Benchmark of Automated Machine Learning with State-of-the-Art Image Segmentation Algorithms for Tool Condition Monitoring

Lutz, Benjamin; Reisch, Raven T.; Kißkalt, Dominik; Avcı, Burçak Kılıçkıran; Regulin, Daniel; Knoll, Alois; Franke, Jörg

doi:10.1016/j.promfg.2020.10.031

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…Several papers investigate the use of AutoML methods to make the benefits of MLbased models even easier to apply to tool condition monitoring [6,[27][28][29]. AutoML leverages the autonomous adaptation of models to changing process conditions, especially in individualized production.…”

Section: Process Conditions Among Multiple Tool Lifecycles Varyingmentioning

confidence: 99%

Explainable Remaining Tool Life Prediction for Individualized Production Using Automated Machine Learning

Krupp,

Wiede,

Friedhoff

et al. 2023

Sensors

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The increasing demand for customized products is a core driver of novel automation concepts in Industry 4.0. For the case of machining complex free-form workpieces, e.g., in die making and mold making, individualized manufacturing is already the industrial practice. The varying process conditions and demanding machining processes lead to a high relevance of machining domain experts and a low degree of manufacturing flow automation. In order to increase the degree of automation, online process monitoring and the prediction of the quality-related remaining cutting tool life is indispensable. However, the varying process conditions complicate this as the correlation between the sensor signals and tool condition is not directly apparent. Furthermore, machine learning (ML) knowledge is limited on the shop floor, preventing a manual adaption of the models to changing conditions. Therefore, this paper introduces a new method for remaining tool life prediction in individualized production using automated machine learning (AutoML). The method enables the incorporation of machining expert knowledge via the model inputs and outputs. It automatically creates end-to-end ML pipelines based on optimized ensembles of regression and forecasting models. An explainability algorithm visualizes the relevance of the model inputs for the decision making. The method is analyzed and compared to a manual state-of-the-art approach for series production in a comprehensive evaluation using a new milling dataset. The dataset represents gradual tool wear under changing workpieces and process parameters. Our AutoML method outperforms the state-of-the-art approach and the evaluation indicates that a transfer of methods designed for series production to variable process conditions is not easily possible. Overall, the new method optimizes individualized production economically and in terms of resources. Machining experts with limited ML knowledge can leverage their domain knowledge to develop, validate and adapt tool life models.

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Section: Process Conditions Among Multiple Tool Lifecycles Varyingmentioning

confidence: 99%

Explainable Remaining Tool Life Prediction for Individualized Production Using Automated Machine Learning

Krupp,

Wiede,

Friedhoff

et al. 2023

Sensors

View full text Add to dashboard Cite

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“…The results of the comparison of the available tool wear datasets from various dimensions are shown in Table 8. Specifically, "Public" indicates whether the dataset is publicly available, "Tool images" represents the data type in NJUST-CCTD: An Image Database for Milling Tool Wear Classification with Deep L Milling -García-Ordás [6] Milling 573 Wu [7] Milling 8400 B. Lutz [8] Turning 100 Bergs.T [9] Milling 3000 B. Lutz [12] Milling 207 Brili, N [13] Turning 9333 NJUST-CCTD Milling 8000…”

Section: The Advantages Of the Datasetmentioning

confidence: 99%

“…Bergs et al [9] introduced 3000 images of tool wear for image classification and segmentation. Lutz et al [12] recorded a heterogeneous dataset of 207 industrial cutting tool images and compare manually trained segmentation networks and automated machine learning. Brili et al [13] collected 9333 thermal imaging data when the tool was not soaked in cutting fluid after working.…”

Section: Introductionmentioning

confidence: 99%

NJUST-CCTD: An Image Database for Milling Tool Wear Classification with Deep Learning

Pan

Xu²,

Xiong³

et al. 2022

Preprint

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Deep learning has gained popularity in the task of tool wear identification recently. As an important application of deep learning, however, there exist few public datasets and benchmarks for the research of visual identification of tool wear. To address this issue, we present a classification-based image dataset for carbide milling tool wear (NJUST-CCTD) and make it publicly available on the Github website. This dataset includes two categories: wear tools and no-wear tools. The two categories contain 5000 and 3000 photos, respectively. Based on this dataset, eight baselines are evaluated as references against this benchmark. To further improve the classification performance, we propose a novel cemented carbide milling tool wear intelligent classification framework (CMCNet). The framework consists of two modules: a deep learning based classification network and a multi-scale feature fusion denoising network called DSSNet. DSSNet is constructured with deeper network structure, connections across layers, and multi-scale sequence fusion module. It is capable of explicitly modeling the semantic and spatial correlation. Apart from DSSNet, the denoising module further improves the performance by adaptively altering the level of denoising based on the performance of the network. The two modules could be optimized with the backward gradient, yielding an end-to-end learning framework. On the basis of the dataset, CMCNet performed exceptionally well when categorizing photos intelligently. After 50 training epochs, the model outperformed the original classification network by 3.250%, achieving the top-1 accuracy of 95.375% on the test set. The NJUST-CCTD can be downloaded at https://github.com/paddy112233/PADDY

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“…The field of vision-based condition monitoring techniques is smaller and in the field of machine tools mainly focused on monitoring the wear of cutting tools. [22] implement a new automatic machine learning setup where they remedy the tedious task of hyper-parameter set up for machine learning systems by applying amongst other, different data augmentation techniques to build robust classifiers for the detection of wear on cutting tools. [38] use classical CNNs to predict the wear on classical cutting tools.…”

Section: Related Workmentioning

confidence: 99%

Intelligent vision based wear forecasting on surfaces of machine tool elements

Schlagenhauf

Burghardt

2021

SN Appl. Sci.

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To realize autonomous production machines it is necessary that machines are able to automatically and autonomously predict their condition. Although many classical as well as Deep Learning based approaches have shown the ability to classify faults, so far there are no approaches that go beyond the basic detection of faults. A complete, image based predictive maintenance approach for machine tool components has to the best of our knowledge not been investigated so far. In this paper it is shown how defects on a Ball Screw Drive (BSD) can be automatically detected by using a machine learning based detection module, quantified by using an intelligent defect quantification module and finally forecasted by a prognosis module in a combined approach. To optimize the presented method, it is shown how existing domain knowledge can be formalized in an expert system and combined with the predictions of the machine learning model to aid quality of the prediction system. This enables the practitioner to forecast the severity of failures on BSD and prevent machine breakdowns. The work is intended to set new accents for the development of practical predictive maintenance systems and bridging the fields of machine learning and production engineering. The code is available under: https://github.com/2Obe/Pitting_Pred_Maintenance.

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Benchmark of Automated Machine Learning with State-of-the-Art Image Segmentation Algorithms for Tool Condition Monitoring

Cited by 11 publications

References 19 publications

Explainable Remaining Tool Life Prediction for Individualized Production Using Automated Machine Learning

Explainable Remaining Tool Life Prediction for Individualized Production Using Automated Machine Learning

NJUST-CCTD: An Image Database for Milling Tool Wear Classification with Deep Learning

Intelligent vision based wear forecasting on surfaces of machine tool elements

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