scholarly journals Probability-Statistical Estimation Method of Feed Influence on As-Turned Finish of Steels and Non-Ferrous Metals

Metals ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 965 ◽  
Author(s):  
Petro Kryvyi ◽  
Volodymyr Dzyura ◽  
Nadiya Tymoshenko ◽  
Pavlo Maruschak ◽  
Justas Nugaras ◽  
...  
Keyword(s):  
Experimental Data ◽  
Statistical Estimation ◽  
Cutting Tool ◽  
Estimation Method ◽  
Roughness Parameter ◽  
Small Sample ◽  
Ferrous Metals ◽  
The Mean ◽  
Non Ferrous Metals ◽  
Turning Test

Based on the experimental data on the roughness parameter Ra, which stands for the mean arithmetic deviation of the profile, as obtained in the process of turning test specimens from different materials with constant elements of the cutting mode (depth a and velocity v) and structural-geometric parameters of the cutting tool, but with different feed rates f, the probability-statistical method for estimating the influence of feed rate f on the resulting surface roughness by the parameter Ra is proposed using the theory of a small sample.

scholarly journals DTA and TGA study of MeSo4 (Me = Fe, Co, Ni) dissociation in the presence of coke

2002 ◽  
Vol 38 (1-2) ◽  
pp. 103-116 ◽  
Author(s):  
B.S. Boyanov
Keyword(s):  
Experimental Data ◽  
Industrial Production ◽  
Effect Of Temperature ◽  
Ferrous Metals ◽  
Dissociation Temperature ◽  
Non Ferrous Metals ◽  
Temperature Time

MeSO4 (Me = Fe, Co, Ni) dissociation is investigated in order to estimate the effect of temperature, time and presence of coke as reducer on the degree and mechanism of dissociation. It is proved that the presence of coke decreases the dissociation temperature considerably and increases the degree of MeSO4 dissociation. Based on the obtained experimental data, a mechanism of the processes is proposed. The obtained results can be used in the industrial production of non-ferrous metals for explaining the processes that take place in the reduction of zinc and lead cakes in order to achieve favorable environmental, technical and economic results.

scholarly journals Hydrochemical cleaning of gold-containing cathode deposits from heavy non-ferrous metal impurities

2020 ◽  
Vol 24 (5) ◽  
pp. 1126-1136
Author(s):  
Viktoria Zhmurova ◽  
Keyword(s):  
Experimental Data ◽  
Chemical Composition ◽  
Hydrochloric Acid ◽  
Mass Fraction ◽  
Ferrous Metal ◽  
Precious Metals ◽  
Dispersion Analysis ◽  
Ferrous Metals ◽  
Gold Silver ◽  
Non Ferrous Metals

The purpose of this paper is to conduct the research on hydrochloric acid cleaning of gold-containing cathode deposits from the impurities of heavy non-ferrous metals and mathematical processing of the experimental data obtained by the method of dispersion analysis. The atomic absorption method is used to study the chemical composition of the cathode deposits. The method of dispersion analysis is used to process experimental data. The composition of cathode deposit impurities is studied using x-ray spectral microanalysis. The study of the chemical composition of cathode deposits has shown that their main components are gold, silver, copper, lead, as well as non-metallic impurity compounds (CaO, SiO2, etc.). It is found that the optimal concentration of hydrochloric acid for cleaning gold-containing cathode deposits from heavy non-ferrous metals is 371 kg/m3; the degree of copper transition to solution is 69.06%, lead - 93.9%. The calculation of the expected mass fraction of precious metals in the alloyed gold demonstrates an increase in the mass fraction of gold by 14.08%, silver - by 17.46%. The study of the chemical composition of cathode deposits has also revealed that the main impurities that affect their subsequent processing are copper and lead. The latter fall into the ingot of alloyed gold, which is the target product of gold-bearing ore processing and complicate subsequent refining. The dispersion analysis of experimental data shows that solvent concentration significantly affects the transition degree of heavy non-ferrous metals to the solution starting from the value of 20.1 kg/m3. It is shown that the proposed method allows to increase the content of precious metals in the alloyed gold by 31.54%, as well as to perform maximum transition of copper and lead to the solution. The use of acid leaching of impurities from cathode deposits obtained by cyanide-sorption technology is one of the promising directions for improving the quality of gold-containing alloys and hence the reduction of the cost of refining services.

scholarly journals Application of Artificial Intelligence (AI) for Sustainable Highway and Road System

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Md Arifuzzaman ◽  
Muhammad Aniq Gul ◽  
Kaffayatullah Khan ◽  
S. M. Zakir Hossain
Keyword(s):  
Experimental Data ◽  
High Performance ◽  
Adhesion Force ◽  
Model Development ◽  
Real Life ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Adhesive Properties ◽  
Bayesian Optimization Algorithm ◽  
The Mean

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.

scholarly journals An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1867
Author(s):  
Tasbiraha Athaya ◽  
Sunwoong Choi
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Health ◽  
Estimation Method ◽  
Absolute Error ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Arterial Blood ◽  
Blood Pressure Waveform ◽  
The Mean ◽  
Mimic Iii

Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson’s correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.

scholarly journals Reliability estimation for drive axle of wheel loader under extreme small sample

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983684 ◽  
Author(s):  
Leilei Cao ◽  
Lulu Cao ◽  
Lei Guo ◽  
Kui Liu ◽  
Xin Ding
Keyword(s):  
Sample Size ◽  
Promising Result ◽  
Small Sample Size ◽  
Bootstrap Method ◽  
Estimation Method ◽  
Small Sample ◽  
Reliability Estimation ◽  
Fatigue Reliability ◽  
Drive Axle ◽  
Semi Empirical

It is difficult to have enough samples to implement the full-scale life test on the loader drive axle due to high cost. But the extreme small sample size can hardly meet the statistical requirements of the traditional reliability analysis methods. In this work, the method of combining virtual sample expanding with Bootstrap is proposed to evaluate the fatigue reliability of the loader drive axle with extreme small sample. First, the sample size is expanded by virtual augmentation method to meet the requirement of Bootstrap method. Then, a modified Bootstrap method is used to evaluate the fatigue reliability of the expanded sample. Finally, the feasibility and reliability of the method are verified by comparing the results with the semi-empirical estimation method. Moreover, from the practical perspective, the promising result from this study indicates that the proposed method is more efficient than the semi-empirical method. The proposed method provides a new way for the reliability evaluation of costly and complex structures.

Sign in / Sign up

Export Citation Format

Share Document