Machine Learning-based CPS for Clustering High throughput Machining Cycle Conditions

Díaz-Rozo, Javier; Bielza, Concha; Larrañaga, Pedro

doi:10.1016/j.promfg.2017.07.091

Cited by 39 publications

(13 citation statements)

References 17 publications

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“…Unsupervised learning solutions for CPS have been developed in Reference [52]. Focusing on monitoring the machine spindle during its operation in a CPS, multidimensional classification using ML clustering was developed.…”

Section: Unsupervised Learning-based Solutionsmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

238

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The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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Section: Unsupervised Learning-based Solutionsmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

238

View full text Add to dashboard Cite

show abstract

“…As a result, the most optimum solution is found by comparing methods from given the best results. The area that appears on the graph as 'Other' contains performance metrics such as Model Correlation Coefficient [35], Software Product Quality Metrics (ISO / IEC 9126, 25041, 25051) [41], α−λ Metric [42] and Opinion of a Machining Expert [43]. To evaluate the performance of clustering algorithms, metrics that not sufficiently reliable are used such as homogeneity score, integrity score, V measurement, corrected Rand index, corrected mutual information, silhouette coefficient.…”

Section: Rq22: Which Methods Have Been Used To Determine the Predictive Maintenance Performance Achieved?mentioning

confidence: 99%

“…To evaluate the performance of clustering algorithms, metrics that not sufficiently reliable are used such as homogeneity score, integrity score, V measurement, corrected Rand index, corrected mutual information, silhouette coefficient. In this case, as in [43], the results can be evaluated in the opinion of a machining expert. Another outstanding performance measurement criterion is the 'Compared Actual RUL and Estimated RUL' option.…”

Section: Rq22: Which Methods Have Been Used To Determine the Predictive Maintenance Performance Achieved?mentioning

confidence: 99%

Kestirimci Bakım ve Kalan Yararlı Ömür Uygulama için Teknikler: Sistematik Haritalama Çalışması

Ture

Akbulut

Zaim

2021

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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With prognostic activities, it is possible to predict the remaining useful life (RUL) of industrial systems with high accuracy by following the current health status of devices. In this study, we have collected 199 articles on predictive maintenance and remaining useful life. The aim of our systematic mapping study is to determine which techniques and methods are used in the areas of predictive maintenance and remaining useful life. Another thing we aim is to give an idea about the main subject to the researchers who will work in this field. We created our article repository by searching databases such as IEEE and Science Direct with certain criteria and classified the articles we obtained. By applying the necessary inclusion and exclusion criteria in the article pool we collected, the most appropriate articles were determined and our study was carried out through these articles. When we focused on the results, it was learned that the SupportVector Machine algorithm is the most preferred predictive maintenance method. Most studies aimed at evaluating the performance and calculating the accuracy of the results used the Root Mean Square Error algorithm. In our study, every method and algorithm included in the articles are discussed. The articles were examined together with the goals and questions we determined, and results were obtained. The obtained results are explained and shown graphically in the article. According to the results, it is seen that the topics of predictive maintenance and remaining useful lifetime provide functionality and financial gain to the environment they are used in. Our study was concluded by light on many questions about the application of predictive maintenance.

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“…Further, K-mean variation has formed the basis of research aimed at improving refinery catalytic processes [23]. A very recent study [24] proposed the use of a cyber-physical system that uses actual data from a machining process. The objective is to evaluate the performance of a machine tool spindle during a high-performance machining operation.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Machine Learning for Advanced Tolerance Monitoring of Wire Electrical Discharge Machining of Disc Turbine Fir-Tree Slots

Wang

Sánchez

Ayesta

et al. 2018

Sensors

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Manufacturing more efficient low pressure turbines has become a topic of primary importance for aerospace companies. Specifically, wire electrical discharge machining of disc turbine fir-tree slots has attracted increasing interest in recent years. However, important issues must be still addressed for optimum application of the WEDM process for fir-tree slot production. The current work presents a novel approach for tolerance monitoring based on unsupervised machine learning methods using distribution of ionization time as a variable. The need for time-consuming experiments to set-up threshold values of the monitoring signal is avoided by using K-means and hierarchical clustering. The developments have been tested in the WEDM of a generic fir-tree slot under industrial conditions. Results show that 100% of the zones classified into Clusters 1 and 2 are related to short-circuit situations. Further, 100% of the zones classified in Clusters 3 and 5 lie within the tolerance band of ±15 μm. Finally, the 9 regions classified in Cluster 4 correspond to situations in which the wire is moving too far away from the part surface. These results are strongly in accord with tolerance distribution as measured by a coordinate measuring machine.

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Machine Learning-based CPS for Clustering High throughput Machining Cycle Conditions

Cited by 39 publications

References 17 publications

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Kestirimci Bakım ve Kalan Yararlı Ömür Uygulama için Teknikler: Sistematik Haritalama Çalışması

Unsupervised Machine Learning for Advanced Tolerance Monitoring of Wire Electrical Discharge Machining of Disc Turbine Fir-Tree Slots

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