基于集成式无线振动传感刀架的铣削刀具状态监控系统

IF 1.9 4区 工程技术 Q2 Engineering
X. Sun
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引用次数: 0

摘要

刀具状态监测(TCM)对智能制造至关重要,而切削振动信号被证明与刀具磨损状态高度相关。本文设计了一种用于在线振动信号传感的无线智能刀架,将加速度计嵌入振动源附近,并集成了信号处理电路,与传统的有线方式相比,显示出良好的振动传感能力。为保证 TCM 算法在不同加工条件下的泛化能力,设计了切削参数变化范围较大的切削实验,并通过智能手柄采集刀具整个生命周期的振动信号。然后研究了特征提取和选择以提供有价值的信息,并实现了人工神经网络算法。结果表明,该算法的准确率为 85.0%,但在区分某些磨损状态方面表现不佳。为解决这一问题,提出了一种基于两个串联人工神经网络和新特征集的优化方法。优化算法的准确率为 90.0%,准确率提高了 16.8%,初始磨损样本的平均预测概率提高了 15.0%。尽管牺牲了速度,但优化算法在识别准确率和数据置信度方面取得了进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Milling Tool Condition Monitoring Based on an Integrated Wireless Vibration Sensing Tool Holder

Milling Tool Condition Monitoring Based on an Integrated Wireless Vibration Sensing Tool Holder

Tool condition monitoring (TCM) is crucial for smart manufacturing and cutting vibration signal is proven to be highly related to tool wear state. In this paper, a wireless smart tool holder is designed for online vibration signal sensing for TCM with accelerometer embedded close to vibration source and signal processing circuits integrated, showing good performance of vibration sensing ability compared with traditional wired ways. Cutting experiments are designed with cutting parameters of great varied range to guarantee the generalization ability of TCM algorithm for different machining conditions and vibration signal of whole tool life cycle is collected by smart handle. Then feature extraction and selection are studied to provide valuable information and artificial neural network algorithm is realized. Results show the algorithm has an accuracy of 85.0% with poor performance in distinguishing some wear states. To solve this problem, an optimized method based on two ANNs in series with new feature sets is proposed. The optimized algorithm has an accuracy of 90.0% with an accuracy increase of 16.8% and the average predicted probability increase of 15.0% in initial wear samples. In spite of speed sacrifice, the optimized algorithm makes progress in recognition accuracy and data confidence level.

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来源期刊
CiteScore
4.10
自引率
10.50%
发文量
115
审稿时长
3-6 weeks
期刊介绍: The International Journal of Precision Engineering and Manufacturing accepts original contributions on all aspects of precision engineering and manufacturing. The journal specific focus areas include, but are not limited to: - Precision Machining Processes - Manufacturing Systems - Robotics and Automation - Machine Tools - Design and Materials - Biomechanical Engineering - Nano/Micro Technology - Rapid Prototyping and Manufacturing - Measurements and Control Surveys and reviews will also be planned in consultation with the Editorial Board.
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