Abstract

The dynamic performance of the steel–Basalt fiber polymer concrete (BFPC) machine tool joint surface (referred to as the joint surface) has a significant impact on the overall BFPC machine tool performance; however, its dynamic characteristics remain unclear. In order to solve this problem, the influence of roughness and surface pressure on the dynamic performance of joint surface was studied experimentally, and a neural network prediction model for the dynamic performance of the joint surface was established. A BFPC bed was designed and manufactured, and BFPC bed’s dynamic performance was tested experimentally. The finite element simulation model of BFPC bed was established with equivalent spring-damper element. The BFPC bed’s dynamic performance without considering the influence of the joint surface and considering the influence of the joint surface was studied separately. The results show that the maximum error of the natural frequency of the BFPC bed was 6.937% considering the influence of the joint surface, which was much lower than the error without considering the influence of the joint surface. The maximum amplitude error of the X-axis and Z-axis acceleration of the BFPC bed was 6.917% and 5.15%, which were much smaller than the error without considering the influence of the joint surface. It proves the accuracy of the neural network prediction model for dynamic performance of the joint surface and the validity of the finite element simulation method for the joint surface. It provides theoretical support for the design analysis of BFPC machine tool.

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