Dense-Block Structured Convolutional Neural Network-Based Analytical Prediction System of Cutting Tool Wear

Kuo, Ping‐Huan; Lin, Chi‐Chang; Luan, Po‐Chien; Yau, Her‐Terng

doi:10.1109/jsen.2022.3206308

Cited by 15 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the research, the optimal dimensionality for embeddings is between three and seven. If the value is insufficiently tiny, the MPE will be unable to accurately depict signal dynamic mutations; if the value is insufficiently great, the time series will become homogenized and ineffective at catching minute changes in the time series data [36]. Following the investigation, we chose 6 as the embedding dimension m and 1 as the delay time τ.…”

Section: Mpe-based Milling Cutter Wear State Feature Extractionmentioning

confidence: 99%

“…Yang et al [35] presented a system for tool wear monitoring based on multivariate cutting force and a 1D CNN, which demonstrated higher precision in recognizing the degradation condition of milling tools. Lu et al [36] incorporated an Adaptive Frequency Band Attention Module (AFBAM) into the CNN model. This module adaptively amplifies the frequency bands with the most vibration information by analyzing the signal's time-frequency characteristics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tool Wear State Identification Based on the IWOA-VMD Feature Selection Method

Shui,

Rong,

Dan

et al. 2024

Machines

View full text Add to dashboard Cite

Complex, thin-walled components are the most important load-bearing structures in aircraft equipment. Monitoring the wear status of milling cutters is critical for enhancing the precision and efficiency of thin-walled item machining. The cutting force signals of milling cutters are non-stationary and non-linear, making it difficult to detect wear stages. In response to this issue, a system for monitoring milling cutter wear has been presented, which is based on parameterized Variational Mode Decomposition (VMD) Multiscale Permutation Entropy. Initially, an updated whale optimization technique is used, with the joint correlation coefficient serving as the fitness value for determining the VMD parameters. The improved VMD technique is then used to break down the original signal into a series of intrinsic mode functions, and the Multiscale Permutation Entropy of each effective mode is determined to generate a feature vector. Finally, a 1D Convolutional Neural Network (1D CNN) is employed as the input model for state monitoring using the feature vector. The experimental findings show that the suggested technique can efficiently extract characteristics indicating the wear condition of milling cutters, allowing for the precise monitoring of milling cutter wear states. The recognition rate is as high as 98.4375%, which is superior to those of comparable approaches.

show abstract

Section: Mpe-based Milling Cutter Wear State Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tool Wear State Identification Based on the IWOA-VMD Feature Selection Method

Shui,

Rong,

Dan

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

“…Kuo et al implemented a DenseNet architecture to predict cutting tool wear with the NASA milling dataset [26]. The dataset consists of AE, vibration, and current sensor data.…”

Section: B Densenet-121mentioning

confidence: 99%

A Novel Custom One-Dimensional Time-Series DenseNet for Water Pipeline Leak Detection and Localization Using Acousto-Optic Sensor

Rajasekaran,

Kothandaraman

2024

IEEE Access

View full text Add to dashboard Cite

A crucial component within any structural health monitoring system is a pipeline leak detection mechanism, which is vital for preventing avoidable water loss. Contemporary literature employs machine learning and deep learning for detecting pipeline leaks and cross-correlation for leak localization. The major drawbacks in the existing methodologies are that machine learning and deep learning methods need two different architectures for leak detection and localization, and the cross-correlation needs two sensors with a denoising technique. The primary objective of this paper is to deploy a unified architecture capable of executing both the detection and localization of a leak without any denoising technique and with a single sensor. The proposed technique utilizes the data collected using an Acousto-optic sensor with two different pressures. This paper proposes a novel custom one-dimensional time-series DenseNet for leak detection and localization. The proposed method gives better accuracies compared with the existing onedimensional DenseNet-121, three different one-dimensional convolutional neural networks (1DCNN), and cross-correlation for two different pressure datasets. The proposed method's processing time is thirteen times less than the existing one-dimensional DenseNet-121, with the observed average leak detection and localization accuracy of 99.08%. The results state that the proposed novel custom one-dimensional timeseries DenseNet accurately detects and localizes the leak with less time.

show abstract

“…The road ahead is replete with opportunities. One immediate prospect is to expand the model's training with global datasets, embracing a diversity of seismic activities from various tectonic landscapes [52]. This could make the model more universally applicable.…”

Section: F Future Prospects and Recommendationsmentioning

confidence: 99%

Deep Convolutional Neural Network for Accurate Prediction of Seismic Events

Turarbek,

Bektemesov,

Ongarbayeva

et al. 2023

IJACSA

View full text Add to dashboard Cite

In recent years, the realm of seismology has witnessed an increased integration of advanced computational techniques, seeking to enhance the precision and timeliness of earthquake predictions. The paper titled "Deep Convolutional Neural Network and Machine Learning Enabled Framework for Analysis and Prediction of Seismic Events" embarks on an ambitious exploration of this interstice, marrying the formidable prowess of Deep Convolutional Neural Networks (CNNs) with an array of machine learning algorithms. At the forefront of our investigation is the Deep CNN, known for its unparalleled capability to process spatial hierarchies and multi-dimensional seismic data. Accompanying this neural behemoth is LightGBM, a gradient boosting framework that offers superior speed and performance, especially with voluminous datasets. Additionally, conventional neural networks, noted for their adeptness in pattern recognition, offer a robust method to gauge the intricacies of seismic data. Our exploration doesn't halt here; the research delves deeper with Random Forest and Support Vector Machines (SVM), both renowned for their resilient performance in classification tasks. By amalgamating these diverse methodologies, this research crafts a multifaceted and synergistic framework. The culmination is a sophisticated tool poised to not only discern the minutiae of seismic activities with heightened accuracy but to predict forthcoming events with a degree of certainty previously deemed elusive. In this era of escalating seismic activities, our research offers a timely beacon, heralding a future where communities are better equipped to respond to the Earth's capricious tremors.

show abstract

Dense-Block Structured Convolutional Neural Network-Based Analytical Prediction System of Cutting Tool Wear

Cited by 15 publications

References 26 publications

Tool Wear State Identification Based on the IWOA-VMD Feature Selection Method

Tool Wear State Identification Based on the IWOA-VMD Feature Selection Method

A Novel Custom One-Dimensional Time-Series DenseNet for Water Pipeline Leak Detection and Localization Using Acousto-Optic Sensor

Deep Convolutional Neural Network for Accurate Prediction of Seismic Events

Contact Info

Product

Resources

About