scholarly journals Development of turning process digital twin based on machine learning

2021 ◽  
pp. 32-41
Author(s):  
D. A. Rastorguev ◽  
◽  
A. A. Sevastyanov ◽  
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Networks ◽  
Information Technologies ◽  
Cutting Process ◽  
Turning Process ◽  
Force Load ◽  
Cnc Machine ◽  
Digital Twin ◽  
Artificial Neural

Today, manufacturing technologies are developing within the Industry 4.0 concept, which is the information technologies introduction in manufacturing. One of the most promising digital technologies finding more and more application in manufacturing is a digital twin. A digital twin is an ensemble of mathematical models of technological process, which exchanges information with its physical prototype in real-time. The paper considers an example of the formation of several interconnected predictive modules, which are a part of the structure of the turning process digital twin and designed to predict the quality of processing, the chip formation nature, and the cutting force. The authors carried out a three-factor experiment on the hard turning of 105WCr6 steel hardened to 55 HRC. Used an example of the conducted experiment, the authors described the process of development of the digital twin diagnostic module based on artificial neural networks. When developing a mathematical model for predicting and diagnosing the cutting process, the authors revealed higher accuracy, adaptability, and versatility of artificial neural networks. The developed mathematical model of online diagnostics of the cutting process for determining the surface quality and chip type during processing uses the actual value of the cutting depth determined indirectly by the force load on the drive. In this case, the model uses only the signals of the sensors included in the diagnostic subsystem on the CNC machine. As an informative feature reflecting the force load on the machine’s main motion drive, the authors selected the value of the energy of the current signal of the spindle drive motor. The study identified that the development of a digital twin is possible due to the development of additional modules predicting the accuracy of dimensions, geometric profile, tool wear.

Correcting geometric deviations of CNC Machine-Tools: An approach with Artificial Neural Networks

2015 ◽  
Vol 36 ◽  
pp. 114-124 ◽  
Author(s):  
Wanderson de Oliveira Leite ◽  
Juan Carlos Campos Rubio ◽  
Jaime Gilberto Duduch ◽  
Paulo Eduardo Maciel de Almeida
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Machine Tools ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Geometric Deviations ◽  
Artificial Neural

Autonomous Design of Artificial Neural Networks by Neurex

1996 ◽  
Vol 8 (8) ◽  
pp. 1767-1786 ◽  
Author(s):  
François Michaud ◽  
Ruben Gonzalez Rubio
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Networks ◽  
Expert System ◽  
Design Process ◽  
Design Rules ◽  
Learning Abilities ◽  
Artificial Neural ◽  
Number Of Iterations ◽  
Learning Law

Artificial neural networks (ANN) have been demonstrated to be increasingly more useful for complex problems difficult to solve with conventional methods. With their learning abilities, they avoid having to develop a mathematical model or acquiring the appropriate knowledge to solve a task. The difficulty now lies in the ANN design process. A lot of choices must be made to design an ANN, and there are no available design rules to make these choices directly for a particular problem. Therefore, the design of an ANN demands a certain number of iterations, mainly guided by the expertise and the intuition of the developer. To automate the ANN design process, we have developed Neurex, composed of an expert system and an ANN simulator. Neurex autonomously guides the iterative ANN design process. Its structure tries to reproduce the design steps done by a human expert in conceiving an ANN. As a whole, the Neurex structure serves as a framework to implement this expertise for different learning paradigms. This article presents the system's general characteristics and its use in designing ANN using the standard backpropagation learning law.

Classification of disc damage status by discovering knowledge from experimental data in marble cutting process

2016 ◽  
Vol 231 (13) ◽  
pp. 2407-2416 ◽  
Author(s):  
Emre Akarslan ◽  
Fatih O Hocaoğlu ◽  
Ismail Ucun
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Process ◽  
Mean Values ◽  
Current Voltage ◽  
The Status ◽  
Artificial Neural ◽  
Natural Stones ◽  
Industrial Injuries

In marble industry, it is of vital importance to determine the damaged discs on time to prevent possible industrial injuries. Therefore, in this study, it is proposed to classify the status of the cutting discs that are used while cutting the natural stones. To classify the deflections of the discs, 673 different experiments are performed. Cutting discs corresponding to four different damage classes (undamaged disc, less damaged disc, much damaged disc, and broken disc) are employed in the tests. Eight different parameters (cutting forces (Fx, Fy, Fz), noise, peripheral speed of the disc, current, voltage, power consumption) are measured and recorded in the experiments. For each experiment, mean values of different measured data are studied. Artificial neural networks are employed as classifiers. In the first stage, all of these mean values corresponding to eight parameters are selected as the input vectors of the artificial neural networks, whereas in the second stage, the dimension of input vector is decreased by leaving out the parameters one by one. In this stage, it is aimed to determine the most important parameter that caries much more information about the cutting process.

scholarly journals Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ali Al Haidan ◽  
Osama Abu-Hammad ◽  
Najla Dar-Odeh
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Tooth Surface ◽  
Output Data ◽  
Surface Loss ◽  
Artificial Neural ◽  
High Degree

Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.

scholarly journals Mathematical model for the evaluation of risk of emergency situations at a dangerous technical object based on artificial neural networks

2018 ◽  
Vol 44 ◽  
pp. 00069
Author(s):  
Nikolay Peganov ◽  
Aleksandr Tumanov ◽  
Vladimir Tumanov
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Risk Assessment ◽  
Artificial Neural Networks ◽  
Fire Risk ◽  
Management Decision ◽  
Technical Object ◽  
Emergency Situations ◽  
Artificial Neural ◽  
The Mathematical Model

In the work performed adaptation of artificial neural networks in modern security systems potentially dangerous technical objects — high-rise buildings as tools for assessing and forecasting in management decision. The study obtained the main scientific results: the mathematical model of risk assessment of man-made emergencies based on artificial neural networks; the mathematical model, adapted to the cumulative model of development technogene emergency-fire; provided risk assessment technique manmade emergencies based on artificial neural networks; represented private man-made fire risk assessment methodology using artificial neural networks.

scholarly journals INFORMATION TECHNOLOGIES IN HYDROMECHANICAL METHOD OF AMBER MINING

2021 ◽  
Author(s):  
Zinovii Malanchuk ◽  
◽  
Viktor Nadutyi ◽  
Valerii Korniyenko ◽  
Yevhenii Malanchuk ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Information Technologies ◽  
Modern Information ◽  
Artificial Neural

The analysis results of possibilities of use of modern information technologies at control of process of extraction of amber from sand deposits are resulted. The expediency and efficiency of using artificial neural networks in the control of hydromechanical extraction of amber from sand deposits is shown. The structure of an artificial neural network which considers features of hydromechanical extraction of amber is offered.

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