Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: A review

Pandiyan, Vigneashwara; Shevchik, Sergey; Wasmer, Kilian; Castagne, Sylvie; Tjahjowidodo, Tegoeh

doi:10.1016/j.jmapro.2020.06.013

Cited by 75 publications

(24 citation statements)

References 172 publications

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“…CNN's are well known as a first applied method for tool wear prediction compared to other DL methods such as recurrent neural network (RNN), gated neural network (GNN) and long short-term memory (LSTM) [10,11,21,26]. CNN can have different architectures, and it depends based on the problem at hand.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…The abrasive belt grinding is a modification of the traditional rigid grinding process. However, the advantage of belt grinding over traditional grinding lies in its ease to uniformly machine any intricate geometries for workpieces [11,12]. The polymer wheel backing of the belt grinding tool on which the belt embedded with abrasive grains rests enables conformability with the intricate surfaces [13].…”

Section: Introductionmentioning

confidence: 99%

“…The polymer wheel backing of the belt grinding tool on which the belt embedded with abrasive grains rests enables conformability with the intricate surfaces [13]. Due to the nature of the polymer backing's compliant, the process is highly nonlinear and dependent on the process parameters [11,14]. The belt grinding tool consists of three primary parts, namely the driving mechanism, the polymer wheel and the belt itself.…”

Section: Introductionmentioning

confidence: 99%

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A CNN Prediction Method for Belt Grinding Tool Wear in a Polishing Process Utilizing 3-Axes Force and Vibration Data

et al. 2021

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This paper presents a tool wear monitoring methodology on the abrasive belt grinding process using vibration and force signatures on a convolutional neural network (CNN). A belt tool typically has a random orientation of abrasive grains and grit size variation for coarse or fine material removal. Degradation of the belt condition is a critical phenomenon that affects the workpiece quality during grinding. This work focuses on the identifation and the study of force and vibrational signals taken from sensors along an axis or combination of axes that carry important information of the contact conditions, i.e., belt wear. Three axes of the two sensors are aligned and labelled as X-axis (parallel to the direction of the tool during the abrasive process), Y-axis (perpendicular to the direction of the tool during the abrasive process) and Z-axis (parallel to the direction of the tool during the retract movement). The grinding process was performed using a customized abrasive belt grinder attached to a multi-axis robot on a mild-steel workpiece. The vibration and force signals along three axes (X, Y and Z) were acquired for four discrete sequential belt wear conditions: brand-new, 5-min cycle time, 15-min cycle time, and worn-out. The raw signals that correspond to the sensor measurement along the different axes were used to supervisedly train a 10-Layer CNN architecture to distinguish the belt wear states. Different possible combinations within the three axes of the sensors (X, Y, Z, XY, XZ, YZ and XYZ) were fed as inputs to the CNN model to sort the axis (or combination of axes) in the order of distinct representation of the belt wear state. The CNN classification results revealed that the combination of the XZ-axes and YZ-axes of the accelerometer sensor provides more accurate predictions than other combinations, indicating that the information from the Z-axis of the accelerometer is significant compared to the other two axes. In addition, the CNN accuracy of the XY-axes combination of dynamometer outperformed that of other combinations.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A CNN Prediction Method for Belt Grinding Tool Wear in a Polishing Process Utilizing 3-Axes Force and Vibration Data

et al. 2021

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“…Finally, sliding interaction between asperities results in instantaneous loading and unloading, existing for a few microseconds, which can be picked up only by AE sensors. This is due to the combination of high sensitivity and temporal resolution, making the AE sensing technique preferred as compared to other techniques and sensors [43].…”

Section: Introductionmentioning

confidence: 99%

Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm

et al. 2021

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Functional surfaces in relative contact and motion are prone to wear and tear, resulting in loss of efficiency and performance of the workpieces/machines. Wear occurs in the form of adhesion, abrasion, scuffing, galling, and scoring between contacts. However, the rate of the wear phenomenon depends primarily on the physical properties and the surrounding environment. Monitoring the integrity of surfaces by offline inspections leads to significant wasted machine time. A potential alternate option to offline inspection currently practiced in industries is the analysis of sensors signatures capable of capturing the wear state and correlating it with the wear phenomenon, followed by in situ classification using a state-of-the-art machine learning (ML) algorithm. Though this technique is better than offline inspection, it possesses inherent disadvantages for training the ML models. Ideally, supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid biasing. The collection of such a dataset is very cumbersome and expensive in practice, as in real industrial applications, the malfunction period is minimal compared to normal operation. Furthermore, classification models would not classify new wear phenomena from the normal regime if they are unfamiliar. As a promising alternative, in this work, we propose a methodology able to differentiate the abnormal regimes, i.e., wear phenomenon regimes, from the normal regime. This is carried out by familiarizing the ML algorithms only with the distribution of the acoustic emission (AE) signals captured using a microphone related to the normal regime. As a result, the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new, unseen signal arrives. To achieve this goal, a generative convolutional neural network (CNN) architecture based on variational auto encoder (VAE) is built and trained. During the validation procedure of the proposed CNN architectures, we were capable of identifying acoustics signals corresponding to the normal and abnormal wear regime with an accuracy of 97% and 80%. Hence, our approach shows very promising results for in situ and real-time condition monitoring or even wear prediction in tribological applications.

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“…The precision of grinding process depends on the precise material removal model which are usually affected by many factors, such as grinding parameters, workpiece parameters and abrasive belt wear status [6]. In the grinding process, grinding parameters and workpiece parameters are constant values, while the abrasive belt wear state is a dynamic amount of time variation [7]. Therefore, the accurate material removal model mainly depends on the accurate monitoring of the wear state of abrasive belt.…”

Section: Introductionmentioning

confidence: 99%

A Novel Monitoring Method for Belt Wear State Based on Machine Vision and Image-processing Under Varying Grinding Parameters

Wang¹,

Zhang²,

Ren³

et al. 2021

Preprint

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The wear state of abrasive belt is one of the important factors affecting the grinding precision of belt grinding processes. Accurate monitoring of abrasive belt wear can not only provide the basis for accurate material removal model to improve grinding accuracy, but also can replace the belt to avoid surface burn in time. However, most of the existing abrasive belt wear monitoring methods are only suitable for monitoring the belt wear state under specific grinding parameters, are not universal. This paper introduces a method of belt wear state monitoring based on machine vision and image-processing. All the surface images of the belt were obtained from the new belt to the worn-out of the belt by a non-contact electron microscope. The features of abrasive belt surface images are extracted from RGB color space and wavelet texture. By analyzing the trendency of the extracted features in the whole grinding process, the wear state is divided into three categories. Three image features related to the wear state are selected: the first order distance of color component R, the entropy of horizontal subgraph, and vertical subgraph of texture feature. Based on the selected features and the random forest classification algorithm, the wear state classifier of abrasive belt is established. The performance of the classifier is verified and evaluated by using the data subset of different images. The results show that the proposed method has high recognition accuracy for the belt wear state, and the accuracy can reach 99% in the accelerated wear stage. The proposed method is suitable for the monitoring of the belt wear state by the surface images of the abrasive belt measured under different grinding parameters and different measurement parameters.

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Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: A review

Cited by 75 publications

References 172 publications

A CNN Prediction Method for Belt Grinding Tool Wear in a Polishing Process Utilizing 3-Axes Force and Vibration Data

A CNN Prediction Method for Belt Grinding Tool Wear in a Polishing Process Utilizing 3-Axes Force and Vibration Data

Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm

A Novel Monitoring Method for Belt Wear State Based on Machine Vision and Image-processing Under Varying Grinding Parameters

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