Tool Wear Monitoring In Micro-Milling Based on Digital Twin Technology with an Extended Kalman Filter

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Christiand, Gandjar Kiswanto, Ario Sunar Baskoro, Zulhendri Hasymi, Tae Jo Ko
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引用次数: 0

Abstract

In order to avoid catastrophic events that degrade the quality of machined products, such as tool breakage, it is vital to have a prognostic system for monitoring tool wear during the micro-milling process. Despite the long history of the tool wear monitoring field, creating such a system to track, monitor, and foresee the rapid progression of tool wear still needs to be improved in the application of micro-milling. On the other hand, digital twin technology has recently become widely recognized as significant in manufacturing and, notably, within the Industry 4.0 ecosystem. Digital twin technology is considered a potential breakthrough in developing a prognostic tool wear monitoring system, as it enables the tracking, monitoring, and prediction of the dynamics of a twinned object, e.g., a CNC machine tool. However, few works have explored the digital twin technology for tool wear monitoring, particularly in the micro-milling field. This paper presents a novel tool wear monitoring system for micro-milling machining based on digital twin technology and an extended Kalman filter framework. The proposed system provides wear progression notifications to assist the user in making decisions related to the machining process. In an evaluation using four machining datasets of slot micro-milling, the proposed system achieved a maximum error mean of 0.038 mm from the actual wear value. The proposed system brings a promising opportunity to widen the utilization of digital twin technology with the extended Kalman filter framework for seamless data integration for wear monitoring service.
基于数字孪生技术和扩展卡尔曼滤波器的微铣削刀具磨损监控系统
为了避免刀具破损等降低加工产品质量的灾难性事件,在微铣加工过程中建立刀具磨损监测预报系统至关重要。尽管刀具磨损监测领域历史悠久,但在微铣削应用中,创建这样一个系统来跟踪、监测和预测刀具磨损的快速发展仍有待改进。另一方面,数字孪生技术最近已被广泛认为在制造业,尤其是工业 4.0 生态系统中具有重要意义。数字孪生技术可跟踪、监测和预测孪生对象(如数控机床)的动态,因此被认为是开发刀具磨损预报监测系统的潜在突破口。然而,很少有研究将数字孪生技术用于刀具磨损监测,尤其是在微铣领域。本文介绍了一种基于数字孪生技术和扩展卡尔曼滤波器框架的新型微铣削加工刀具磨损监测系统。该系统可提供磨损进展通知,帮助用户做出与加工过程相关的决策。在使用四个插槽微铣削加工数据集进行的评估中,拟议系统与实际磨损值的最大误差平均值为 0.038 毫米。通过扩展卡尔曼滤波器框架实现磨损监测服务的无缝数据集成,拟议系统为扩大数字孪生技术的应用带来了大好机会。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
期刊介绍: ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric. Indexed/​Abstracted: Web of Science SCIE Scopus CAS INSPEC Portico
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