Publications

@article{Pimenov2022,

title = {Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review},

author = {Danil Yurievich Pimenov and Andrés Bustillo and Szymon Wojciechowski and Vishal Santosh Sharma and Munish Kumar Gupta and Mustafa Kuntğlu},

url = {https://link.springer.com/article/10.1007/s10845-022-01923-2#citeas},

doi = {10.1007/s10845-022-01923-2},

issn = {0956-5515},

year  = {2022},

date = {2022-03-12},

journal = {Journal of Intelligent Manufacturing},

volume = {2022},

abstract = {The wear of cutting tools, cutting force determination, surface roughness variations and other machining responses are of keen interest to latest researchers. The variations of these machining responses results in change in dimensional accuracy and productivity upto great extent. In addition, an excessive increase in wear leads to catastrophic consequences, exceeding the tool breakage. Therefore, this article discusses the online trend of modern approaches in tool condition monitoring while different machining operations. For this purpose, the effective use of new sensors and artificial intelligence (AI) is considered and followed during this holistic review work. The sensor systems used for monitoring tool wear are dynamometers, accelerometers, acoustic emission sensors, current and power sensors, image sensors, other sensors. These systems allow to solve the problem of automation and modeling of technological parameters of the main types of cutting, such as turning, milling, drilling and grinding. The modern artificial intelligence methods are considered, such as: Neural networks, Image recognition, Fuzzy logic, Adaptive neuro-fuzzy inference systems, Bayesian Networks, Support vector machine, Ensembles, Decision and regression trees, k-nearest neighbors, Artificial Neural Network, Markov model, Singular Spectrum Analysis, Genetic algorithms. Discussions also includes the main advantages, disadvantages and prospects of using various AI methods for tool wear monitoring. Moreover, the problems and future directions of the main processing methods using AI models are also highlighted.},

keywords = {Artificial intelligence, Machining, Sensor, tool condition monitoring, Tool life, Wear},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{BUSTILLO2018215,

title = {Using artificial intelligence models for the prediction of surface wear based on surface isotropy levels},

author = {A Bustillo and D.Yu. Pimenov and M Matuszewski and T Mikolajczyk},

url = {http://www.sciencedirect.com/science/article/pii/S0736584517303733},

doi = {https://doi.org/10.1016/j.rcim.2018.03.011},

issn = {0736-5845},

year  = {2018},

date = {2018-10-01},

journal = {Robotics and Computer-Integrated Manufacturing},

volume = {53},

pages = {215 - 227},

abstract = {Currently, a key industrial challenge in friction processes is the prediction of surface roughness and loss of mass under different machining processes, such as Electro-Discharge Machining (EDM), and turning and grinding processes. Under industrial conditions, only the sliding distance is easily evaluated in friction processes, while the acquisition of other variables usually implies expensive costs for production centres, such as the integration of sensors in functioning machine-tools. Besides, appropriate datasets are usually very small, because the testing of different friction conditions is also expensive. These two restrictions, small datasets and very few inputs, make it very difficult to use Artificial Intelligence (AI) techniques to model the industrial problem. So, the use of the isotropy level of the surface structure is proposed, as another input that is easily evaluated prior to the friction process. In this example, the friction processes of a cubic sample of 102Cr6 (40 HRC) steel and a further element made of X210Cr12 (60 HRC) steel are considered. Different artificial intelligence techniques, such as artificial regression trees, multilayer perceptrons (MLPs), radial basis networks (RBFs), and Random Forest, were tested considering the isotropy level as either a nominal or a numeric attribute, to evaluate improvements in the accuracy of surface roughness and loss-of-mass predictions. The results obtained with real datasets showed that RBFs and MLPs provided the most accurate models for loss of mass and surface roughness prediction, respectively. MLPs have slightly higher surface prediction accuracy than Random Forest, although MLP models are very sensitive to the tuning of their parameters (a small mismatch between the learning rate and the momentum in the MLP will drastically reduce the accuracy of the model). In contrast, Random Forest has no parameter to be tuned and its prediction is almost as good as MLPs for surface roughness, so Random Forest will be more suitable for industrial use where no expert in AI model tuning is available. Moreover, the inclusion of the isotropy level in the dataset, especially as a numeric attribute, greatly improved the accuracy of the models, in some cases, by up to 52% for MLPs, and by a smaller proportion of 16% in the Random Forest models in terms of Root Mean Square Error. Finally, Random Forest ensembles only trained with low and very high isotropy level experimental datasets generated reliable models for medium levels of isotropy, thereby offering a solution to reduce the size of training datasets.},

keywords = {Ensembles, Isotropy level geometric structure of the surface, Roughness, Small size dataset, Wear},

pubstate = {published},

tppubtype = {article}

}