2020
Bustillo, Andrés; Pimenov, Danil Yurievich; Mia, Mozammel; Kapłonek, Wojciech
Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth Journal Article
In: Journal of Intelligent Manufacturing, 2020, ISSN: 0956-5515.
Abstract | Links | BibTeX | Tags: Cutting power, Face milling, Flatness deviation, Random forest, SMOTE, tool condition monitoring, Tool life, Wear
@article{Bustillo2020b,
title = {Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth},
author = {Andrés Bustillo and Danil Yurievich Pimenov and Mozammel Mia and Wojciech Kapłonek},
url = {https://link.springer.com/article/10.1007/s10845-020-01645-3},
doi = {https://doi.org/10.1007/s10845-020-01645-3},
issn = {0956-5515},
year = {2020},
date = {2020-09-03},
journal = {Journal of Intelligent Manufacturing},
abstract = {The acceptance of the machined surfaces not only depends on roughness parameters but also in the flatness deviation (Δfl). Hence, before reaching the threshold of flatness deviation caused by the wear of the face mill, the tool inserts need to be changed to avoid the expected product rejection. As current CNC machines have the facility to track, in real-time, the main drive power, the present study utilizes this facility to predict the flatness deviation—with proper consideration to the amount of wear of cutting tool insert’s edge. The prediction of deviation from flatness is evaluated as a regression and a classification problem, while different machine-learning techniques like Multilayer Perceptrons, Radial Basis Functions Networks, Decision Trees and Random Forest ensembles have been examined. Finally, Random Forest ensembles combined with Synthetic Minority Over-sampling Technique (SMOTE) balancing technique showed the highest performance when the flatness levels are discretized taking into account industrial requirements. The SMOTE balancing technique resulted in a very useful strategy to avoid the strong limitations that small experiment datasets produce in the accuracy of machine-learning models.},
keywords = {Cutting power, Face milling, Flatness deviation, Random forest, SMOTE, tool condition monitoring, Tool life, Wear},
pubstate = {published},
tppubtype = {article}
}
The acceptance of the machined surfaces not only depends on roughness parameters but also in the flatness deviation (Δfl). Hence, before reaching the threshold of flatness deviation caused by the wear of the face mill, the tool inserts need to be changed to avoid the expected product rejection. As current CNC machines have the facility to track, in real-time, the main drive power, the present study utilizes this facility to predict the flatness deviation—with proper consideration to the amount of wear of cutting tool insert’s edge. The prediction of deviation from flatness is evaluated as a regression and a classification problem, while different machine-learning techniques like Multilayer Perceptrons, Radial Basis Functions Networks, Decision Trees and Random Forest ensembles have been examined. Finally, Random Forest ensembles combined with Synthetic Minority Over-sampling Technique (SMOTE) balancing technique showed the highest performance when the flatness levels are discretized taking into account industrial requirements. The SMOTE balancing technique resulted in a very useful strategy to avoid the strong limitations that small experiment datasets produce in the accuracy of machine-learning models.
2019
Pimenov, Danil Yurievich; Hassui, Amauri; Wojciechowski, Szymon; Mia, Mozammel; Magri, Aristides; Suyama, Daniel I.; Bustillo, Andrés; Krolczyk, Grzegorz; Gupta, Munish Kumar
Effect of the Relative Position of the Face Milling Tool towards the Workpiece on Machined Surface Roughness and Milling Dynamics Journal Article
In: Applied Sciences, vol. 9, no. 5, pp. 842, 2019, ISSN: 2076-3417.
Abstract | Links | BibTeX | Tags: acceleration, cutting force, Face milling, relative position, surface roughness
@article{Pimenov2019,
title = {Effect of the Relative Position of the Face Milling Tool towards the Workpiece on Machined Surface Roughness and Milling Dynamics},
author = {Danil Yurievich Pimenov and Amauri Hassui and Szymon Wojciechowski and Mozammel Mia and Aristides Magri and Daniel I. Suyama and Andrés Bustillo and Grzegorz Krolczyk and Munish Kumar Gupta},
doi = {10.3390/app9050842},
issn = {2076-3417},
year = {2019},
date = {2019-02-27},
journal = {Applied Sciences},
volume = {9},
number = {5},
pages = {842},
abstract = {In face milling one of the most important parameters of the process quality is the roughness of the machined surface. In many articles, the influence of cutting regimes on the roughness and cutting forces of face milling is considered. However, during flat face milling with the milling width B lower than the cutter’s diameter D, the influence of such an important parameter as the relative position of the face mill towards the workpiece and the milling kinematics (Up or Down milling) on the cutting force components and the roughness of the machined surface has not been sufficiently studied. At the same time, the values of the cutting force components can vary significantly depending on the relative position of the face mill towards the workpiece, and thus have a different effect on the power expended on the milling process. Having studied this influence, it is possible to formulate useful recommendations for a technologist who creates a technological process using face milling operations. It is possible to choose such a relative position of the face mill and workpiece that will provide the smallest value of the surface roughness obtained by face milling. This paper shows the influence of the relative position of the face mill towards the workpiece and milling kinematics on the components of the cutting forces, the acceleration of the machine spindle in the process of face milling (considering the rotation of the mill for a full revolution), and on the surface roughness obtained by face milling. Practical recommendations on the assignment of the relative position of the face mill towards the workpiece and the milling kinematics are given.},
keywords = {acceleration, cutting force, Face milling, relative position, surface roughness},
pubstate = {published},
tppubtype = {article}
}
In face milling one of the most important parameters of the process quality is the roughness of the machined surface. In many articles, the influence of cutting regimes on the roughness and cutting forces of face milling is considered. However, during flat face milling with the milling width B lower than the cutter’s diameter D, the influence of such an important parameter as the relative position of the face mill towards the workpiece and the milling kinematics (Up or Down milling) on the cutting force components and the roughness of the machined surface has not been sufficiently studied. At the same time, the values of the cutting force components can vary significantly depending on the relative position of the face mill towards the workpiece, and thus have a different effect on the power expended on the milling process. Having studied this influence, it is possible to formulate useful recommendations for a technologist who creates a technological process using face milling operations. It is possible to choose such a relative position of the face mill and workpiece that will provide the smallest value of the surface roughness obtained by face milling. This paper shows the influence of the relative position of the face mill towards the workpiece and milling kinematics on the components of the cutting forces, the acceleration of the machine spindle in the process of face milling (considering the rotation of the mill for a full revolution), and on the surface roughness obtained by face milling. Practical recommendations on the assignment of the relative position of the face mill towards the workpiece and the milling kinematics are given.
2018
Grzenda, Maciej; Bustillo, Andres
Semi-supervised roughness prediction with partly unlabeled vibration data streams Journal Article
In: Journal of Intelligent Manufacturing, pp. 1-13, 2018, ISSN: 1572-8145.
Abstract | Links | BibTeX | Tags: Face milling, Roughness prediction, Semi-supervised techniques, Unlabeled data
@article{Grzenda2018,
title = {Semi-supervised roughness prediction with partly unlabeled vibration data streams},
author = {Maciej Grzenda and Andres Bustillo},
url = {https://doi.org/10.1007/s10845-018-1413-z},
doi = {10.1007/s10845-018-1413-z},
issn = {1572-8145},
year = {2018},
date = {2018-03-23},
journal = {Journal of Intelligent Manufacturing},
pages = {1-13},
abstract = {Experimental data sets that include tool settings, tool and machine-tool behavior, and surface roughness data for milling processes are usually of limited size, due mainly to the high costs of machining tests. This fact restricts the application of machine-learning techniques for surface roughness prediction in industrial settings. The primary objective of this work is to investigate the way data streams that are missing product features (i.e. unlabeled data streams) can contribute to the development of prediction models. The investigation is followed by a proposal for a semi-supervised approach to the development of roughness prediction models that can use partly unlabeled data to improve the accuracy of roughness prediction. Following this strategy, records collected during the milling process, which miss roughness measurements, but contain vibration data are used to increase the accuracy of the prediction models. The method proposed in this work is based on the selective use of such unlabelled instances, collected at tool settings that are not represented in the labeled data. This strategy, when applied properly, yields both extended training data sets and higher accuracy in the roughness prediction models that are derived from them. The scale of accuracy improvement and its statistical significance are shown in the study case of high-torque face milling of F114 steel. The semi-supervised approach proposed in this work has been used in combination with supervised k Nearest Neighbours and random forest techniques. Furthermore, the study of both continuous and discretized roughness prediction, showed higher gains in accuracy in the second.},
keywords = {Face milling, Roughness prediction, Semi-supervised techniques, Unlabeled data},
pubstate = {published},
tppubtype = {article}
}
Experimental data sets that include tool settings, tool and machine-tool behavior, and surface roughness data for milling processes are usually of limited size, due mainly to the high costs of machining tests. This fact restricts the application of machine-learning techniques for surface roughness prediction in industrial settings. The primary objective of this work is to investigate the way data streams that are missing product features (i.e. unlabeled data streams) can contribute to the development of prediction models. The investigation is followed by a proposal for a semi-supervised approach to the development of roughness prediction models that can use partly unlabeled data to improve the accuracy of roughness prediction. Following this strategy, records collected during the milling process, which miss roughness measurements, but contain vibration data are used to increase the accuracy of the prediction models. The method proposed in this work is based on the selective use of such unlabelled instances, collected at tool settings that are not represented in the labeled data. This strategy, when applied properly, yields both extended training data sets and higher accuracy in the roughness prediction models that are derived from them. The scale of accuracy improvement and its statistical significance are shown in the study case of high-torque face milling of F114 steel. The semi-supervised approach proposed in this work has been used in combination with supervised k Nearest Neighbours and random forest techniques. Furthermore, the study of both continuous and discretized roughness prediction, showed higher gains in accuracy in the second.