Artificial intelligence applied to automatic supervision, diagnosis and control in sheet metal stamping processes

Garcı́a, C.

doi:10.1016/j.jmatprotec.2005.02.031

Cited by 32 publications

(18 citation statements)

References 12 publications

(12 reference statements)

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“…AI is also considered as a computer's ability to recognize patterns and take actions based on available data and statistical models. Artificial intelligence has shown superior performance in abundant fields, including voice (e.g., Amazon's Alexa, Apple's Siri and Google Assistant) and pattern recognition algorithms [27], monitoring processes in industries [28][29][30], fault detection [31,32], forecasting [33,34] and especially in the health care sector to improve treatment process [35][36][37].…”

Section: Ai Andmentioning

confidence: 99%

Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future?

et al. 2020

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Artificial intelligence (AI) is a rapidly growing technological phenomenon that all industries wish to exploit to benefit from efficiency gains and cost reductions. At the macrolevel, AI appears to be capable of replacing humans by undertaking intelligent tasks that were once limited to the human mind. However, another school of thought suggests that instead of being a replacement for the human mind, AI can be used for intelligence augmentation (IA). Accordingly, our research seeks to address these different views, their implications, and potential risks in an age of increased artificial awareness. We show that the ultimate goal of humankind is to achieve IA through the exploitation of AI. Moreover, we articulate the urgent need for ethical frameworks that define how AI should be used to trigger the next level of IA.

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Section: Ai Andmentioning

confidence: 99%

Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future?

et al. 2020

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“…However, their algorithm focuses only on detecting splits and cracks, and thus, other possible defects (such as inclusions) may not be detected. Garcia (2005) provided a more complete model combining image processing techniques and fuzzy logic to detect cracks and wrinkles in stamped parts in real-time with good precision. However, an obvious limitation in Garcia's approach is that it requires changes in the work station, which cannot always be attained on a shop floor.…”

Section: Related Sheet Metal Fault Detection Techniquesmentioning

confidence: 99%

Real-time fault detection in manufacturing environments using face recognition techniques

Megahed

Camelio

2010

J Intell Manuf

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New image processing techniques as well digital image capture equipment provide an opportunity for fast detection and diagnosis of quality problems in manufacturing environments compared with traditional dimensional measurement techniques. This paper proposes a new use of image processing to detect in real-time quality faults using images traditionally obtained to guide manufacturing processes. The proposed method utilizes face recognition tools to eliminate the need of specific feature detection on determining out-of-specification parts. The focus of the proposed methodology is on computational efficiency to ensure that the algorithm runs in real time in high volume manufacturing environments. The algorithm is trained with previously classified images. New images are then classified into two groups, healthy and unhealthy. This paper proposes a method that combines Discrete Cosine Transform with Fisher's Linear Discriminant Analysis to detect faults, such as cracks, directly from aluminum stamped parts.

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“…Hence the requirement for other sensing technologies especially those combined as a multispectral approach. Garcia [10] used another technique based on the use of digital camera and applied optimised wavelet to distinguish wrinkles and surface roughness by extracting 2D images. In summary, machining learning techniques have been applied to sensor data, such as strain data obtained from strain gauges, in sheet metal stamping.…”

Section: Introductionmentioning

confidence: 99%

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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Artificial intelligence applied to automatic supervision, diagnosis and control in sheet metal stamping processes

Cited by 32 publications

References 12 publications

Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future?

Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future?

Real-time fault detection in manufacturing environments using face recognition techniques

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

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