Condition monitoring system for machine tool auxiliaries

Gittler, Thomas; Stoop, Fabian; Kryscio, David R.; Weiss, Lukáš; Wegener, Konrad

doi:10.1016/j.procir.2020.05.062

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…These influences are cumbersome to reproduce in purely physical models approaches, wherefore many recently published PHM approaches in manufacturing incorporate statistical models. Prominent examples for the application of data driven models in monitoring are described by e.g., [10][11][12][13][14][15], relevant studies on data-based approaches for prognosis are described by [9,[16][17][18]. Both the PHM approach, as well as the applied learning algorithm strongly impact the capabilities and performance of the application.…”

Section: Failure Detection and Prognostics And Health Management (Phmmentioning

confidence: 99%

Machine Tool Component Health Identification with Unsupervised Learning

Gittler

Scholze²,

Rupenyan

et al. 2020

JMMP

Self Cite

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Unforeseen machine tool component failures cause considerable losses. This study presents a new approach to unsupervised machine component condition identification. It uses test cycle data of machine components in healthy and various faulty conditions for modelling. The novelty in the approach consists of the time series representation as features, the filtering of the features for statistical significance, and the use of this feature representation to train a clustering model. The benefit in the proposed approach is its small engineering effort, the potential for automation, the small amount of data necessary for training and updating the model, and the potential to distinguish between multiple known and unknown conditions. Online measurements on machines in unknown conditions are performed to predict the component condition with the aid of the trained model. The approach was exemplarily tested and verified on different healthy and faulty states of a grinding machine axis. For the accurate classification of the component condition, different clustering algorithms were evaluated and compared. The proposed solution demonstrated encouraging results as it accurately classified the component condition. It requires little data, is straightforward to implement and update, and is able to precisely differentiate minor differences of faults in test cycle time series.

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Section: Failure Detection and Prognostics And Health Management (Phmmentioning

confidence: 99%

Machine Tool Component Health Identification with Unsupervised Learning

Gittler

Scholze²,

Rupenyan

et al. 2020

JMMP

Self Cite

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“…Considerations for preliminary analysis and data acquisition for monitoring are exemplarily described e.g. by Gittler et al in [13] and [14]. For the diagnostics and health assessment tasks, Wang et al [15] give an overview of current PHM analytics with a focus of vibration signal-based health indicators.…”

Section: Prognostics Health and Monitoring In Machining And Manufacturingmentioning

confidence: 99%

International Conference on Advanced and Competitive Manufacturing Technologies milling tool wear prediction using unsupervised machine learning

Gittler

Glasder

Öztürk

et al. 2021

Int J Adv Manuf Technol

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Degraded or defect machine components and consumables negatively impact manufacturing quality and productivity. Diagnosing and predicting the wear or degradation status of critical machine components or parts are therefore of general interest. To tackle this challenge, data-driven approaches based on supervised machine learning principles have demonstrated promising results. However, supervised learning models capable of degradation identification require large quantities of data. In practice, run-to-failure data in large amounts is usually not available and expensive to obtain. To overcome this issue, this study proposes an unsupervised learning approach for degradation prognostics of machine tool components and consumables. It uses time series of multi-sensor signal data, which are transformed into a feature representation. The features consist of various characterizations of the time series, allowing to make different signal measurements comparable, and cluster them according to their feature values. The herewith obtained density-based clustering model is used to diagnose and predict the degradation states of components and parts in unknown conditions. The novelty in the proposed approach lies within the identification of continuous component and part degradation states based on unsupervised learning principles. The proposal is verified and demonstrated on an exemplary data set containing a small sample of run-to-failure multi-sensor signals of milling inserts and their corresponding wear state. By the application of the proposed procedure on the exemplary data set, we demonstrate that an unsupervised clustering approach is capable of separating wear data such that meaningful and accurate estimations of the part condition are possible. The advantages are its ability to cope with scarce data sets, its limited engineering and hyperparameter tuning effort, and its straightforward implementation to a multitude of degradation and wear diagnostics scenarios.

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“…Thus, there is a need to develop machine systems based on computer modelling that could meet new requirements [10,11]. It should also be noted that new machines should have quality indicators of rigidity and accuracy [12], while improved developments should be equipped with modern devices and systems [13] that could simplify the implementation of certain procedures, such as [14]: clamping the workpiece, positioning, tool replacement, as well as setting up equipment for batch production [15]. Based on previous research, the authors observed this that published rare research work on the use of machine learning in production [16].…”

Section: Introductionmentioning

confidence: 99%

Current Trends in the Development of Automation Systems in Mechanical Engineering

Ruban

Pasternak

Samchuk

et al. 2022

Advances in Science and Technology

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In this scientific study, the problem of automation of machine-building production is justified. A 3D model of the lathe is presented and its design is improved. Standard layout schemes based on the upgraded spindle assembly have been developed, which make it possible to increase the speed of this type of machine. The results obtained make it possible to achieve the desired cutting speed, which has significantly increased by 2-2,5 times. The constructed dependence of the deviation on the roundness of samples by the finite element method allows predicting the main indicators: feed rate, spindle speed, cutting depth, static imbalance, initial and final pressure. Also, the obtained analytical results allow us to establish the main regularities of forming the accuracy of this lathe.

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Condition monitoring system for machine tool auxiliaries

Cited by 12 publications

References 8 publications

Machine Tool Component Health Identification with Unsupervised Learning

Machine Tool Component Health Identification with Unsupervised Learning

International Conference on Advanced and Competitive Manufacturing Technologies milling tool wear prediction using unsupervised machine learning

Current Trends in the Development of Automation Systems in Mechanical Engineering

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