Tool wear prediction using long short-term memory variants and hybrid feature selection techniques

Sayyad, S. S.; Kumar, Satish; Bongale, Arunkumar; Kotecha, Ketan; Selvachandran, Ganeshsree; Suganthan, Ponnuthurai Nagaratnam

doi:10.1007/s00170-022-09784-y

Cited by 24 publications

(7 citation statements)

References 48 publications

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“…Also, for the case of adding a slightly different process, such as milling, the procedure of creating transfer active learning is much more complicated. So, in the case of a dataset that can be quite complex in terms of predictability (Company C Dataset [63]), that has also been used previously in the literature [64], force signals (or time series, equivalently) constitute the force measurements. It is noted that taking simple metrics such as mean square value, mean value, or standard deviation, the classes defined by the tool wear levels are not separable in a straightforward way, at least in the context of utilizing the least data.…”

Section: Discussionmentioning

confidence: 99%

Learning More with Less Data in Manufacturing: The Case of Turning Tool Wear Assessment through Active and Transfer Learning

Papacharalampopoulos,

Alexopoulos,

Catti

et al. 2024

Processes

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Monitoring tool wear is key for the optimization of manufacturing processes. To achieve this, machine learning (ML) has provided mechanisms that work adequately on setups that measure the cutting force of a tool through the use of force sensors. However, given the increased focus on sustainability, i.e., in the context of reducing complexity, time and energy consumption required to train ML algorithms on large datasets dictate the use of smaller samples for training. Herein, the concepts of active learning (AL) and transfer learning (TL) are simultaneously studied concerning their ability to meet the aforementioned objective. A method is presented which utilizes AL for training ML models with less data and then it utilizes TL to further reduce the need for training data when ML models are transferred from one industrial case to another. The method is tested and verified upon an industrially relevant scenario to estimate the tool wear during the turning process of two manufacturing companies. The results indicated that through the application of the AL and TL methodologies, in both companies, it was possible to achieve high accuracy during the training of the final model (1 and 0.93 for manufacturing companies B and A, respectively). Additionally, reproducibility of the results has been tested to strengthen the outcomes of this study, resulting in a small standard deviation of 0.031 in the performance metrics used to evaluate the models. Thus, the novelty presented in this paper is the presentation of a straightforward approach to apply AL and TL in the context of tool wear classification to reduce the dependency on large amounts of high-quality data. The results show that the synergetic combination of AL with TL can reduce the need for data required for training ML models for tool wear prediction.

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Section: Discussionmentioning

confidence: 99%

Learning More with Less Data in Manufacturing: The Case of Turning Tool Wear Assessment through Active and Transfer Learning

Papacharalampopoulos,

Alexopoulos,

Catti

et al. 2024

Processes

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“…Machine health monitoring can be aided by anomaly detection [8] . Identifying the initial abnormality's timestamp may offer further information about the machinery's remaining useful life (RUL) [9] . Supervised anomaly identification techniques require awareness of the instance labels ahead of time.…”

Section: Methods Validationmentioning

confidence: 99%

Anomaly-informed remaining useful life estimation (AIRULE) of bearing machinery using deep learning framework

Kamat,

Kumar,

Patil

et al. 2024

MethodsX

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“…Another method used for feature selection is the RF method. RF is the embedded feature selection method that lowers the danger of overfitting and performs quicker operations by overcoming the limitations of wrapper and filter feature selection methods [ 36 ]. RF is made up of a number of decision trees that were created by randomly extracting characteristics from the data.…”

Section: Proposed Methodologymentioning

confidence: 99%

Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time–Frequency-Based Features and Deep Learning Models

Sayyad

Kumar

Bongale

et al. 2023

Sensors

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The milling machine serves an important role in manufacturing because of its versatility in machining. The cutting tool is a critical component of machining because it is responsible for machining accuracy and surface finishing, impacting industrial productivity. Monitoring the cutting tool’s life is essential to avoid machining downtime caused due to tool wear. To prevent the unplanned downtime of the machine and to utilize the maximum life of the cutting tool, the accurate prediction of the remaining useful life (RUL) cutting tool is essential. Different artificial intelligence (AI) techniques estimate the RUL of cutting tools in milling operations with improved prediction accuracy. The IEEE NUAA Ideahouse dataset has been used in this paper for the RUL estimation of the milling cutter. The accuracy of the prediction is based on the quality of feature engineering performed on the unprocessed data. Feature extraction is a crucial phase in RUL prediction. In this work, the authors considers the time–frequency domain (TFD) features such as short-time Fourier-transform (STFT) and different wavelet transforms (WT) along with deep learning (DL) models such as long short-term memory (LSTM), different variants of LSTN, convolutional neural network (CNN), and hybrid models that are a combination of CCN with LSTM variants for RUL estimation. The TFD feature extraction with LSTM variants and hybrid models performs well for the milling cutting tool RUL estimation.

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Tool wear prediction using long short-term memory variants and hybrid feature selection techniques

Cited by 24 publications

References 48 publications

Learning More with Less Data in Manufacturing: The Case of Turning Tool Wear Assessment through Active and Transfer Learning

Learning More with Less Data in Manufacturing: The Case of Turning Tool Wear Assessment through Active and Transfer Learning

Anomaly-informed remaining useful life estimation (AIRULE) of bearing machinery using deep learning framework

Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time–Frequency-Based Features and Deep Learning Models

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