Predicting the evolution of sheet metal surface scratching by the technique of artificial intelligence

Li, Wei; Zhang, Liangchi; Chen, Xinping; Wu, Chuhan; Cui, Zhenxiang; Niu, Chao

doi:10.1007/s00170-020-06394-4

Cited by 24 publications

(12 citation statements)

References 29 publications

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“…The fuzzy clustering algorithm is certainly more e cient in its calculations which is consistent with [38], however, it is di cult to learn the input waveforms to output values where the intensities are intermittent, as galling occurs and then smooths out and then occurs again and carries on in this manner. There appears to be a discrepancy between the results of Table 3 It is not surprising fuzzy clustering performs less than the other two, supervised techniques as other work uses optimisation algorithms to improve accuracy and e ciency of the fuzzy clustering technique [11].…”

Section: Cart Analysismentioning

confidence: 95%

“…In summary, machining learning techniques have been applied to sensor data, such as strain data obtained from strain gauges, in sheet metal stamping. Also other machine learning techniques have been applied to distinguish the evolution of scratch forming with ball-on-disk sliding based on input parameters [11].…”

Section: Introductionmentioning

confidence: 99%

“…This paper's work was inspired from previous works where AE sensors have been used to study wear mechanisms and source location without complex signal processing and data orientated algorithms [7], [8], [10], [12], [13], [16]- [18]. Time-frequency techniques to study acoustic emission waveforms for the fault diagnosis of machining processes and the in-service operation of bearings has been previously investigated [9][10][11][12][13][14][15][16], [22]- [29]. Condition monitoring of stamping processes using acoustic emissions discussed in [1], [15] is applied with the work presented in [17,18] [30], [31] where Hilbert Huang Transform (HHT) provides a method to track the state from both stationary and non-stationary data.…”

Section: Introductionmentioning

confidence: 99%

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Application of machine learning for acoustic emissions waveform to classify galling wear on sheet metal stamping tools

Griffin

Shanbhag

Pereira

et al. 2021

Int J Adv Manuf Technol

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Galling wear in sheet metal stamping processes can degrade the product quality and adversely affect mass production. Studies have shown that acoustic emission sensors can be used to measure galling. In the literature, attempts have been made to correlate the acoustic emission features and galling wear in the sheet metal stamping process. However, there is very little attempt made to implement machine learning techniques to detect acoustic emission features that can classify non-galling and galling wear as well as provide additional wear-state information in the form of strong visualisations. In the rst part of the paper time domain and frequency domain analysis are used to determine the acoustic emission features that can be used for unsupervised classi cation. Due to galling wear progression on the stamping tools, the behaviour of acoustic emission waveform changes from stationary to a nonstationary state. The initial change in acoustic emission waveform behaviour due to galling wear initiation is very di cult to observe due to the ratio of change against the large data size of the waveform. Therefore, a time-frequency technique "Hilbert Huang Transform" is applied to the acoustic emission waveform as that is sensitive to change of wear state, and is used for the classi cation of 'non galling' and the 'transition of galling'. Also, the unsupervised learning algorithm fuzzy clustering is used as comparison against the supervised learning techniques. Despite not knowing a priori the wear state labels, fuzzy clustering is able to de ne three relatively accurate distinct classes: "unworn", "transition to galling", and "severe galling". In the second part of the paper, the acoustic emission features are used as an input to the supervised machine learning algorithms to classify acoustic emission features related to non-galling and galling wear. An accuracy of 96% was observed for the prediction of non-galling and galling wear using Classi cation, Regression Tree (CART) and Neural Network techniques. In the last part, a reduced Short Time Fourier Transform of top 10 absolute maximum component acoustic emission feature sets that correlates to wear measurement data "pro le depth" is used to train and test supervised Neural Network and CART algorithms. The algorithms predicted the pro le depth of 530 unseen parts (530 unseen cases), which did not have any associated labelled depth data. This shows the power of using machine learning techniques that can use a small data training set to provide additional predicted wear-state on a much larger data set. Furthermore, the machine learning techniques presented in this paper can be used further to develop a real-time measurement system to detect the transition of galling wear from measured acoustic emission features.

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Section: Cart Analysismentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of machine learning for acoustic emissions waveform to classify galling wear on sheet metal stamping tools

Griffin

Shanbhag

Pereira

et al. 2021

Int J Adv Manuf Technol

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show abstract

“…2 c–e, were identified based on the h max . This is because h max plays an important role in determining the severity of scratching damage [ 35 , 36 ]. Overall, the database with a total of 10,500 surface images was identified by ten labels, as shown in Table 2 .…”

Section: Image Database Of Surface Scratchesmentioning

confidence: 99%

A new lightweight deep neural network for surface scratch detection

Zhang²,

Wu³

et al. 2022

Int J Adv Manuf Technol

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This paper aims to develop a lightweight convolutional neural network, WearNet , to realise automatic scratch detection for components in contact sliding such as those in metal forming. To this end, a large surface scratch dataset obtained from cylinder-on-flat sliding tests was used to train the WearNet with appropriate training parameters such as learning rate, gradient algorithm and mini-batch size. A comprehensive investigation on the network response and decision mechanism was also conducted to show the capability of the developed WearNet . It was found that compared with the existing networks, WearNet can realise an excellent classification accuracy of 94.16% with a much smaller model size and faster detection speed. Besides, WearNet outperformed other state-of-the-art networks when a public image database was used for network evaluation. The application of WearNet in an embedded system further demonstrated such advantages in the detection of surface scratches in sheet metal forming processes.

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“…The AI technology leads the investigations on big data processing in metal forming, spanning the material design [78], forming force prediction [77], tribological characteristics [78,79] and product surface quality [80], geometrical accuracy [81], process formability [82], tool path design and generation [83] (especially for incremental sheet metal forming [84]), product [85] and die designs [86], in-process defect monitoring [87], etc. Therein, various AI methods, especially machine learning (ML) algorithms, including the artificial neural network (ANN) algorithm [85], support vector machine (SVM) [78], quantum-behaved particle swarm optimisation (QPSO) algorithm [80], multilayer perceptron (MLP) algorithm [88] and convolutional neural networks (CNNs) [83], are widely utilised. In terms of the applications of AI in sheet metal forming, supervised ML is still the predominant AI method [77].…”

Section: Big Datamentioning

confidence: 99%

Industry 4.0 in Metal Forming Industry Towards Automotive Applications: A Review

Liu

Dhawan

Shen

et al. 2022

IJAMM

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Review Industry 4.0 in Metal Forming Industry Towards Automotive Applications: A Review Heli Liu 1,2, Saksham Dhawan 1,2, Merrill Shen 1, Kangan Chen 1, Vincent Wu 3, and Liliang Wang 1,2,* 1 Department of Mechanical Engineering, Imperial College London, London, SW7 2AZ, UK 2 SmartForming Research Base, Imperial College London, London, SW7 2AZ, UK 3 School of Informatics, The University of Edinburgh, Edinburgh, EH8 9LE, UK * Correspondence: liliang.wang@imperial.ac.uk Received: 23 September 2022 Accepted: 18 November 2022 Published: 18 December 2022 Abstract: Industry 4.0 is shaping the metal forming industry. The ongoing key Industry 4.0 technologies, including the industrial cyber-physical system (I-CPS), industrial internet of things (I-IoT), digital twin (DT), big data (BD) and cloud computing (CC), are expected to improve every stage during the metal forming processes, including the supply chains, raw material provision, tool design and manufacture, forming operations, energy consumption, cost, quality control and customer services. Here, we review the development and implementations of these key Industry 4.0 technologies in the metal forming industry. Based on the discussion of the opportunities and challenges of Industry 4.0 technologies in metal forming, this review provides some perspectives of future metal forming research directions towards automotive applications.

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Predicting the evolution of sheet metal surface scratching by the technique of artificial intelligence

Cited by 24 publications

References 29 publications

Application of machine learning for acoustic emissions waveform to classify galling wear on sheet metal stamping tools

Application of machine learning for acoustic emissions waveform to classify galling wear on sheet metal stamping tools

A new lightweight deep neural network for surface scratch detection

Industry 4.0 in Metal Forming Industry Towards Automotive Applications: A Review

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