the Coriolis acceleration a gc determines whether to take the positive sign or the negative sign for calculation.…”

Section: Kinematic Modelingmentioning

confidence: 99%

“…Feng H. et al focused on the task of precise control of the hydraulic excavator using multi-objective genetic algorithm optimization [11]. Li X. et al improved the digging efficiency of the hydraulic excavator by optimization of the working mechanism based on the algorithm of parallel PSO [12]. Kim J.-W. et al optimized the working performance of the hydraulic excavator for multiple objects through the hybrid Taguchi random coordinate search algorithm [13].…”

Section: Introductionmentioning

confidence: 99%

Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a Multi-Objective Genetic Algorithm

Wang

et al. 2020

Energies

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As one of the most essential earth-moving equipment, cable shovels significantly influence the efficiency and economy in the open-pit mining industry. The optimal digging trajectory planning for each cycle is the base for achieving effective and energy-saving operation, especially for robotic excavation, in which case, the digging trajectory can be precisely tracked. In this paper, to serve the vision of cable shovel automation, a two-phase multi-objective genetic algorithm was established for optimal digging trajectory planning. To be more specific, the optimization took digging time and energy consumption per payload as objects with the constraints of the limitations of the driving system and geometrical conditions. The WK-55-type cable shovel was applied for the validation of the effectiveness of the multi-objective optimization method for digging trajectories. The digging performance of the WK-55 cable shovel was tested in the Anjialing mining site to establish the constraints. Besides, the digging parameters of the material were selected based on the tested data to make the optimization in line with the condition of the real digging operations. The optimization results for different digging conditions indicate that the digging time decreased from an average of 20 s to 10 s after the first phase optimization, and the energy consumption per payload reduced by 13.28% after the second phase optimization, which validated the effectiveness and adaptivity of the optimization algorithm established in this paper.

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“…e D-H coordinates system of the hydraulic excavator is established ( Figure 3) [19,20]. e relationship between adjacent coordinates systems (o i x i y i z i and o j x j y j z j ) can be expressed by the following four parameters: o set distance s j (the distance from axis x i to axis x j along axis z i ), angle θ j (the angle from axis x i to axis x j along axis z i ), rod length h j (the distance from axis z i to axis z j along axis x j ), and torsion angle α j (the angle from axis z i to axis z j along axis x j ).…”

Section: Kinematics Modelmentioning

confidence: 99%

Active‐Side Calculation Method for a Backhoe Hydraulic Excavator with Incomplete Digging Resistance in a Normal State

Ren

Wang

Chen

et al. 2019

Mathematical Problems in Engineering

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The digging resistance in a normal state is the key to excavator design and automated excavation. It is difficult to accurately predict, simulate, or directly measure the digging resistance in a normal state due to uncertainties in the soil properties and excavation parameters. In this paper, a research idea is proposed that uses the working device as the entry point to indirectly calculate the digging resistance in a normal state by measuring the motion parameters and the cylinder pressure intensity. Based on the rule of combination for spatial force systems, a method for combining and projecting the system of the digging resistance is proposed in which the system is projected as six parts, and the tangential force, normal force, and bending moment in the plane of symmetry of the working device are the objects of the solution to avoid redundant equations. Based on kinematics and dynamics models of the excavator and the force and moment equilibrium conditions of the working device, equations for the active-side calculation of the incomplete digging resistance are derived. Based on these equations, the motion parameters of the working device and data on the cylinder pressure intensity obtained by measurement are used to calculate the incomplete digging resistance. The validation scheme and process proposed use the incomplete digging resistance as the external load to obtain the simulated stress of the working device through transient analysis. The simulated stress and the measured stress corresponding to the position of the measurement point are extracted and compared. The results show that there is a difference in the size of the numerical value between the simulated and measured stress, but the variation law is highly consistent, which validates the calculation method. In this paper, an active-side calculation method is provided for the incomplete digging resistance in a normal state without considering the soil-tool interaction relationships, which lays a theoretical foundation for the study of the digging resistance characteristics in a normal state, as well as excavator design and automated excavation.

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Optimal design of a hydraulic excavator working device based on parallel particle swarm optimization

Cited by 20 publications

References 30 publications

Knowledge-Based Structure Optimization Design for Boom of Excavator

Knowledge-Based Structure Optimization Design for Boom of Excavator

Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a Multi-Objective Genetic Algorithm

Active‐Side Calculation Method for a Backhoe Hydraulic Excavator with Incomplete Digging Resistance in a Normal State

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