In-process chatter detection in micro-milling using acoustic emission via machine learning classifiers

Sestito, Guilherme Serpa; Venter, Giuliana Sardi; Ribeiro, Kandice Suane Barros; Rodrigues, Alessandro Roger; Silva, Maíra Martins da

doi:10.1007/s00170-022-09209-w

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…Monitoring performed in real time using machine learning (ML) algorithms and neural networks (NN) present a model-free monitoring approach, which is suitable for complex systems that require a multiphysical modelling involving a great number of variables. Such monitoring may provide information on both the fault being monitored [14][15][16] and its specific intensity.…”

Section: Introductionmentioning

confidence: 99%

A hybrid machine learning model for in-process estimation of printing distance in laser Directed Energy Deposition

Ribeiro

Núñez²,

Venter³

et al. 2023

Preprint

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There are several parameters that highly influence material quality and printed shape in laser Directed Energy Deposition (L-DED) operations. These parameters are usually defined for an optimal combination of energy input (laser power, scanning speed) and material feed rate, providing ideal bead geometry and layer height to the printing setup. However, during printing, layer height can vary. Such variation affects the upcoming layers by changing the printing distance, inducing printing to occur in defocus zone then cumulatively increasing shape deviation. In order to address such issue, this paper proposes a novel intelligent hybrid method for in-process estimating the printing distance ( $Z_s$ ) from melt pool images acquired during L-DED. The proposed hybrid method uses transfer learning to combine pre-trained Convolutional Neural Network (CNN) and Support Vector Regression (SVR) for an accurate yet computationally fast methodology. A dataset with $2,700$ melt pool images was generated from the deposition of lines, at $60$ different values of $Z_s$, and used for training. The best hybrid algorithm trained performed with a Mean Average Error (MAE) of $0.266$ , which indicates an average target error of $6.7%$ . The deployment of this algorithm in an application dataset allowed the printing distance to be estimated and the final part geometry to be inferred from the data. Thus, the present method can aid on-line feedback control on the Z-axis increment, to regulate layer height, improving 3D shape geometry in L-DED.

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Section: Introductionmentioning

confidence: 99%

A hybrid machine learning model for in-process estimation of printing distance in laser Directed Energy Deposition

Ribeiro

Núñez²,

Venter³

et al. 2023

Preprint

Self Cite

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“…Traditional signal processing algorithms, however, heavily depend on the user experience to model the system and predict possible faults. Machine learning algorithms, on the other hand, have emerged as powerful tools for monitoring and controlling very complex processes [22][23][24]. By leveraging the capabilities of these algorithms, it becomes possible to analyze complex and high-dimensional data acquired during the process and extract valuable insights in real-time [25].…”

Section: Introductionmentioning

confidence: 99%

Real-time prediction of deposited bead width in L-DED using Semi-Supervised Transfer Learning

Mochi,

Núñez,

Ribeiro

et al. 2023

Preprint

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Laser Directed Energy Deposition (L-DED) is an additive manufacturing (AM) technology that offers unique advantages for creating and repairing metallic parts. The quality of the final print is highly dependent on the consistency of the printing parameters, and as a result, the deposited bead geometry along the printing pathway. Inconsistencies, particularly in the bead width, can produce defects between adjacent beads, increasing the material susceptibility to failure. Therefore, it is crucial to monitor and predict the printing parameters in real-time during the process. The objective of this study is to develop a machine learning model that accurately predicts the width of the deposited track in real-time. To achieve this, a dataset with approximately 12,379 melt pool images was used to train the algorithm and predict the width of the deposited track. Each one of the deposited track was measured by a profilometer at 10 points along the pathway, and intermediate labels were predicted using a spline curve, making the model semi-supervised. To implement the machine learning model, pre-trained frozen networks based on Convolutional Neural Networks (CNN) architectures VGG, ResNet, and DenseNet were employed, using transfer learning principles. These networks were integrated into a dense, fully connected network containing trainable parameters. The results demonstrate a good model fit, with a mean absolute error of 4.5% and a mean absolute error of 0.0358 mm. Moreover, the processing frequency of the best model, at 55 Hz, enables real-time control of the L-DED manufacturing process. It is important to highlight, however, that the VGG based model shows a frequency of 250 Hz and similar results. Thus, accurately predicting the width of the deposited bead has the potential to significantly improve the quality of the final part by reducing overall defects, such as lack of fusion and porosity, whenever this data is used as input to a feed-back control system. This is particularly valuable in industries where mechanical properties and fatigue life are critical, such as automotive, aerospace, and biomedical sectors. The models developed in this study offer a promising approach to effectively provide data to control the L-DED manufacturing process in real-time, enabling the reliable production of high-quality components associated with reduced manufacturing costs.

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“…More studies have been performed in milling using machine learning techniques, such as in the stability monitoring process Wang et al (2021b), Sestito et al (2022 or in the chatter detection via support vector machine classification Chen et al (2019). The XGBoost algorithm has also been used for heat transfer prediction in machining processes Qian et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Investigation on eXtreme Gradient Boosting for cutting force prediction in milling

Heitz,

He,

Ait-Mlouk

et al. 2023

J Intell Manuf

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In-process chatter detection in micro-milling using acoustic emission via machine learning classifiers

Cited by 12 publications

References 21 publications

A hybrid machine learning model for in-process estimation of printing distance in laser Directed Energy Deposition

A hybrid machine learning model for in-process estimation of printing distance in laser Directed Energy Deposition

Real-time prediction of deposited bead width in L-DED using Semi-Supervised Transfer Learning

Investigation on eXtreme Gradient Boosting for cutting force prediction in milling

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