基于新型卷积增强变压器的工具尖振动预测

IF 3.4 Q1 ENGINEERING, MECHANICAL 国际机械系统动力学学报(英文) Pub Date : 2024-02-29 DOI:10.1002/msd2.12096
Adeel Shehzad, Xiaoting Rui, Yuanyuan Ding, Yu Chang, Jianshu Zhang, Hanjing Lu, Yiheng Chen
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引用次数: 0

摘要

卓越的表面光洁度仍然是精密加工操作的基本标准,而刀尖振动是显著影响加工表面质量的重要因素。在应用于复杂的高端系统时,基于物理的模型在很大程度上依赖于简化模型的假设。然而,这些假设可能会影响模型的准确性。相比之下,数据驱动技术已成为预测和复杂系统分析等任务中颇具吸引力的替代方案。为了利用数据驱动模型的优势,本研究引入了一种用于工具尖振动预测的新型卷积增强变压器模型,简称为 CeT-TV。该模型在超精密飞切(UPFC)操作中的成功应用证明了其有效性。该模型有两种不同的变体,即引导式和非引导式 CeT-TV,在为 UPFC 应用定制的数据集上进行了开发和严格测试。结果表明,有向导 CeT-TV 模型性能卓越,平均绝对误差和均方根误差值最低。此外,该模型在预测值和实际测量值之间表现出极佳的一致性,从而凸显了其在 UPFC 中预测刀尖振动的效率和潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Tool-tip vibration prediction based on a novel convolutional enhanced transformer

Superior surface finish remains a fundamental criterion in precision machining operations, and tool-tip vibration is an important factor that significantly influences the quality of the machined surface. Physics-based models heavily rely on assumptions for model simplification when applied to complex high-end systems. However, these assumptions may come at the cost of compromising the model's accuracy. In contrast, data-driven techniques have emerged as an attractive alternative for tasks such as prediction and complex system analysis. To exploit the advantages of data-driven models, this study introduces a novel convolutional enhanced transformer model for tool-tip vibration prediction, referred to as CeT-TV. The effectiveness of this model is demonstrated through its successful application in ultra-precision fly-cutting (UPFC) operations. Two distinct variants of the model, namely, guided and nonguided CeT-TV, were developed and rigorously tested on a data set custom-tailored for UPFC applications. The results reveal that the guided CeT-TV model exhibits outstanding performance, characterized by the lowest mean absolute error and root mean square error values. Additionally, the model demonstrates excellent agreement between the predicted values and the actual measurements, thus underlining its efficiency and potential for predicting the tool-tip vibration in the context of UPFC.

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Issue Information Cover Image, Volume 4, Number 3, September 2024 Design of bionic water jet thruster with double-chamber driven by electromagnetic force A data-assisted physics-informed neural network (DA-PINN) for fretting fatigue lifetime prediction Comparison of the performance and dynamics of the asymmetric single-sided and symmetric double-sided vibro-impact nonlinear energy sinks with optimized designs
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