Development Trends in Automation and Metrology

Bauer, Jorge M.; Bas, G.; Durakbasa, Numan M.; Kopacek, Peter

doi:10.1016/j.ifacol.2015.12.077

Cited by 17 publications

(7 citation statements)

References 3 publications

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“…To obtain stable measurements, it is necessary to minimize the impact of these factors [10][11][12][13]. One of the ways to stabilize the technological process is automated measurements.…”

Section: Discussionmentioning

confidence: 99%

Stabilization of calibration metrological characteristics of technological inspection tools

Petrovsky

Bukhtoyarov

Savitskaya

2016

2016 2nd International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

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Abstract-The paper presents analysis of the results of template 873 measurements which is a tool used for inspection in wagon depo. The data obtained were analyzed to confirm the stability of inspection results. It was shown that the measurement and determination of the final result by calculating the average value causes misleading results. Also random factors which affect measurements are presented. The subjective factor in this process is from the calibrator. To make measurement process more stable we need to reduce the effect of these factors. One of the ways to improve measurement process and make it more stable is its automatization. Improving the accuracy of measurements is one of the main objectives concerning this issue.

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“…To obtain stable measurements, it is necessary to minimize the impact of these factors [10][11][12][13]. One of the ways to stabilize the technological process is automated measurements.…”

Section: Discussionmentioning

confidence: 99%

Stabilization of calibration metrological characteristics of technological inspection tools

Petrovsky

Bukhtoyarov

Savitskaya

2016

2016 2nd International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

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“…In particular, non contact artificial vision technologies should be flexible and scalable for those industrial applications that require high-precision operations in real uncontrolled environments [9][10][11]. These systems have reported good success rates in productive processes [12,13] and in other sectors [14][15][16]. For example, vision systems in the civil infrastructure field [17][18][19][20] have been successfully employed to capture images of large structures in real uncontrolled open environments.…”

Section: Related Workmentioning

confidence: 99%

“…can be difficult and computationally costly. Thus taking the most precise measurements possible requires good resolution images [12].…”

Section: Related Workmentioning

confidence: 99%

Improving the competitiveness of aircraft manufacturing automated processes by a deep neural network

Ruiz¹,

Díaz²,

González³

et al. 2023

ICA

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The accuracy and reliability requirements in aerospace manufacturing processes are some of the most demanding in industry. One of the first steps is detection and precise measurement using artificial vision models to accurately process the part. However, these systems require complex adjustments and do not work correctly in uncontrolled scenarios, but require manual supervision, which reduces the autonomy of automated machinery. To solve these problems, this paper proposes a convolutional neural network for the detection and measurement of drills and other fixation elements in an uncontrolled industrial manufacturing environment. In addition, a fine-tuning algorithm is applied to the results obtained from the network, and a new metric is defined to evaluate the quality of detection. The efficiency and robustness of the proposed method were verified in a real production environment, with 99.7% precision, 97.6% recall and an overall quality factor of 96.0%. The reduction in operator intervention went from 13.3% to 0.6%. The presented work will allow the competitiveness of aircraft component manufacturing processes to increase, and working environments will be safer and more efficient.

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“…One fundamental part of referencing these processes is the detection and measurement of the parts in their real positions by artificial vision systems, which have been used in different industrial sectors [6,[11][12][13][14][15]. Non-contact methods are being increasingly employed during aeronautical manufacturing processes as enabling technology in the fields of verification [6], metrology [7,10,13] and quality analysis [11] are implemented, both for their excellent inherent performance and the saved cost of accelerating production rates to meet production demands. However, their flexibility and reconfiguration must balance with accuracy and reliability to find suitable applications.…”

Section: Introductionmentioning

confidence: 99%

“…or incorrect lighting (shadows, changes in lighting, etc.). These problems entail image processing that can become computationally expensive, especially given the resolution required by images to obtain measurements with the best possible precision [13].…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network

Ruiz

Torres²,

Gómez³

et al. 2020

Applied Sciences

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The aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility.

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Development Trends in Automation and Metrology

Cited by 17 publications

References 3 publications

Stabilization of calibration metrological characteristics of technological inspection tools

Stabilization of calibration metrological characteristics of technological inspection tools

Improving the competitiveness of aircraft manufacturing automated processes by a deep neural network

Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network

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