Evaluation System of Tooth Contact Patterns of Hypoid Gears Using Artificial Intelligence

2012 ◽  
Author(s):  
Morimasa Nakamura ◽  
Keisuke Kojima ◽  
Ichiro Moriwaki
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Evaluation System ◽  
Gear Pair ◽  
Contact Pattern ◽  
Hypoid Gear ◽  
Tooth Contact ◽  
Contact Patterns ◽  
The Neural Network ◽  
Hypoid Gear Pairs

Tooth contact inspection is one of the most common methods for checking qualities of hypoid gear pairs. A change in machine setting parameters for cutting and lapping processes of a hypoid gear pair enables a tooth contact pattern of a hypoid gear pair to be varied. The deviation of the pattern from the target one is represented by a grade point. In the inspection, the qualities of hypoid gear pairs are usually classified into only two grades; OK or NG. However, in order to conduct a follow-up survey on problems of the products and to be useful to be trouble shooting tasks of the end products, finer classifications and more quantitative evaluations of tooth contact patterns could be effective. Such approaches have been tried, however, only experienced and well-trained technicians for the inspection of hypoid gear pairs can determine the point of each tooth contact pattern. And it is difficult to make this evaluation method automatic. To overcome this problem, an application of artificial intelligence system must be useful. The present paper describes a computer evaluation system using the neural network, which is a kind of the artificial intelligence systems, for tooth contact patterns of hypoid gear pairs which can evaluate the results of the inspections instead of experienced hypoid gear technicians. This system with the neural network has a capability to learn relationships between evaluation grade points of tooth contact patterns given by the hypoid gear technicians and graphics of tooth contact patterns of hypoid gear pairs. Moreover, it can return the evaluation grade points when a tooth contact pattern is input into the system. The evaluation performance of the developed system was discussed. And a quality of normative tooth contact patterns, which were used as the teacher signals for training the neural network system, greatly affected its performance. The comparison of evaluated grade points obtained from developed system with the technician’s ones showed that the correct answer ratio obtained from the developed system was about 90% in the best case.

Optimized Bearing Ellipses of Hypoid Gears1

2003 ◽  
Vol 125 (4) ◽  
pp. 739-745 ◽  
Author(s):  
J. Achtmann ◽  
G. Ba¨r
Keyword(s):  
Influence Function ◽  
Gear Tooth ◽  
Target Pair ◽  
Evaluation Function ◽  
Contact Pattern ◽  
Hypoid Gear ◽  
Tooth Contact ◽  
Gear Drive ◽  
Contact Patterns ◽  
Best Fit

For given machine tool settings of a universal hypoid gear generator, the tooth contact patterns are computed for the coast and drive side of a hypoid gear drive. Each contact pattern is replaced by a determined tooth-bearing ellipse. The position, shape, and inclination of each bearing ellipse is calculated. By the help of these data, an influence function is designed that describes the influence of supplemental kinematic flank correction motions (modified motions) on the gear-tooth contact. Examples show the influence of helical motion and modified roll. An evaluation function permits the calculation of modified motions which improve the tooth contact either at coast and drive side simultaneously, or only at one of the sides. For a given pair of start-bearing ellipses at coast and drive side, and for given importance weights to the sides, we describe how modified motions can be computed that best fit a given target pair of bearing ellipses.

scholarly journals Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046265
Author(s):  
Shotaro Doki ◽  
Shinichiro Sasahara ◽  
Daisuke Hori ◽  
Yuichi Oi ◽  
Tsukasa Takahashi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Psychological Distress ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Explanatory Variables ◽  
The Neural Network ◽  
Significant Difference ◽  
Total Data

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.

Optimal Tooth Modifications in Hypoid Gears

2004 ◽  
Vol 127 (4) ◽  
pp. 646-655 ◽  
Author(s):  
Vilmos Simon
Keyword(s):  
Load Distribution ◽  
Angular Position ◽  
Point Contact ◽  
Tooth Surface ◽  
Gear Pair ◽  
Hypoid Gear ◽  
Tooth Contact ◽  
Hypoid Gears ◽  
Transmission Errors ◽  
Tool Profile

A method for the determination of optimal tooth modifications in hypoid gears based on improved load distribution and reduced transmission errors is presented. The modifications are introduced into the pinion tooth surface by using a cutter with bicircular profile and optimal diameter. In the optimization of tool parameters the influence of shaft misalignments of the mating members is included. As the result of these modifications a point contact of the meshed teeth surfaces appears instead of line contact; the hypoid gear pair becomes mismatched. By using the method presented in (Simon, V., 2000, “Load Distribution in Hypoid Gears,” ASME J. Mech. Des., 122, pp. 529–535) the influence of tooth modifications introduced on tooth contact and transmission errors is investigated. Based on the results that was obtained the radii and position of circular tool profile arcs and the diameter of the cutter for pinion teeth generation were optimized. By applying the optimal tool parameters, the maximum tooth contact pressure is reduced by 16.22% and the angular position error of the driven gear by 178.72%, in regard to the hypoid gear pair with a pinion manufactured by a cutter of straight-sided profile and of diameter determined by the commonly used methods.

scholarly journals MODEL JARINGAN SARAF TIRUAN UNTUK EVALUASI RESIKO KREDIT

2018 ◽  
Vol 2 (1) ◽  
pp. 76
Author(s):  
Novan Wijaya
Keyword(s):  
Neural Network ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Financial Risk ◽  
Banking Sector ◽  
Major Focus ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Risk Evaluation System

Credit risk evaluation is an importanttopic in financial risk management and become a major focus in the banking sector. This research discusses a credit risk evaluation system using an artificial neural network model based on backpropagation algorithm. This system is to train and test the neural network to determine the predictive value of credit risk, whether high riskorlow risk. This neural network uses 14 input layers, nine hidden layers and an output layer, and the data used comes from the bank that has branches in EastJakarta. The results showed that neural network can be used effectively in the evaluation of credit risk with accuracy of 88% from 100 test data

scholarly journals Artificial Intelligence Techniques for Phishing Detection

2019 ◽  
Vol 8 (11) ◽  
pp. 2330-2335
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Neural Systems ◽  
Element Determination ◽  
Artificial Intelligence Techniques ◽  
The Neural Network ◽  
Proper Number ◽  
Phishing Detection

The objective of this undertaking is to apply neural systems to phishing email recognition and assess the adequacy of this methodology. We structure the list of capabilities, process the phishing dataset, and execute the Neural Network frameworks. we analyze its exhibition against that of other real Artificial Intelligence Techniques – DT , K-nearest , NB and SVM machine.. The equivalent dataset and list of capabilities are utilized in the correlation. From the factual examination, we infer that Neural Networks with a proper number of concealed units can accomplish acceptable precision notwithstanding when the preparation models are rare. Additionally, our element determination is compelling in catching the qualities of phishing messages, as most AI calculations can yield sensible outcomes with it.

Latest Technology and Future Trends

2021 ◽  
pp. 270-273
Author(s):  
Meghna Babubhai Patel ◽  
Jagruti N. Patel ◽  
Upasana M. Bhilota
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Smart Devices ◽  
The Internet ◽  
Future Trends ◽  
Internet Industry ◽  
The Neural Network ◽  
Dramatic Rise ◽  
Technological World ◽  
The Way

ANN can work the way the human brain works and can learn the way we learn. The neural network is this kind of technology that is not an algorithm; it is a network that has weights on it, and you can adjust the weights so that it learns. You teach it through trials. It is a fact that the neural network can operate and improve its performance after “teaching” it, but it needs to undergo some process of learning to acquire information and be familiar with them. Nowadays, the age of smart devices dominates the technological world, and no one can deny their great value and contributions to mankind. A dramatic rise in the platforms, tools, and applications based on machine learning and artificial intelligence has been seen. These technologies not only impacted software and the internet industry but also other verticals such as healthcare, legal, manufacturing, automobile, and agriculture. The chapter shows the importance of latest technology used in ANN and future trends in ANN.

An AI based design of student performance prediction and evaluation system in college physical education

2020 ◽  
pp. 1-9
Author(s):  
Zhang Yangsheng
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Physical Education ◽  
Student Performance ◽  
Internal Structure ◽  
Process Evaluation ◽  
Performance Prediction ◽  
Evaluation System ◽  
Evaluation Model ◽  
Good Effect

College physical education is too one-sided, which makes the teaching process evaluation meaningless. Based on this, based on neural network technology, this article combines artificial intelligence teaching system to build an artificial intelligence sports teaching evaluation model based on neural network. The artificial intelligence model starts from the process evaluation and the final evaluation. Moreover, it uses a recurrent neural network for data training and analysis, and introduces a new decoder to perform data processing, and introduces a simplified gated neural network internal structure diagram to build the internal structure of the model.In addition, this study designs a control experiment to evaluate the performance of the model constructed in this study. The research results show that the artificial intelligence model constructed in this paper has a good effect in the performance prediction and evaluation of college sports students.

Optimal Tooth Modifications in Hypoid Gears

2003 ◽  
Author(s):  
Vilmos V. Simon
Keyword(s):  
Angular Position ◽  
Point Contact ◽  
Tooth Surface ◽  
Gear Pair ◽  
Hypoid Gear ◽  
Tooth Contact ◽  
Hypoid Gears ◽  
Transmission Errors ◽  
Tool Profile

A method for the determination of optimal tooth modifications in hypoid gears based on improved load distribution and reduced transmission errors is presented. The modifications are introduced into the pinion tooth surface by using a cutter with bicircular profile and by changing the cutter diameter. In the optimization of tool parameters the influence of shaft misalignments of the mating members is included. As the result of these modifications a point contact of the meshed teeth surfaces appears instead of line contact; the hypoid gear pair becomes mismatched. By using the method presented in [1] the influence of tooth modifications introduced on tooth contact and transmission errors is investigated. Based on the results that was obtained the radii and position of circular tool profile arcs and the cutter diameter for pinion teeth generation were optimized. By applying the optimal tool parameters, the maximum tooth contact pressure is reduced by 16.22% and the angular position error of the driven gear by 178.72%, in regard to the hypoid gear pair with a pinion manufactured by a cutter of straight-sided profile and of diameter determined by the commonly used methods.

506 Tooth Contact Pattern of Angular Hypoid Gear

2000 ◽  
Vol 2000.37 (0) ◽  
pp. 171-172
Author(s):  
Yoshio TODA ◽  
Norio ITO ◽  
Akihiro KIRI
Keyword(s):  
Contact Pattern ◽  
Hypoid Gear ◽  
Tooth Contact

scholarly journals Pattern Recognition using a Neural Network on a Microcontroller with I2C Ultrasonic Sensors

2019 ◽  
Vol 3 (1) ◽  
pp. 9-19 ◽  
Author(s):  
Fazal Noor
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Integrated Circuit ◽  
High Accuracy ◽  
Ultrasonic Sensors ◽  
Motion Patterns ◽  
The Neural Network ◽  
Proposed Model ◽  
Neural Network Algorithm

Ultrasonic sensors have been used in a variety of applications to measure ranges to objects. Hand gestures via ultrasonic sensors form unique motion patterns for controls. In this research, patterns formed by placing a set of objects in a grid of cells are used for control purposes. A neural network algorithm is implemented on a microcontroller which takes in range signals as inputs read from ultrasonic sensors and classifies them in one of four classes. The neural network is then trained to classify patterns based on objects’ locations in real-time. The testing of the neural network for pattern recognition is performed on a testbed consisting of Inter-Integrated Circuit (I2C) ultrasonic sensors and a microcontroller. The performance of the proposed model is presented and it is observed the model is highly scalable, accurate, robust and reliable for applications requiring high accuracy such as in robotics and artificial intelligence.

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