Tool Wear Control through Cognitive Paradigms

D’Addona, Doriana M.; Matarazzo, D.; Ullah, Aman; Teti, Roberto

doi:10.1016/j.procir.2015.06.040

Cited by 26 publications

(5 citation statements)

References 12 publications

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“…Here, we adopt one open source dataset: dynamometer, accelerometer and acoustic emission data sampled from high-speed Computer Numerical Control (CNC) milling machine cutters (the dataset has been kindly provided at ). The corresponding task is defined as the estimation of tool wear conditions based on sensory signals, i.e., tool wear depth [23,24]. In our setting, this problem has been transformed into a regression problem with sequential data, in which each sequential datum, i.e., sensory data, represents one certain tool wear condition that corresponds to the actual tool wear width.…”

Section: Introductionmentioning

confidence: 99%

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

Zhao

Yan

Wang

et al. 2017

Sensors

591

302

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Abstract:In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks (LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.

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Section: Introductionmentioning

confidence: 99%

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

Zhao

Yan

Wang

et al. 2017

Sensors

591

302

View full text Add to dashboard Cite

show abstract

“…It is crucial for automatic, or so-called unmanned machining, to detect wear of a cutting tool edge in time to prevent negative effects on the quality of a machined surface [ 6 , 7 ]. Excessive cutting tool wear can also lead to serious workpiece or machine damage [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process

Brili

Ficko

Klančnik

2021

Sensors

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This article presents a control system for a cutting tool condition supervision, which recognises tool wear automatically during turning. We used an infrared camera for process control, which—unlike common cameras—captures the thermographic state, in addition to the visual state of the process. Despite challenging environmental conditions (e.g., hot chips) we protected the camera and placed it right up to the cutting knife, so that machining could be observed closely. During the experiment constant cutting conditions were set for the dry machining of workpiece (low alloy carbon steel 1.7225 or 42CrMo4). To build a dataset of over 9,000 images, we machined on a lathe with tool inserts of different wear levels. Using a convolutional neural network (CNN), we developed a model for tool wear and tool damage prediction. It determines the state of a cutting tool automatically (none, low, medium, high wear level), based on thermographic process data. The accuracy of classification was 99.55%, which affirms the adequacy of the proposed method. Such a system enables immediate action in the case of cutting tool wear or breakage, regardless of the operator’s knowledge and competence.

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“…It is widely used in fault prognostics, e.g., RUL estimation of bearings [9][10][11] and tool wear recognition [21]. Here, a NN is adopted to train our model and predict the short-term tendency of feature series.…”

Section: Neural Network For Short-term Tendency Predictionmentioning

confidence: 99%

Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox

et al. 2016

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Abstract:Predicting the remaining useful life (RUL) of critical subassemblies can provide an advanced maintenance strategy for wind turbines installed in remote regions. This paper proposes a novel prognostic approach to predict the RUL of bearings in a wind turbine gearbox. An artificial neural network (NN) is used to train data-driven models and to predict short-term tendencies of feature series. By combining the predicted and training features, a polynomial curve reflecting the long-term degradation process of bearings is fitted. Through solving the intersection between the fitted curve and the pre-defined threshold, the RUL can be deduced. The presented approach is validated by an operating wind turbine with a faulty bearing in the gearbox.

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Tool Wear Control through Cognitive Paradigms

Cited by 26 publications

References 12 publications

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process

Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox

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