Theoretical considerations on application of artificial intelligence in coordinate metrology

Wieczorowski, Michał; Kucharski, D.; Śniatała, Pawel; Królczyk, Grzegorz M.; Pawlus, Paweł; Gapiński, Bartosz

doi:10.1109/nanofim54124.2021.9737344

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…The measurement uncertainty of a particular system depends on its technical design, technical execution and proper use. This uncertainty can be reduced by lowering the sensitivity threshold, reducing systematic and random uncertainty, increasing temporal stability and independence from observer skill [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

Metrological Aspects of Assessing Surface Topography and Machining Accuracy in Diagnostics of Grinding Processes

et al. 2023

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The paper presents probabilistic aspects of diagnostics of grinding processes with consideration of metrological aspects of evaluation of topography of machined surfaces and selected problems of assessment of machining accuracy. The processes of creating the geometric structure of the ground surface are described. It was pointed out that the distribution of features important for process diagnostics depends on the mechanism of cumulative effects of random disturbances. Usually, there is a multiplicative mechanism or an additive mechanism of the component vectors of relative displacements of the tool and workpiece. The paper describes a method for determining the classification ability of specific parameters used to evaluate stereometric features of ground surfaces. It is shown that the ability to differentiate the geometric structure of a certain set of surfaces using a selected parameter depends on the geometric mean of the differences in normalized and sorted, consecutive values of this parameter. A methodology is presented for evaluating the ability of various parameters to distinguish different geometric structures of surfaces. Further, on the basis of analyses of a number of grinding processes, a methodology was formulated for proceeding leading to a comprehensive evaluation of machining accuracy and forecasting its results. It was taken into account that in forecasting the accuracy of grinding, it is necessary to determine the deviations, arising under the conditions of multiplicative interaction of the effects of various causes of inaccuracy. Examples are given of processes in which, due to the deformation of the technological system, dependent on the position of the zone and machining force, varying temperature fields and tool wear, the distributions of dimensional deviations are not the realization of stationary processes. It was emphasized that on the basis of the characteristics of the dispersion of the deviation value in the sum set of elements, it is not possible to infer its causes. Only the determination of the “instantaneous” values of the deviation dispersion parameters allows a more complete diagnosis of the process.

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Section: Methodsmentioning

confidence: 99%

Metrological Aspects of Assessing Surface Topography and Machining Accuracy in Diagnostics of Grinding Processes

et al. 2023

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“…The use of artificial intelligence or AI in metrology is a topic that is receiving increasing attention [52]. It is predicted that it will simply become a necessity in the near future, as the cost of hiring an employee and fully preparing him or her for modern measurement workstations is a time-consuming process.…”

Section: Information Technologies In Metrology 40mentioning

confidence: 99%

Towards Metrology 4.0 in Dimensional Measurements

Wieczorowski¹,

Trojanowska²

2023

Journal of Machine Engineering

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The paper presents the transformations taking place in length and angle metrology related to Metrology 4.0, a measurement strategy resulting from Industry 4.0. Metrology in an industrial conditions is gradually focusing more and more on advanced measurement systems. The coming reality will see the development of communication between systems and their components, as well as the individual sensors belonging to them. The Internet of Things and artificial intelligence as well as the possibility of using augmented or virtual reality will play a momentous role. The demand for these technologies results in the development of new specialized software and hardware solutions, the use and availability of which are diametrically different compared to the past. Also, the use of AI and cybersecurity in metrology is a topic that is receiving increasing attention. Metrology 4.0 is therefore becoming a very important part of the functioning of industry, changing the philosophy and organization of measurements carried out on the basis of new measurement techniques.

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“…In recent years, with the development of artificial intelligence, it has been more and more widely used in the field of measurement [22]. In order to improve the efficiency and quality of measurement, more and more scholars have searched to show the relationship between morphological features and parameters [23,24], and even proposed new parameters.…”

Section: Sample Preparationmentioning

confidence: 99%

Deep learning classification and recognition method for milling surface roughness combined with simulation data

Lu,

Yi,

Shu

et al. 2022

Metrology and Measurement Systems

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To address the problem that a deep neural network needs a sufficient number of training samples to have a good prediction performance, this paper firstly used the Z-Map algorithm to generate a simulated profile of the milling surface and construct an optical simulation model of surface imaging to supplement the training sample size of the neural network. Then the Deep CORAL model was used to match the textures of the simulated samples and the actual samples across domains to solve the problem that the simulated samples were not in the same domain as the actual milling samples. Experimental results have shown that high texture matching could be achieved between optical simulation images and actual images, laying the foundation for expanding the actual milled workpiece images with the simulation images. The deep convolutional neural model Xception was used to predict the classification of six classes of data sets with the inclusion of simulation images, and the accuracy was improved from 86.48% to 92.79% compared with the model without the inclusion of simulation images. The proposed method solves the problem of the need for a large number of samples for deep neural networks and lays the foundation for similar methods to predict surface roughness for different machining processes.

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Theoretical considerations on application of artificial intelligence in coordinate metrology

Cited by 8 publications

References 21 publications

Metrological Aspects of Assessing Surface Topography and Machining Accuracy in Diagnostics of Grinding Processes

Metrological Aspects of Assessing Surface Topography and Machining Accuracy in Diagnostics of Grinding Processes

Towards Metrology 4.0 in Dimensional Measurements

Deep learning classification and recognition method for milling surface roughness combined with simulation data

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