☆ 4.7 Review

Improving the useful life of tools using active vibration control through data-driven approaches: A systematic literature review

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2024)

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Article Acoustics

Active vibration control development in ultra-precision machining

Francesco Aggogeri et al.

Summary: This study presents a combined feedback-feedforward adaptive regulator for active vibration control in machining processes. Through simulations and experimental campaigns, the system is proven effective in reducing vibration amplitudes, with a maximum reduction of 43.7% at critical frequencies.

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Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning

Mustafa Kuntoglu et al.

Summary: Utilizing five different sensors for data collection on a lathe, the study measured tool wear and investigated the successes of sensors in detecting wear and breakage through graphical analysis. Results show that temperature and acoustic emission signals are highly accurate in predicting wear, and sensor fusion-based turning provides more reliable, robust, and consistent machining.

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Analysis and prediction of the impact of technological parameters on cutting force components in rough milling of AZ31 magnesium alloy

M. Kulisz et al.

Summary: This study investigates the milling process of AZ31 magnesium alloy through dry milling experiments. It shows that milling with high cutting speed can reduce the values of cutting force components and transition into high-speed machining conditions range. Predictions of cutting force components using artificial neural networks, including amplitudes and root mean square, can be made based on the experimental results.

ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING (2021)

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Mustafa Kuntoglu et al.

Summary: The indirect tool condition monitoring systems provide a way to track the condition of cutting tools through various energy types, without direct intervention in the cutting zone. This study reviews the sensors commonly used in indirect tool condition monitoring systems and their correlations with tool wear over the past two decades, highlighting the limited research on tool condition monitoring systems in turning and the need for detailed analysis of measured variables such as tool wear and vibration. Tool condition monitoring is essential for the development of Industry 4.0 in machining industry, paving the way for future advancements.

SENSORS (2021)

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Article Chemistry, Analytical

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Ziqiu Kang et al.

Summary: This study introduces a novel machine learning-based approach for automating the prediction of equipment failures in continuous production lines. The proposed model utilizes various techniques to preprocess data and employs neural network algorithm, demonstrating effectiveness in predicting the remaining useful life of turbo engines in a case study using NASA turbo engine datasets.

SENSORS (2021)

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Article Automation & Control Systems

A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life

Lei Ren et al.

Summary: The integration of various aspects of the manufacturing process with new information technologies like IoT, big data, and cloud computing leads to more flexible and intelligent industrial manufacturing systems. This paper proposes a new LIB RUL prediction method named Auto-CNN-LSTM, based on improved CNN and LSTM, which utilizes an autoencoder to enhance data dimensions for better training and a filter for smoother output. Experiments show the effectiveness of this method compared to other commonly used techniques.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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Active vibration control of thin-walled milling based on ANFIS parameter optimization

Xiaojuan Wang et al.

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

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MECCANICA (2021)

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Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery

Biao Wang et al.

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

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A state-of-the-art review on sensors and signal processing systems in mechanical machining processes

Mustafa Kuntoglu et al.

Summary: Sensors play a key role in the health diagnosis of system in data-based enterprises, providing prognoses through early-warning systems and improving efficiency by determining the remaining useful life of machines or tools. Sensor fusion, a technology that uses multi-sensor information to make decisions about tool status and system stability, is still not widely accepted or practiced. Despite the advantages of sensors in terms of efficiency, time savings, and cost, there is a need for improved adaptive control systems and more studies contributing to the literature to further enhance their utilization in the industry.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

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Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

Sameer Sayyad et al.

Summary: The article discusses the importance of predictive maintenance for cutting tools in industrial operations to avoid unplanned downtime and productivity loss. Various monitoring techniques, feature extraction methods, and decision-making models are summarized, along with a comparison of prediction models for tool wear estimation under different operating conditions. The challenges, limitations, recent advancements, and future research scope in RUL prognostics techniques using AI are also highlighted.

IEEE ACCESS (2021)

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Tool condition monitoring framework for predictive maintenance: a case study on milling process

E. Traini et al.

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A novel approach for predicting tool remaining useful life using limited data

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