Hall-Petch and Multiple Linear Regression Equations for the Prediction of Mechanical Properties in Gamma-Based Titanium Aluminides

1996 ◽  
Vol 460 ◽  
Author(s):  
W. O. Soboyejo ◽  
A. B. O. Soboyejo ◽  
Y. Ni ◽  
C. Mercer
Keyword(s):  
Mechanical Properties ◽  
Fracture Toughness ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Titanium Aluminides ◽  
Regression Equations ◽  
Combined Effects ◽  
Wide Range ◽  
Elongation To Failure ◽  
Prediction Of Mechanical Properties

In a recent paper, Mercer and Soboyejo [1] demonstrated the Hall-Petch dependence of basic room- and elevated-temperature (815°C) mechanical properties (0.2% offset strength), ultimate tensile strength, plastic elongation to failure and fracture toughness) on the average equiaxed/lamellar grain size. Simple Hall-Petch behavior was shown to occur in a wide range of extruded duplex α2-γ alloys (Ti-48A1, Ti-48Al-1.4Mn Ti-48Al-2Mn and Ti-48Al-1.5Cr). As in steels and other materials [2–5], simple Hall-Petch equations with were derived for the above properties [1]. However, the Hall-Petch equations did not include the effect of other variables that can affect to the basic mechanical properties of gamma alloys. Multiple linear regression equations for the prediction of the combined effects of several (alloying, microstructure and temperature) variables on basic mechanical properties temperature are presented in this paper.

scholarly journals Probabilistic Approaches to the Prediction of the Effects of Multiple Parameters on the Mechanical Properties of Gamma-Based Titanium Aluminides

2000 ◽  
Author(s):  
A. B. O. Soboyejo ◽  
Y. Ni ◽  
C. Mercer ◽  
W. O. Soboyejo
Keyword(s):  
Mechanical Properties ◽  
Fracture Toughness ◽  
Titanium Aluminides ◽  
Bayes Theorem ◽  
Combined Effects ◽  
Multiple Parameters ◽  
Probabilistic Mechanics ◽  
Multiple Parameter ◽  
Regression Techniques ◽  
Strain Stress

Abstract Multiple parameter expressions of the form, Y = αoXiαi (where αo and ai are empirically-derived exponents), are presented for the prediction of the effects of multiple parameters, Xi, on the mechanical properties, Y, of gamma-based titanium aluminides. The multiparameter expressions, obtained via multiple linear regression techniques, provide single functions for the prediction of the combined effects of composition, microstructure and temperature on basic mechanical properties such as yield/ultimate tensile strength, plastic strain to failure and fracture toughness. Approximate exponential reliability functions are also presented for the prediction of the probabilities of failure/survival at various possible stress/strain/stress intensity factor levels. The use of Bayes theorem is proposed for the improvement of the exponential reliability functions as more data becomes available. Simplified analytical methods are presented for the prediction of the combined errors of prediction. The paper highlights the need for multiparameter probabilistic mechanics approaches to the prediction of basic mechanical properties.

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

Control of properties and structure of steel vessels by cooling in various media at the outlet of rolling and pressing lines

2021 ◽  
pp. 34-38
Author(s):  
R. L. Shatalov ◽  
V. A. Medvedev
Keyword(s):  
Mechanical Properties ◽  
Physical And Mechanical Properties ◽  
Metal Structure ◽  
Machine Building ◽  
Regression Equations ◽  
Wide Range ◽  
Industrial Mineral ◽  
Cooling Media ◽  
Technological Limitations ◽  
Non Destructive

When controlling the mechanical properties and structure of vessels made of carbon structural steels manufactured by hot deformation on rolling and pressing lines (PPL) of machine-building enterprises of Russia, such cooling media as water, I20 industrial mineral oil, air are used. The applied cooling media are able to provide the workpieces with a given structure with a wide range of mechanical properties. However, the cooling media have a number of technological limitations and conditions of the use, non-compliance with which leads to reject. When cooled in oil, the probability of ignition is high; when cooled in water, hardening cracks may form, and air is not always able to provide the required rate and uniformity of heat transfer to the environment. The efficiency of control of physical and mechanical properties and structure of deformed vessels made of 50 steel by cooling in TERMAT polymer aqueous solutions in different concentrations on PPL of the plant of JSC NPO Pribor was studied. The effect of varying the concentration from 2 to 9% of TERMAT polymer on the formation of metal structure, as well as physical and mechanical properties of hot-deformed vessels was studied. The results of testing the strength and plastic characteristics of vessels by destructive and non-destructive control methods are presented. According to the results of physical and mechanical properties, regression equations were obtained with at least 95% reliability of R2, which establish the relationship between the controlled plastic and strength parameters of the vessel metal`s properties. The conducted researches allowed to compare the indicators of the main physical and mechanical properties of steel vessels at the PPL outlet and to propose methods of inhomogeneity control that reduce time and material costs by 5–10% during the tests.

scholarly journals PENGARUH TINGKAT PENDIDIKAN, MOTIVASI, DAN KOMPENSASI TERHADAP KINERJA PEGAWAI DI BRI CABANG SEMARANG AHMAD YANI

2019 ◽  
Vol 11 (02) ◽  
pp. 31-47
Author(s):  
Sopi Sopi ◽  
Zumrotun Nafi'ah
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Industrial Revolution ◽  
Employee Performance ◽  
Science And Technology ◽  
Regression Equations ◽  
Linear Regression Equation ◽  
Improve Performance ◽  
Independent Variables ◽  
Life Education

Education, motivation and compensation are important things that can improve performance. This study aims to explain whether there is an influence of education, motivation and compensation on employee performance. So that through the results of this study it is expected to be a reference for leaders in managing the organization. In this study there are three independent variables namely education, motivation and compensation and one dependent variable is employee performance. At present it is in the era of industrial revolution 4.0, which is marked by; big data / giant data, internet of think, labor knowledge, and long life education. Since the beginning of the life of mankind to an infinite period, it is largely determined by the mastery of science and technology. Science and technology can not be separated from the progress of education level. Education is the base of all changes both individually, as well as countries. Employee performance is determined by the education that is owned, as high as education, the higher the performance and vice versa. The population in this study are BRI CAB employees, SEMARANG A-YANI, 60 people and all of them are sampled. The results of the analysis using SPSS 23 program statistical tools obtained multiple linear regression equation Y = 0.505 X1 + 0.175 X2 + 0.408 X3 The results of multiple linear regression equations show that there is a positive and significant influence between education on employee performance at BRI CAB. A YANI SEMARANG (t count test 6.314> t table 0.05), motivation towards employee performance at BRI CAB. A YANI SEMARANG (tcount 2,160> t table 0,05), and compensation for employee performance at BRI CAB. A YANI SEMARANG (t test 5.108> ttable 0.05). While together (simultaneously) the influence of education, motivation and compensation has an effect on and significant on the performance of employees at BRI CAB. A YANI SEMARANG (count = 44,692> ftabel = 0.05). The influence of the two research variables is very strong with a correlation value of 69.0% for employee performance at BRI CAB. A YANI SEMARANG is influenced by the motivation and compensation education of the remaining 31.0% of the employees' performance at BRI CAB. A YANI SEMARANG is influenced by other variables that affect employee performance.

scholarly journals PENGARUH LINGKUNGAN KERJA DAN KOMPENSASI TERHADAP KINERJA PEGAWAI PADA DINAS PENDIDIKAN, PEMUDA DAN OLAHRAGA KABUPATEN MAJENE

2020 ◽  
Vol 9 (2) ◽  
pp. 121
Author(s):  
Sri Indira Hartawati ◽  
Meutia A Sahur
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Work Environment ◽  
Employee Performance ◽  
Linear Regression Analysis ◽  
Youth Sports ◽  
Multiple Linear Regression Analysis ◽  
Department Of Education ◽  
Test Reliability ◽  
Regression Equations

<p><em>This research was conducted at the Department of Education, Youth and Sports of Majene Regency with the title The Effect of Work Environment and Compensation on Employee Performance. The formulation of the problem used by researchers is How the influence of the Work Environment on Employee Performance at the Education and Youth Sports Office of Majene Regency, How the influence of Compensation on Employee Performance at the Education and Youth Sports Office of Majene Regency, which variables have more influence on Employee Performance at the Education and Youth Office Majene District Sports. The research method, namely the population and sample used in this study were all employees of the Department of Education and Youth Sports of Majene Regency, which amounted to about 50 people, while the analysis method used the Validity Test, Reliability Test, Multiple Linear Regression Analysis This analysis was used to determine how much influence it had. independent variables, namely: compensation (X1), and work environment (X2) on the dependent variable, namely Employee Performance (Y). Multiple linear regression equations, Partial Significance Test (t test) and Simultaneous Test (F test). The results obtained from this study are the work environment has a significant effect on employee performance at the Department of Education and Youth Sports of Majene Regency, compensation has an effect on employee performance at the Education and Youth Sports Office of Majene Regency. and the work environment has a more dominant influence on employee performance at the Department of Education and Youth Sport, Majene Regency.</em></p><p><strong><em>Keywords: </em></strong><em>Work Environment, Compensation, Employee Performance</em></p>

scholarly journals Pengaruh Pelatihan Dan Pengembangan Terhadap Kinerja Karyawan Departemen Produksi Pada PT. Kaltim Diamond Coal Site Loa Gagak Kutai Kartanegara

2021 ◽  
Vol 9 (2) ◽  
pp. 170
Author(s):  
Wijayanti Wijayanti Wijayanti
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Employee Performance ◽  
Training And Development ◽  
Test Reliability ◽  
Regression Equations ◽  
Production Department ◽  
Data Analysis Technique ◽  
Analysis Technique

The results of the analysis using multiple linear regression equations show the following regression equation: Y = 5.622 + 0.488 (X1) + 0.329 (X2). Data collection techniques are based on the source, including primary data through observation, interviews, questionnaires or questionnaires, and include documentation. Secondary data, among others, by searching for literature in the form of previous research journals, and textbooks that are related to the variables to be studied. The data analysis technique used the validity test, reliability test, and classical assumption test. As for the data analysis technique method using Multiple Linear Regression, Correlation Analysis, Analysis of Determination (R2), hypothesis testing using the F test, T test and variable test which have the most influence. The results showed that the Training (X1) and Development (X2) variables simultaneously had a significant effect on the Employee Performance (Y) of the Production Department of PT. Kaltim Diamond Coal Site Loa Gagak Kutai Kartanegara.“The results of multiple linear regression tests simultaneously found that the Training and Development variables had a significant effect on the Employee Performance of the Production Department“at PT. Kaltim Diamond Coal Site Loa Gagak”Kutai Kartanegara.”The results of”the multiple linear regression test partially found that training and development had a significant influence on the performance of the production department“employees at PT. Kaltim Diamond Coal Site Loa Gagak Kutai Kartanegara”. The results of the most influential variable test, it is known that the training variable is the variable that has the most dominant influence on the Employee Performance of the Production Department at PT. Kaltim Diamond Coal Site Loa Gagak Kutai Kartanegara.

scholarly journals . Reflection of individual typological properties in the tremorogram (Predicting the indicators of Rusalov’s test according to the indicators of the tremorogram)

2021 ◽  
Vol 11 (5) ◽  
pp. 213-222
Author(s):  
B. A. Lobasyuk ◽  
L. N. Akimova ◽  
A. N. Stoyanov ◽  
A. V. Zamkovaya
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Coefficients ◽  
Regression Equations ◽  
Target Feature ◽  
Left Hand ◽  
Right Hand ◽  
Postural Load ◽  
Significant Regression ◽  
The Right

Rationale for choosing. An increase in physiological tremor (Tr) in emotionally saturated situations is reflected not only in fiction, but also in scientific literature. In other words, tremors and emotional responses are interconnected. Purpose. To investigate the reflection of individual-typological properties in a tremorogram using V. M. Rusalov’s mathematical modeling. Material and methods. Tr was recorded using a linear transducer. Tr was recorded under postural load (arms extended forward). The sensor was alternately placed on the outstretched right and left arms in front of oneself, under conditions of “eyes open” (operative rest). The analysis of the tremorogram (TrG) files was carried out after the end of the study using the "Analist - 2" software according to the half - period analysis algorithm. To study the individual psychological characteristics of the personality, we used the method of determining the properties of the temperament by V.M. Rusalov. Each of the many indicators of Rusalov’s test selected in the analysis was considered as a target feature (Y-s), and the amplitudes and frequencies of TrG were considered as influencing variables (sets of X - s) and multiple linear regression equations of the form were built: The parameters of the amplitude and frequency of EEG rhythms were used as Xs. Own research. In multiple regression analysis of the influence of TrG indices of the right hand on the indices of Rusalov’s test, 12 statistically significant regression coefficients were determined, and 11 statistically significant regression coefficients for the left hand. After obtaining the diagnostic equations of multiple linear regression, describing the influence of TrG indicators on the indicators of Rusalov’s test, an attempt was made, using these equations, to obtain the indicators of Rusalov’s test, using the tremor indicators. On average, the% discrepancy between the determined and predicted indicators was 97.42% for the right hand, and 101.98 for the left. Conclusions. 1. With the use of diagnostic equation, it was possible to predict the indicators of psychological testing according to Rusalov’s test by the indicators of tremor of the right and left hands.2. Influence of Rusalov’s test indicators on TrG indicators were less in modulus than the influence of TrG indicators on the indicators of Rusalov’s  test, i.e. did not participate in the control of the mechanisms of TrG generation.3. The results obtained indicate that tremor indicators contain information about the subject-activity and communicative aspects of temperament according to V. M. Rusalov.

Effects of Hearing Levels at the Activator and Other Frequencies upon the Expected Levels of the Acoustic Reflex Threshold

1983 ◽  
Vol 48 (1) ◽  
pp. 11-17 ◽  
Author(s):  
Stanley A. Gelfand ◽  
Neil Piper ◽  
Shlomo Silman
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Clinical Applications ◽  
Hearing Level ◽  
Regression Equations ◽  
Acoustic Reflex ◽  
Reflex Threshold ◽  
Acoustic Reflex Threshold

Multiple linear regression equations were derived to define the expected levels of acoustic reflex thresholds (ARTs) at 500, 1000, and 2000 Hz predictable from hearing levels as 500–4000 Hz in 646 ears. When the hearing level at the activator frequency is ⩽50 dB HL, ARTs tend to be best defined by a constant value of 86–90 dB HL with small adjustments due to the loss at the activator or higher frequencies. When the hearing level at the activator frequency is 55–90 dB HL, the ART is best predicted by a constant plus the degree of loss at that frequency plus the loss at 4000 Hz. These data present the clinician with empirically derived bases for determining the effects of other frequencies on a given ART. Application of these findings permits the clinician to apply known relationships in determining whether a particular ART is representative of those expected for normal and/or cochlear impaired ears, elevated or depressed, without having to rely on vague intuitions of such relationships. Clinical applications are discussed.

Interpretation of Hydroxylic Solvent Effects Based on Correlations with Solvent Parameters. Reaction of Et3N with EtI

1994 ◽  
Vol 59 (4) ◽  
pp. 898-904 ◽  
Author(s):  
António R. T. Calado ◽  
Lídia M. V. Pinheiro ◽  
Lídia M. P. C. Albuquerque ◽  
Raquel M. C. Goncalves ◽  
Martí Rosés ◽  
...  
Keyword(s):  
Energy Density ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Solvent Effects ◽  
Rate Constants ◽  
Cohesive Energy ◽  
Regression Equations ◽  
Solvatochromic Parameters ◽  
Cohesive Energy Density ◽  
Solvent Parameters

Hydroxylic solvent effects on 20 rate constants, k, of the Menschutkin reaction of Et3N with EtI are unravelled and rationalized by means of multiple linear regression equations. To perform this analysis new k values in 2 mono- and 9 dialcohols are obtained. New values are also presented for the Kamlet and Taft solvatochromic parameters π*, α and β of 1-hexanol. The results show that the solvent dipolarity, polarizability and cohesive energy density are the main properties influencing the reactivity, for the set of studied solvents.

scholarly journals Does the Preferred Walk-Run Transition Speed on Steep Inclines Minimize Energetic Cost, Heart Rate or Neither?

2021 ◽  
pp. jeb.233056
Author(s):  
Jackson W. Brill ◽  
Rodger Kram
Keyword(s):  
Heart Rate ◽  
Linear Regression ◽  
Energetic Cost ◽  
Regression Equations ◽  
Rate Data ◽  
Heart Rate Data ◽  
Transition Speed ◽  
Wide Range ◽  
Incremental Protocol ◽  
Preferred Transition Speed

Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0° and 15° trials and on day 2, 5° and 10°. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0°, 5°, and 10°, but the two converged at 15°. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.

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