Andrews curves Applied Machine Learning Bagging Boosting Business intelligence Class-imbalanced problems Classifier ensembles Cocke-Younger-Kasami algorithm Computer Science teaching Data analysis Data Mining Data visualization Decision trees Disturbing neighbors End of studies project Ensemble methods Ensembles Exploratory data analysis Exploratory projection pursuit Face milling Finite automata Grammars Imbalanced data Instance selection Linear projections LL parsing Machine learning Multi-label classification Neural networks Parsing algorithms Random forest Random oracles Regression Regression ensembles Regression trees Regular expressions Rotation forest Self organizing maps SMOTE Support vector machines
2020 |
Bustillo, Andrés; Reis, Roberto; Machado, Alisson R; Pimenov, Danil Yurievich Improving the accuracy of machine-learning models with data from machine test repetitions Journal Article In: Journal of Intelligent Manufacturing, 2020, ISSN: 0956-5515. Abstract | Links | BibTeX | Tags: Artificial intelligence, Brandsma facing tests, Ensembles, Machine learning, Tool geometry, Turning @article{Bustillo2020, title = {Improving the accuracy of machine-learning models with data from machine test repetitions}, author = {Andrés Bustillo and Roberto Reis and Alisson R. Machado and Danil Yurievich Pimenov}, url = {https://link.springer.com/article/10.1007%2Fs10845-020-01661-3}, doi = {https://doi.org/10.1007/s10845-020-01661-3}, issn = {0956-5515}, year = {2020}, date = {2020-09-17}, journal = {Journal of Intelligent Manufacturing}, abstract = {The modelling of machining processes by means of machine-learning algorithms is still based on principles that are especially adapted to mechanical approaches, in which very few inputs are varied with little repetition of experimental conditions. These principles might not be ideal to achieve accurate machine-learning models and they are certainly not aligned with the practicalities of industrial machining in factories. In this research the effect of a new strategy to improve machine-learning model accuracy is studied: experimental repetition. Tool-life prediction in the face-turning operations of AISI 1045 steel discs, depending on different cooling systems and tool geometries, is selected as a case study. Both the side rake and the relief angles of HSS tools are optimized using the Brandsma facing test under dry, MQL, and flooding conditions. Different machine-learning algorithms, such as regression trees, kNNs, artificial neural networks, and ensembles (bagging and Random Forest) are tested. On the one hand, the results of the study showed that artificial neural networks of Radial Basis Functions presented the highest model accuracy (11.4 mm RMSE), but required a very sensitive and complex tuning process. On the other hand, they demonstrated that ensembles, especially Random Forest, provided models with accuracy in the same range, but with no tuning procedure (12.8 mm RMSE). Secondly, the effect of an increased dataset size, by means of experimental repetition, is evaluated and compared with traditional experimental modelling that used average values. The results showed that some machine-learning techniques, including both ensemble types, significantly improved their accuracy with this strategy, by up to 23%. The results therefore suggested that the use of raw experimental data, rather than their averaged values, can achieve machine-learning models of higher accuracy for tool-wear processes.}, keywords = {Artificial intelligence, Brandsma facing tests, Ensembles, Machine learning, Tool geometry, Turning}, pubstate = {published}, tppubtype = {article} } The modelling of machining processes by means of machine-learning algorithms is still based on principles that are especially adapted to mechanical approaches, in which very few inputs are varied with little repetition of experimental conditions. These principles might not be ideal to achieve accurate machine-learning models and they are certainly not aligned with the practicalities of industrial machining in factories. In this research the effect of a new strategy to improve machine-learning model accuracy is studied: experimental repetition. Tool-life prediction in the face-turning operations of AISI 1045 steel discs, depending on different cooling systems and tool geometries, is selected as a case study. Both the side rake and the relief angles of HSS tools are optimized using the Brandsma facing test under dry, MQL, and flooding conditions. Different machine-learning algorithms, such as regression trees, kNNs, artificial neural networks, and ensembles (bagging and Random Forest) are tested. On the one hand, the results of the study showed that artificial neural networks of Radial Basis Functions presented the highest model accuracy (11.4 mm RMSE), but required a very sensitive and complex tuning process. On the other hand, they demonstrated that ensembles, especially Random Forest, provided models with accuracy in the same range, but with no tuning procedure (12.8 mm RMSE). Secondly, the effect of an increased dataset size, by means of experimental repetition, is evaluated and compared with traditional experimental modelling that used average values. The results showed that some machine-learning techniques, including both ensemble types, significantly improved their accuracy with this strategy, by up to 23%. The results therefore suggested that the use of raw experimental data, rather than their averaged values, can achieve machine-learning models of higher accuracy for tool-wear processes. |
2019 |
Juez-Gil, Mario; Erdakov, Ivan Nikolaevich; Bustillo, Andrés; Pimenov, Danil Yurievich A regression-tree multilayer-perceptron hybrid strategy for the prediction of ore crushing-plate lifetimes Journal Article In: Journal of Advanced Research, July 2019 (18), pp. 173-184, 2019, ISSN: 2090-1232. Abstract | Links | BibTeX | Tags: Artificial intelligence, Hadfield steel, Lifetime prediction, Multi-layer perceptrons, Regression trees, Resource savings @article{Juez-Gil2019, title = {A regression-tree multilayer-perceptron hybrid strategy for the prediction of ore crushing-plate lifetimes}, author = {Mario Juez-Gil and Ivan Nikolaevich Erdakov and Andrés Bustillo and Danil Yurievich Pimenov}, doi = {10.1016/j.jare.2019.03.008}, issn = {2090-1232}, year = {2019}, date = {2019-07-01}, journal = {Journal of Advanced Research}, volume = {July 2019}, number = {18}, pages = {173-184}, abstract = {Highly tensile manganese steel is in great demand owing to its high tensile strength under shock loads. All workpieces are produced through casting, because it is highly difficult to machine. The probabilistic aspects of its casting, its variable composition, and the different casting techniques must all be considered for the optimisation of its mechanical properties. A hybrid strategy is therefore proposed which combines decision trees and artificial neural networks (ANNs) for accurate and reliable prediction models for ore crushing plate lifetimes. The strategic blend of these two high-accuracy prediction models is used to generate simple decision trees which can reveal the main dataset features, thereby facilitating decision-making. Following a complexity analysis of a dataset with 450 different plates, the best model consisted of 9 different multilayer perceptrons, the inputs of which were only the Fe and Mn plate compositions. The model recorded a low root mean square error (RMSE) of only 0.0614 h for the lifetime of the plate: a very accurate result considering their varied lifetimes of between 746 and 6902 h in the dataset. Finally, the use of these models under real industrial conditions is presented in a heat map, namely a 2D representation of the main manufacturing process inputs with a colour scale which shows the predicted output, i.e. the expected lifetime of the manufactured plates. Thus, the hybrid strategy extracts core training dataset information in high-accuracy prediction models. This novel strategy merges the different capabilities of two families of machine-learning algorithms. It provides a high-accuracy industrial tool for the prediction of the full lifetime of highly tensile manganese steel plates. The results yielded a precision prediction of (RMSE of 0.061 h) for the full lifetime of (light, medium, and heavy) crusher plates manufactured with the three (experimental, classic, and highly efficient (new)) casting methods.}, keywords = {Artificial intelligence, Hadfield steel, Lifetime prediction, Multi-layer perceptrons, Regression trees, Resource savings}, pubstate = {published}, tppubtype = {article} } Highly tensile manganese steel is in great demand owing to its high tensile strength under shock loads. All workpieces are produced through casting, because it is highly difficult to machine. The probabilistic aspects of its casting, its variable composition, and the different casting techniques must all be considered for the optimisation of its mechanical properties. A hybrid strategy is therefore proposed which combines decision trees and artificial neural networks (ANNs) for accurate and reliable prediction models for ore crushing plate lifetimes. The strategic blend of these two high-accuracy prediction models is used to generate simple decision trees which can reveal the main dataset features, thereby facilitating decision-making. Following a complexity analysis of a dataset with 450 different plates, the best model consisted of 9 different multilayer perceptrons, the inputs of which were only the Fe and Mn plate compositions. The model recorded a low root mean square error (RMSE) of only 0.0614 h for the lifetime of the plate: a very accurate result considering their varied lifetimes of between 746 and 6902 h in the dataset. Finally, the use of these models under real industrial conditions is presented in a heat map, namely a 2D representation of the main manufacturing process inputs with a colour scale which shows the predicted output, i.e. the expected lifetime of the manufactured plates. Thus, the hybrid strategy extracts core training dataset information in high-accuracy prediction models. This novel strategy merges the different capabilities of two families of machine-learning algorithms. It provides a high-accuracy industrial tool for the prediction of the full lifetime of highly tensile manganese steel plates. The results yielded a precision prediction of (RMSE of 0.061 h) for the full lifetime of (light, medium, and heavy) crusher plates manufactured with the three (experimental, classic, and highly efficient (new)) casting methods. |