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Novel intelligent reasoning system for tool wear prediction and parameter optimization in intelligent milling

Advances in Manufacturing 2024年第1期12卷 76-93页

作者： Long-Hua Xu Chuan-Zhen Huang Zhen Wang Han-Lian Liu Shui-Quan Huang Jun Wang School of Mechanical Engineering Yanshan UniversityQinhuangdao066004HebeiPeople’s Republic of China Center for Advanced Jet Engineering Technologies(CaJET) Key Laboratory of High-efficiency and Clean Mechanical Manufacture(Ministry of Education)National Experimental Teaching Demonstration Center for Mechanical Engineering(Shandong University)School of Mechanical EngineeringShandong UniversityJinan250061People’s Republic of China Institute of Manufacturing Technology Guangdong University of TechnologyGuangzhou510006People’s Republic of China

Accurate intelligent reasoning systems are vital for intelligent *** this study,a new intelligent reasoning system was developed for milling processes to accurately predict tool wear and dynamically optimize machining *** developed system consists of a self-learning algorithm with an improved particle swarm optimization(IPSO)learning algorithm,prediction model determined by an improved case-based reasoning(ICBR)method,and optimization model containing an improved adaptive neural fuzzy inference system(IANFIS)and *** results showed that the IPSO algorithm exhibited the best global convergence *** ICBR method was observed to have a better performance in predicting tool wear than standard CBR *** IANFIS model,in combination with IPSO,enabled the optimization of multiple objectives,thus generating optimal milling *** paper offers a practical approach to developing accurate intelligent reasoning systems for sustainable and intelligent manufacturing.

关键词： Improved particle swarm optimization(IPSO)algorithm Improved case-based reasoning(ICBR)method Adaptive neural fuzzy inference system(ANFIS)model tool wear prediction Intelligent manufacturing

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Multi-Scale Convolutional Gated Recurrent Unit Networks for tool wear prediction in Smart Manufacturing

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Chinese Journal of Mechanical Engineering 2021年第3期34卷 130-145页

作者： Weixin Xu Huihui Miao Zhibin Zhao Jinxin Liu Chuang Sun Ruqiang Yan School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049China

As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health *** of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and *** this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear *** the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time *** features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant *** the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear *** case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

关键词： tool wear prediction Multi-scale Convolutional neural networks Gated recurrent unit

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Machining Simulation Based Flank wear prediction of Cutting tools

Machining Simulation Based Flank Wear Prediction of Cutting ...

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第一届国际模具技术会议

作者： JianchengLiu KazuoYamazaki MasaomiTsutsumi YoshinoriIsobata Department of Mechanical ＆ Aeronautical Engineering University of California Davis CA 95616 USA Department of Mechanical ＆ Aeronautical Engineering University of California Davis CA 95616 USA Tokyo University of Agriculture ＆ Technology Tokyo Japan Tokyo University of Agriculture ＆ Technology Tokyo Japan

＜正＞ One of the important problems facing the unmanned operation of CNC machining tool systems is how to monitor tool wear and timely replace damaged or worn tools automatically. tool wear directly influences the machining accuracy and the stability of machining process. Therefore, it is important to monitor cutting tool conditions and to record tool wear data in conjunction with cutting conditions for tool life inspection and predication. It is known that cutting distance, cutting time, and number of impact of cutting edge with workpiece are closely associated with tool wear. The objective of this research is to develop a new simulation system for tool wear prediction through machining simulation based on NC program, tool geometric information and workpiece shape. In the paper, the machining simulation methodology is introduced and the definition of the tool utilization parameters such as number of impact, material removal, engage and disengage angles, and actual cutting distance are described in detail. Then the relationships between the tool wear and the parameters such as cutting distance, cutting time, material removal, number of impact with different cutting conditions such as different feedrate, pick feed rate and depth of cut are established by both experiment and calculation. Finally, the feasibility was verified by the milling experimental study in a CNC machining center. The experimental results show that the cutting distance and number of impact can be used to predict the tool wear in milling operations

关键词： tool wear machining simulation tool wear prediction milfing operation

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