Datenbasierte Modellierung von Fräsrobotern/Data-driven models of milling robots – Modelling the pose-dependency of the structural dynamics using modern algorithms for machine learning

2020 ◽  
Vol 110 (09) ◽  
pp. 624-628
Author(s):  
Maximilian Busch ◽  
Thomas Semm ◽  
Michael Zäh
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Structural Dynamics ◽  
Machine Tools ◽  
Industrial Robots ◽  
Data Driven ◽  
Learning Methods ◽  
Industrial Management ◽  
Machine Learning Methods ◽  
University Of Munich

Industrieroboter werden aufgrund ihres großen Arbeitsraumes zunehmend für die Fräsbearbeitungen großer Werkstücke eingesetzt. Dynamische Instabilitäten während des Prozesses schränken jedoch ihre Produktivität ein. Maschinelle Lernverfahren gewinnen hierbei an Popularität, um Strukturmodelle aus experimentellen Daten abzuleiten. Das Institut für Werkzeugmaschinen und Betriebswissenschaften (iwb) der Technischen Universität München entwickelt in Zuge dessen Methoden, die mit maschinellen Lernverfahren Simulations- und Experimentaldaten verbinden, um dadurch die Strukturdynamik von Fräsrobotern zu modellieren.   Industrial robots are increasingly used for milling applications of large workpieces due to their large working area. However, dynamic instabilities during the process limit their productivity. Thus, machine learning methods are becoming increasingly popular for deriving system models from experimental data. The Institute for Machine Tools and Industrial Management (iwb) at the Technical University of Munich is developing methods to fuse simulation data and experimental data using machine learning methods to model the structural dynamics of milling robots.

Estimation of data-driven streamflow predicting models using machine learning methods

2021 ◽  
Vol 14 (11) ◽  
Author(s):  
Tanveer Ahmed Siddiqi ◽  
Saima Ashraf ◽  
Sadiq Ali Khan ◽  
Muhammad Jawed Iqbal
Keyword(s):  
Machine Learning ◽  
Data Driven ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Approach to an automatic prediction of the state of industrial manipulators using machine learning methods

2021 ◽  
Vol 24 (1) ◽  
pp. 48-54
Author(s):  
A. S. Goncharov ◽  
◽  
A. O. Savelev ◽  
A. S. Pisankin ◽  
A. Y. Chepkasov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Technologies ◽  
Industrial Revolution ◽  
Industrial Robots ◽  
Learning Methods ◽  
Emergency Situations ◽  
Machine Learning Methods ◽  
Technological Chain ◽  
Using Data ◽  
Working Out

Due to intensive development of information technologies and the onset of 4th industrial revolution the number of robotic industries is steadily growing. The volume of production and the use of robots is also increasing. At the same time, the support and the management of digital production is being rapidly developing. The robotic systems are incapable of completely excluding a person from the technological chain, since they need timely maintenance and personnel working out the emergency situations. One of the solutions to reduce the risk of unexpected breakdowns is a predictive approach to the maintenance. The implementation of this approach is carried out using data analysis tools. This study presents the results of applying machine learning methods to analyze data from industrial robots in order to predict potential failures

scholarly journals Predicting rice blast disease: machine learning versus process-based models

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
David F. Nettleton ◽  
Dimitrios Katsantonis ◽  
Argyris Kalaitzidis ◽  
Natasa Sarafijanovic-Djukic ◽  
Pau Puigdollers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rice Blast ◽  
Machine Learning Algorithms ◽  
Rice Blast Disease ◽  
Blast Disease ◽  
Data Driven ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Plant Disease Management ◽  
Process Based Models

Abstract Background In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent Neural Networks (RNN)), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared. Results Results clearly showed that the models succeeded in providing a warning of rice blast onset and presence, thus representing suitable solutions for preventive remedial actions targeting the mitigation of yield losses and the reduction of fungicide use. All methods gave significant “signals” during the “early warning” period, with a similar level of performance. M5Rules and WARM gave the maximum average normalized scores of 0.80 and 0.77, respectively, whereas Yoshino gave the best score for one site (Kalochori 2015). The best average values of r and r2 and %MAE (Mean Absolute Error) for the machine learning models were 0.70, 0.50 and 0.75, respectively and for the process-based models the corresponding values were 0.59, 0.40 and 0.82. Thus it has been found that the ML models are competitive with the process-based models. This result has relevant implications for the operational use of the models, since most of the available studies are limited to the analysis of the relationship between the model outputs and the incidence of rice blast. Results also showed that machine learning methods approximated the performances of two process-based models used for years in operational contexts. Conclusions Process-based and data-driven models can be used to provide early warnings to anticipate rice blast and detect its presence, thus supporting fungicide applications. Data-driven models derived from machine learning methods are a viable alternative to process-based approaches and – in cases when training datasets are available – offer a potentially greater adaptability to new contexts.

Data in Construction Grammar

2013 ◽  
Author(s):  
Stefan Th. Gries
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Computational Linguistic ◽  
Statistical Analysis ◽  
Construction Grammar ◽  
Data Types ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
New Directions ◽  
Corpus Data

This chapter examines the types of data used in constructionist approaches and the parameters along which data types can be classified. It discusses different kinds of quantitative observational/corpus data (frequencies, probabilities, association measures) and their statistical analysis. In addition, it provides a survey of a variety of different experimental data (novel word/construction learning, priming, sorting, etc.). Finally, the chapter discusses computational-linguistic/machine-learning methods as well as new directions for the development of new data and methods in Construction Grammar.

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