Machine learning approaches for real-time monitoring and evaluation of surface roughness using a sensory milling tool

Möhring, Hans‐Christian; Eschelbacher, S.; Georgi, P.

doi:10.1016/j.procir.2021.09.045

Cited by 14 publications

(9 citation statements)

References 14 publications

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“…The network parameter configuration of ResNet50 The performance evaluation metrics of the classifier are mainly Accuracy, Precision, Recall, and F-Score [32] , which are defined as follows:…”

Section: Tablementioning

confidence: 99%

A monitoring method for surface roughness of γ-TiAl alloy based on deep learning of time–frequency diagram

Wu,

Liu,

Huang

et al. 2023

Int J Adv Manuf Technol

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γ-TiAl alloy is a typically difficult material to machine, and machining defects such as grain pull-out and material spalling are common during machining, resulting in component scrap.As a result, it is critical to investigate the real-time monitoring method of surface roughness during milling. A new model for predicting surface roughness is proposed in this article. Based on the idea of deep learning, the prediction problem of surface roughness is turned into a classification problem.The features of the force signal are extracted using a continuous wavelet transform, and the onedimensional signal is converted into a two-dimensional time-frequency diagram to obtain more information. The transfer learning mechanism is introduced to make the model run faster and improve prediction accuracy. The overfitting problem is solved and the model's generalization ability is improved through batch normalization and dropout layer, and the prediction accuracy is increased by 3%. The results show that the method has high recognition accuracy, with a maximum classification accuracy of 98% and an average accuracy of 96.12%, a 6% improvement over the traditional model, allowing it to accurately realize real-time monitoring of surface quality in milling processing.

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Section: Tablementioning

confidence: 99%

A monitoring method for surface roughness of γ-TiAl alloy based on deep learning of time–frequency diagram

Wu,

Liu,

Huang

et al. 2023

Int J Adv Manuf Technol

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“…Where the LSTM model shows superior performance at higher R a value, the 1-D CNN shows better-extracting features in lower R a ranges. Mohring and colleagues collected milling vibrational signals and used DL models such as CNN [116] to determine surface roughness. One of the most appealing features of DL is that it is vulnerable to minor changes in input data.…”

Section: Future Implementation and Application Perspectivesmentioning

confidence: 99%

Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review

et al. 2021

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The aim of manufacturing can be described as achieving the predefined high quality product in a short delivery time and at a competitive cost. However, it is unfortunately quite challenging and often difficult to ensure that certain quality characteristics of the products are met following the contemporary manufacturing paradigm, such as surface roughness, surface texture, and topographical requirements. Ultraprecision machining (UPM) requirements are quite common and essential for products and components with optical finishing, including larger and highly accurate mirrors, infrared optics, laser devices, varifocal lenses, and other freeform optics that can satisfy the technical specifications of precision optical components and devices without further post-polishing. Ultraprecision machining can provide high precision, complex components and devices with a nanometric level of surface finishing. Nevertheless, the process requires an in-depth and comprehensive understanding of the machining system, such as diamond turning with various input parameters, tool features that are able to alter the machining efficiency, the machine working environment and conditions, and even workpiece and tooling materials. The non-linear and complex nature of the UPM process poses a major challenge for the prediction of surface generation and finishing. Recent advances in Industry 4.0 and machine learning are providing an effective means for the optimization of process parameters, particularly through in-process monitoring and prediction while avoiding the conventional trial-and-error approach. This paper attempts to provide a comprehensive and critical review on state-of-the-art in-surfaces monitoring and prediction in UPM processes, as well as a discussion and exploration on the future research in the field through Artificial Intelligence (AI) and digital solutions for harnessing the practical UPM issues in the process, particularly in real-time. In the paper, the implementation and application perspectives are also presented, particularly focusing on future industrial-scale applications with the aid of advanced in-process monitoring and prediction models, algorithms, and digital-enabling technologies.

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“…Many studies have been conducted to improve productivity more by applying deep learning. In particular, previous studies [2][3][4][5][6][7][8][9][10][11] have extensively reviewed and presented features that can be used as inputs to deep learning to predict surface roughness. Surface roughness is a significant index to evaluate product quality and an indicator of product characteristics that include surface friction and fracture resistance.…”

Section: Introductionmentioning

confidence: 99%

“…To address these limitations, previous studies expanded and reviewed tools [4] and materials [5,6] as additional inputs along with operating parameters. On the other hand, in works [7][8][9][10][11], additional sensors were installed and used as inputs to acquire more informative data from the milling operations. Works [7,8] installed vision sensors to acquire image data, which were then converted to gray-scale levels to be used as inputs.…”

Section: Introductionmentioning

confidence: 99%

Causality-Driven Efficient Feature Selection for Deep-Learning-Based Surface Roughness Prediction in Milling Machines

Lee,

Chun,

Kang

2023

Mathematics

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This paper studies the application of artificial intelligence to milling machines, focusing specifically on identifying the inputs (features) required for predicting surface roughness. Previous studies have extensively reviewed and presented useful features for surface roughness prediction. However, applying research findings to actual operational factories can be challenging due to the additional costs of sensor installations and the diverse environments present in each factory setting. To address these issues, in this paper, we introduced effective features for predicting surface roughness in situations where additional sensors are not installed in the existing environment. These features include feed per tooth, Fz; material removal rate, Q; and the load information. These features are suitable for use in highly constrained environments where separate sensor installation is not required, making it possible to apply the research findings in various factory environments. Additionally, to efficiently select the optimal subset for surface roughness prediction among subsets formed by available features, we apply causality to the feature selection method, proposing an approach called causality-driven efficient feature selection. The experimental results demonstrate that the features introduced in this paper are quite suitable for predicting surface roughness and that the proposed feature selection approach is more effective and efficient compared to existing selection methods.

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Machine learning approaches for real-time monitoring and evaluation of surface roughness using a sensory milling tool

Cited by 14 publications

References 14 publications

A monitoring method for surface roughness of γ-TiAl alloy based on deep learning of time–frequency diagram

A monitoring method for surface roughness of γ-TiAl alloy based on deep learning of time–frequency diagram

Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review

Causality-Driven Efficient Feature Selection for Deep-Learning-Based Surface Roughness Prediction in Milling Machines

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