Tool Condition Monitoring for High-Performance Machining Systems—A Review

Mohamed, Ayman; Hassan, Mahmoud; M’Saoubi, Rachid; Attia, Helmi

doi:10.3390/s22062206

Cited by 72 publications

(41 citation statements)

References 229 publications

(389 reference statements)

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“…The novelty of this study is based on the identification of the patterns of electromobility development presented in the logic sequence of methods, The article provides an overview of the existing literature on Industry 4.0 and electromobility development relations. However, the effects on the management of the companies that use the Industry 4.0 technologies and form inter-organizational networks should be examined in the future in the broader context of the electric vehicles chargers networks [143]. Another novelty of this study is based on the identification of leaders in the presented ranks and clusters.…”

Section: Discussionmentioning

confidence: 99%

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

2022

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The Industry 4.0 idea influences the development of both charging stations and electromobility development, due to its emphasis on device communication, cooperation, and proximity. Therefore, in electromobility development, growing attention is paid to chargers’ infrastructure density and automotive electric vehicles’ accessibility. The main goal of this scientific paper was to present the electromobility development represented in the number of charging stations and its infrastructure development calculations. In this study, the sequence of methods was used to indicate and explore the research gap. The first was the Structured Literature Review (SLR) variation method. The second method was the classical tabular comparison of gathered results. The third research method was a cluster analysis based on secondary data with cross-country comparisons of the number of charging stations and electric cars. Therefore, this paper presents a theoretical discussion and practical business implications based on the achieved results of clusters and rankings. The main finding of this paper is that charging stations play a pivotal role in electromobility development in countries with already developed road infrastructure and maritime transportation. The charging stations can support energetic infrastructure, especially in countries with vast geographical distances. The charging stations and electric vehicles statistics presented in ratios and ranks proved similarities in the electromobility development patterns in the analyzed countries. This paper also presents the limitations of the performed study and identifies possible future research avenues.

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Section: Discussionmentioning

confidence: 99%

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

2022

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“…The advantages of the multi-sensor approach and information fusion have been reported in diverse literature [10,269,407,408]. These methods can be employed to improve the reliability of chatter detection systems.…”

Section: Robustness Enhancementmentioning

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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“…As a result, condition monitoring and process condition monitoring systems are integral parts of intelligent manufacturing that support the quality inspection. Such highly automated systems rather support the flow of huge volumes of data that can be analyzed in real time without interrupting any machining workflow [1].…”

Section: Introductionmentioning

confidence: 99%

Efficient Feature Learning Approach for Raw Industrial Vibration Data Using Two-Stage Learning Framework

Tnani

Subarnaduti

Diepold

2022

Sensors

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In the last decades, data-driven methods have gained great popularity in the industry, supported by state-of-the-art advancements in machine learning. These methods require a large quantity of labeled data, which is difficult to obtain and mostly costly and challenging. To address these challenges, researchers have turned their attention to unsupervised and few-shot learning methods, which produced encouraging results, particularly in the areas of computer vision and natural language processing. With the lack of pretrained models, time series feature learning is still considered as an open area of research. This paper presents an efficient two-stage feature learning approach for anomaly detection in machine processes, based on a prototype few-shot learning technique that requires a limited number of labeled samples. The work is evaluated on a real-world scenario using the publicly available CNC Machining dataset. The proposed method outperforms the conventional prototypical network and the feature analysis shows a high generalization ability achieving an F1-score of 90.3%. The comparison with handcrafted features proves the robustness of the deep features and their invariance to data shifts across machines and time periods, which makes it a reliable method for sensory industrial applications.

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Tool Condition Monitoring for High-Performance Machining Systems—A Review

Cited by 72 publications

References 229 publications

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

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

Efficient Feature Learning Approach for Raw Industrial Vibration Data Using Two-Stage Learning Framework

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