In-process identification of machine tool dynamics

Liu, Yen-Po; Altintaş, Yusuf

doi:10.1016/j.cirpj.2021.01.007

Cited by 15 publications

(6 citation statements)

References 20 publications

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“…This is because machine tools are only excited at one point during operation, the Tool Center Point (TCP). In order to make the method practicable for these applications, Liu and Altintas [8] performed a mode shape compensation of the TOMA. As a result, only one force excitation point is needed.…”

Section: Estimation Of Modal Parametersmentioning

confidence: 99%

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Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools

Oexle,

Heimberger,

Puchta

et al. 2024

Machines

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The increasing demand for personalized products and the lack of skilled workers, intensified by demographic change, are major challenges for the manufacturing industry in Europe. An important framework for addressing these issues is a digital twin that represents the dynamic behavior of machine tools to support the remaining skilled workers and optimize processes in virtual space. Existing methods for modeling the dynamic behavior of machine tools rely on the use of expert knowledge and require a significant amount of manual effort. In this paper, a concept is proposed for individualized and lifetime-adaptive modeling of the dynamic behavior of machine tools with the focus on the machine’s tool center point. Therefore, existing and proven algorithms are combined and applied to this use case. Additionally, it eliminates the need for detailed information about the machine’s kinematic structure and utilizes automated data collection, which reduces the dependence on expert knowledge. In preliminary tests, the algorithm for the initial model setup shows a fit of 99.88% on simulation data. The introduced re-fit approach for online parameter actualization is promising, as in preliminary tests, an accuracy of 95.23% could be reached.

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Section: Estimation Of Modal Parametersmentioning

confidence: 99%

“…Between the measuring points, they use linear interpolation. The mode shape compensation TOMA proposed by Liu and Altintas [8] is then conducted with the mode shapes from the pre-built database at the respective point of the machine tool workspace.…”

Section: Estimation Of Modal Parametersmentioning

confidence: 99%

Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools

Oexle,

Heimberger,

Puchta

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

“…Some researchers have explored different approaches to obtain more accurate SLDs to estimate chatter stability [290,291] or novel prediction methods [292]. The effects of the mass and the connecting cable of an accelerometer on the FRF measurement of a thin-wall workpiece were assessed by Olvera et al [293].…”

Section: Stability Diagram and Chatter Predictionmentioning

confidence: 99%

Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

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“…To achieve minimal inadequacy based smart manufacturing, rapid quality diagnosis is the key feature. CNC machines are highly advanced mechatronic units which are developed for manufacturing with high accuracy and precision (Liu & Altintas, 2021; Sun & Altintas, 2021). The CNC machine system is utilized for boring, cutting, welding, 3D printing, drilling, milling, and aligning operations (Yadav & Gaur, 2020; Zhang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

BWOA based metaheuristic approach for uncertain nonlinear milling CNC machine system

2023

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The scientific advances in the machining process inculcate intelligence in computer numerical control (CNC), to enhance reliability and quality production. The study made in this article has been directed towards the metaheuristic-based intelligent control of a nonlinear uncertain CNC machine for milling purposes. The presence of cutting force and frictional type constraints, along with parametric uncertainties, degrade the production quality in milling operations. Moreover, enhanced performance of milling operation can be achieved via intelligent control of position and velocity of machine table and tool. In this work, the black widow optimization algorithm (BWOA) driven proportional-integral-derivative (PID) and PI controllers are employed for the position and the velocity of machine table and tool, respectively.Servo and regulatory are the two important control problems that are considered, analysed, and addressed in this work. The most challenging task of simultaneous tuning of PI and PID controllers is also addressed in this brief. Finally, a vivid comparative analysis of five state-of-the-art optimization techniques is also performed, and the obtained results demonstrate the superiority of the proposed control scheme. While dealing with the servo performance of x-axis, the proposed controller gives a 100% improvement in overshoot and 61.54% improvement in settling time, and for regulatory performance, it gives a 98.45% improvement in undershoot. Moreover, for z-axis control, the proposed controller gives a 100% improvement in overshoot and 61.54% improvement in settling time and for regulatory performance gives a 100% improvement in undershoot compared to published literature.

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In-process identification of machine tool dynamics

Cited by 15 publications

References 20 publications

Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools

Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools

Chatter detection in milling processes—a review on signal processing and condition classification

BWOA based metaheuristic approach for uncertain nonlinear milling CNC machine system

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