Application method based on EOASM algorithm in construction of agent model of stacking robot driving arm base

Abstract

The invention discloses an application method based on an EOASM algorithm in construction of an agent model of a stacking robot driving arm base. Because computer simulation is needed when the complexengineering problem is solved, the cost is high, and the period is long, the invention provides a combined agent model construction method (EOASM method) based on local error expectation point addition, and the combined agent model construction method is applied to maximum stress modeling of a stacking robot driving arm seat. Firstly, a geometric model of a driving arm seat is constructed; secondly, an initial sample is formed, the maximum stress of the driving arm seat is selected as a performance function, and a static simulation model is called to obtain a real response value of the initial sample; then, an initial Kriging and RBF agent model is constructed; and finally, a final combined agent model meeting the precision requirement is constructed through the EOASM algorithm provided by the invention. According to the EOASM algorithm provided by the invention, the model prediction precision can be ensured, the total number of samples can be as low as possible, the robustness is relatively high, and the application prospect is relatively wide.

Description

technical field [0001] The invention belongs to the technical field of engineering design, and in particular relates to an application method of a combined proxy model building method (EOASM method) based on local error expectation plus points in the proxy model building of a palletizing robot driving arm base. Background technique [0002] In engineering problems such as mechanical design and optimization, it is difficult to conduct a large number of real physical experiments, and computer simulation is usually used to replace most of the real physical experiments. For complex engineering problems, even computer simulation experiments usually require a lot of time and cost, which makes it difficult to carry out later analysis and optimization design work. The surrogate model is an approximate modeling of the real physical model, which can save a lot of analysis and calculation costs under the premise of ensuring the accuracy of the model, greatly reducing the workload in la...

AI Technical Summary

Problems solved by technology

[0005] With the development of industrial automation, palletizing robots are widely used because of their high production efficiency and saving manpower and material resources. However, due to high-intensity operation, the assembly of the large arm of the palletizing robot and its driving arm seat often produces greater stress. Time is prone to fatigue damage, which increases the failure rate of parts and reduces the reliability of work

Due to the strong nonlinearity of the stress, it is difficult to express it with an explicit function. At present, the commonly used method is to rely on large-scale computer simulation experiments, but it is very labor-intensive and material resources

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