Abstract
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.
Highlights
Excavator is a commonly used mechanical device in engineering; it is widely used in hydraulic engineering, transportation, power engineering, mining, and other mechanical construction; it can excavate several tons of Earth; the design of this part is the key to the overall performance of the excavator. e structural design and optimization process should be fully integrated application of domain knowledge, expert experience knowledge
Taking the boom of excavation as an example, this paper proposes a structural optimization design method that combines a numerical optimization algorithm with a knowledge system. e integrated technology of numerical calculation and logical reasoning is discussed, and a method of guiding numerical optimization by logical reasoning is studied
E constraint system optimized for the boom structure mainly includes structural strength constraints, geometric shape constraints, motion interference constraints, and stability constraints. e main reason for the existing methods to generate abnormal structure during the solution process is that the geometric shape constraints are not adequately expressed. erefore, a dynamic design optimization method for integrating the genetic algorithm (GA) with the knowledge-based system (KBS) is proposed
Summary
Excavator is a commonly used mechanical device in engineering; it is widely used in hydraulic engineering, transportation, power engineering, mining, and other mechanical construction; it can excavate several tons of Earth; the design of this part is the key to the overall performance of the excavator. e structural design and optimization process should be fully integrated application of domain knowledge, expert experience knowledge. Methods such as approximate models [6, 7], structure reduction order modeling [8, 9], collaborative optimization [10, 11], and constraint expression and processing [12], all make use of improvements in numerical algorithms or combine static system knowledge with numerical optimization algorithms. It is impossible to minimize the problem of global optimization time for each generation of optimized group structure, and it is easy to fall into a locally optimal solution. For this reason, it is necessary to make full use of domain knowledge, design specifications, expert experience, and other knowledge to study the structural intelligent numerical optimization methods guided by the knowledge of the optimization process. Taking the boom of excavation as an example, this paper proposes a structural optimization design method that combines a numerical optimization algorithm with a knowledge system. e integrated technology of numerical calculation and logical reasoning is discussed, and a method of guiding numerical optimization by logical reasoning is studied
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