Industrial Trial Results of Nb-Bearing or 430 MPa required by rebar makers considering aging

4. Industrial Trial Results of Nb-Bearing or 430 MPa required by rebar makers considering aging

Rebars

problem and normal distribution of mass production. As we can see from Fig. 5, both structures of surface

4.1 Trial Results of Nb-Bearing HRB400E and center are composed of ferrite and pearlite, fulfilling

Table 3 gives the trial results of Nb-bearing the requirements of newly revised standard. Figs. 6 and 7 HRB400E, including chemical compositions, key

show different microstructure type given equal chemical production processing parameters and test results.

compositions and processing conditions. For small size, As we can see from Table 3, Nb additions are set

big austenite grain size resulted from high final rolling from 0.025% to 0.037%, and Mn contents are set from

temperature and quick cooling rate during transformation 1.12-1.50%. Normally, controlled rolling is seldom used

would promote formation of bainitic microstructure. Figs. for rebar rolling, but controlled cooling is widely used

8 and 9 show the effect of reheating temperature on for mass production. So, the two main processing

microstructure, and found higher reheating temperature parameters for rebar production are reheating would result in high volume of bainitic microstructure. Fig. temperature and entry cooling bed temperature. Here

10 shows the tensile curves with ferrite plus pearlite and

86 Strengthening Effects of Niobium on High Strength Rebars

Table 3 Trial results of Nb-bearing HRB400E.

Tensile test No.

Key Temp.

Size, mm C, %

Mn, %

Nb, %

TS A, % 1-1 18 0.21 1.12 0.027 1,129

Reheating T.

Cooling T.

426-432 645-649 27.5-28.0 2-2 18 0.24 1.50 0.025 1,120

428-443 642-657 23.5-26.5 2-3 32 0.24 1.50 0.025 1,130

440-457 638-650 18.0-23.0 3-1 16 0.23 1.25 0.030 1,100

428-442 627-621 23.5-26.5 3-2 16 0.23 1.25 0.030 1,130

419-435 610-623 23.0-26.5 4-1 25 0.22 1.45 0.037 1,190

647, 634 18.0-20.0 4-2 25 0.22 1.45 0.037 1,225

643, 633 15.5-18.0 5-1 12 0.23 1.35 0.022 1,165

430-475 590-620 27.0-32.0 5-2 22 0.23 1.35 0.022 1,220

435-450 610-620 21.0-27.0

Fig. 5 Microstructure of sample 1-3’s surface and middle position.

Fig. 6 Microstructure of sample 2-1. Fig. 7 Microstructure of sample 2-3.

Strengthening Effects of Niobium on High Strength Rebars

Fig. 8 Microstructure of sample 4-1. Fig. 9 Microstructure of sample 4-2.

Fig. 10 Tensile curves of sample 1-3 and sample 4-2.

bainitic microstructure. Based on past production because it is contrary with grain refinement effect based experiences, continuous yielding appears when volume

on Hall-Petch formula. Here both microstructure of of bainitic microstructure surpasses 20.0 percent.

HRB500E and HRB600 is composed of ferrite plus pearlite. According to chemical extraction analysis, most

4.2 Trial Results of Nb-Bearing HRB500E and HRB600 Nb additions are in solution before transformation, which

Table 4 gives the test results of HRB500E and HRB600 would decrease ferrite start transformation temperature with V microalloying process and Nb plus V and reduce volume of ferrite. So, high volume of pearlite microalloying process respectively. As we can see, main

would improve upon tensile-to-yield ratio. problem for V-bearing HRB500E and HRB600 rebars is

5. Analysis and Discussion

tensile-to-yield ratio for small sizes, and test results of

5.1 Reheating Temperature

tensile-to-yield ratio are lower than required 1.25 for earthquake resistant area. But with Nb plus V process,

For hot rolled rebars, one of the most important both test results of 12 mm and 25 mm are higher than 1.25.

processing parameters is reheating temperature because The effect of Nb on tensile-to-yield ratio is very interesting

controlled rolling is seldom used for rebar production.

88 Strengthen ning Effects o of Niobium o on High Stren gth Rebars

Table 4 Test t results of V-b bearing HR500 0E rebars.

Grade Diameter, mm V

δgt , % TS/TS

22.0 15.4 1.29 HEB500E

According t to common s sense for mic croalloyed ste eels, quo oted was prov vided by Palm miere in 1994 4, which also o the first ste ep to make g good use of Nb and V i is to

nee eds higher reh heating temp erature for 0 .03% Nb, up p ensure solut ion of added Nb, V and th heir combinat tion.

to 1 1,300 °C. But t, another solu ubility equati ion published d According t to established d solubility p product equat tion,

by K. Narita sh hows needed d dissolution temperature e Nb requires s higher rehe eating tempe erature comp ared

that t is far less s than above e two solubi ility product t with V, whic ch is one ma ain barrier for r application. For

11 gives calc culation resul lts for 0.23% % Nb-bearing steels, most researchers p prefer to emp ploy

equ uations. Fig.

0.005% N, a C, 0 and dissolutio on temperatur re for 0.03% % solubility fo ormula create ed by Irvine i in 1967, requ uired

Nb is about 1,23 30 °C.

reheating te emperature to o dissolve 0. 03%Nb is ab bout L Log[Nb][C + 12/14N] = 2. .26 - 6770/T .........Irvine 1,230 °C fo r given 0.23% % C and 0.00 5% N ( as sh hown

2.06 - 6700/T T ..................... .....Palmiere from Fig. 1 11). Another solubility ex xpression wi idely

lo og[Nb][C] = 2

Log[Nb][C] = L

3.42 - 7900/T T..................... .....K. Narita

Fig. 11 Diss olution temper rature for Nb-b bearing HRB4 400E by Irvine e formula.

Strengthening Effects of Niobium on High Strength Rebars

Table 5 Test results of yield strength with different reheating temperature.

Rough start rolling T, °C

The best way to validate the empirical equation is to  It is more likely to bring bainitic microstructure test through production practice. Table 5 gives test

for small size rebars;

results with different reheating temperatures. For rebar  In addition, high Mn contents contribute to production, steel mills normally take start rolling

bainitic microstructure formation. temperature as benchmark to set reheating temperature,

Based on above observations, we can infer the and reheating temperature is 50 °C higher. As we can

reason for bainitic microstructure from γ→α see, higher reheating temperatures contribute to no

transformation. Firstly, higher reheating temperatures big increase for yield strength, on the contrary,

mean more solution of Nb and coarsening austenite higher reheating temperatures lead to lower yield

grain size, and both contribute to formation of bainitic strengths.

microstructure. Secondly, for big size rebar, cooling Why production practice is not consistent with

rate in the middle is much slower than that of surface, calculation results of solubility product equation? As

so high temperature means big austenite grain size in we know, with the increase of reheating temperature,

the middle. Thirdly, cooling rate of small size rebar is austenite grain would grow and even coarsen while all

quicker than big size during transformation process, niobium additions start to dissolve before rolling,

which makes ferrite transformation difficult to which brings contrary contribution to yield strength.

complete due to short holding time in ferrite transformation zone. For the effect of Mn contents, it

5.2 Mechanism of Bainitic Microstructure and Control is clear that high Mn contents would inhibit ferrite Measure transformation, and then promote formation of bainitic

As shown from Fig. 10, with certain amount of microstructure. Fig. 12 shows the effect of austenite bainitic microstructure, tensile curve shows grain size before phase transformation. continuous yielding (no yield point plateau). For this

Considering actual production conditions, the kind of rebar, end users refuse to accept because they

measures to control bainitic microstructure are as think safety margin is not enough and may put

follows:

building at risk. By observation, following factors (1) To control austenite grain size before were found to contribute to formation of bainitic

transformation, water cooling is very effective; microstructure:

(2) If available, controlled rolling in lower rolling  High reheating temperature, and resulted higher

temperature will promote transformation of ferrite, final rolling temperature;

and at the same time avoid bainitic microstructure;  Bainitic microstructure appears in the center of

(3) If no water cooling is available, elaborate design big size, instead in the middle;

on Mn contents and reheating temperature is needed.

90 Strengthening Effects of Niobium on High Strength Rebars

Fig. 12 Effect of austenite grain size on transformation.

Fig. 13 Strengthening effects of Nb, Ti and V.

5.3 Strengthening Effects of Nb on Rebars precipitate, and contribute to both yield and tensile strengths.

According to early stage reports, as shown from Fig. YS(MPa) =

53.9 + 32.3%Mn + 83.2%Si + 354%Nf + 17.4d - 1/2 grain refinement plus weak precipitation, but

13, the main strengthening effects of Nb are strong

TS(MPa) = production data of Nb-bearing HRB500E and 294 + 27.7%Mn + 83.2%Si + 3.85%pearlite + 7.7d - 1/2

HRB600 demonstrate high tensile-to-yield ratio, How do we explain this abnormal phenomenon? which is contrary to calculation results of Hall-Petch

Firstly, most research of Nb-bearing steels is about low formula below. By comparison, we find rolling

carbon flat product, and low temperature and large conditions of long product are totally different with

reduction is the typical feature with recrystallization and flat product. For rebar production, higher final rolling

nonrecrystallizaition rolling. However, final rolling temperature and fast rolling speed are typical features,

temperature for rebar is far higher than Tnr, so we cannot which would affect subsequent transformation. On the

obtain pancaked austenite grain and grain refinement cooling bed, fine and uniform Nb(C, N) particles will

effect is small. What is more, more niobium contents

Strengthening Effects of Niobium on High Strength Rebars

after final rolling exist in solution, which will increase transformation volume increase volume of pearlite hardenability and decrease ferrite transformation start

volume. That is the reason why Nb microalloying is temperature. Combining two reasons, transformation

more effective to improve upon tensile-to-yield ratio; strengthening and precipitation strengthening are more

(5) For rebar, precipitation strengthening is more remarkable for rebars.

marked than that of low carbon flat product.

6. Conclusions

References

Considering that newly revised rebar standard will [1] Hey, A., Weise, H., and Wilson, W. G. 1981. “Production come into operation on November 1st, 2018, market and Application of High Strength Concrete Reinforcing Bar.” Meeting Processing of Niobium 1981.

demand on VN alloy will push high vanadium alloy [2] Helmut, W. 1997. “Weldable High-Strength Steels for price. So, it is necessary to develop alternative

Reinforcing Bars.” In Proc. Microalloying 75 Union production process with Nb for cost saving. Compared

Carbide Corp.

[3] with V microalloying process, Nb can obtain equal or Russwum, D., and Wille, P. 1995. “High Strength, Weldable Reinforcing Bars.” In Proceedings of

higher strengthening effect by process optimization.

Microalloying.

Based on production practice, following conclusions [4] DeArdo, A. J. 2001. “Fundamental Metallurgy of can be obtained:

Niobium in Steel.” In Proceedings of the International

(1) Nb demonstrates positive effect to anti-seismic Symposium 2001, Orlando, Florida, USA, December 2-5, 2001.

rebars, in particular, Nb plus V alloy design makes it [5] Hoh, B. 2010. “Some Aspects in the Production of possible to produce anti-seismic HRB600 grade;

Microslloyed Steels.” In Proceedings of the International (2) Unlike low carbon bainitic microstructure for

Symposium on Niobium Bearing Structural Steels, high strength pipeline steel, bainitic microstructure of Singapore, 2010. [6] Morrison, W. 2010. “Historical Perspective on the Use of

Nb-bearing HRB400E leads to continuous yielding Niobium Microalloying in Structural Steels.” In effect. With controlled cooling after rolling, bainitic

Proceedings of the International Symposium on Niobium microstructure can be controlled and totally

Bearing Structural Steels, Singapore, 2010. [7] Baumgardt, H., de Boerm, H., and Heisterkamp, F. 1981,

eliminated; “Review of Microalloyed Structural Plate Metallurgy -

(3) As shown from production practice, Nb-bearing Alloying, Rolling, and Heat Treatment.” in Proceedings rebars do not necessarily need higher reheating

of the International Symposium, Niobium'81, held in San temperature, further research is needed;

Francisco, California, November 8-11, 1981.

[8] (4) Due to higher finish rolling temperature, grain ZHANG, Y. Q., GUO, A. M. 2015. “Application

Research of Nb Microalloying on Medium and High refinement effect can be lower than anticipated. In the

Carbon Long Products.” In Proceedings of HSLA Steels meanwhile, Nb in solution will decrease ferrite

2015, HangZhou, China.

Journal of Mechanics Engineering and Automation 8 (2018) 92-102

doi: 10.17265/2159-5275/2018.02.006

DAVID PUBLISHING

Estimating Emotion for Each Personality by Analyzing BVP

1 2 Emi Takemoto 3 , Yusuke Kajiwara and Hiromitsu Shimakawa 1. Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu 525-0059, Japan

2. Faculty of Production Systems Engineering and Sciences, Komatsu University, Komatsu 923-0921, Japan 3. College of Information Science and Engineering, Ritsumeikan University, Kusatsu 525-0059, Japan

Abstract: This research estimates emotions of university students from their BVP (blood volume pulse). Negative emotion of university students causes school dropout, which is becoming a serious problem in Japan. It is indispensable for school staffs and counselors to know when and where students have negative emotion in the campus. Since BVP signals along with emotion changes vary with personality types, we build a model dependent on personality type, to estimate student emotion from characteristics of blood volume signals. Experimental results show that the model for each personality type improves the accuracy of emotion estimation for new students. Positive or negative emotion estimated from BVP signals contributes to enhancement of campus environment by school counselors.

Key words: Emotion, school dropout, BVP, personality, Big Five.

1. Introduction  arousal or emotional states triggered by school-based stimuli [3]. This research focuses on these functional

University dropout is a serious social problem in conditions. Students who drop out of school are Japan. According to a survey conducted by the assumed to be not able to deal well with negative Ministry of Education, Culture, Sports, Science and emotions and situations causing them. Technology, the rates of student dropout were 0.42% M. E. Pritchard and G. S. Wilson reported the for elementary school, 2.83% for junior high school, combined influence of emotional health had a 1.49% for high school and 2.9% for university [1, 2]. significant effect on intent to drop out [4]. Therefore, According to Kearney & Silverman, youths are the university should be aware of the emotional health considered to drop out of school for one or more of the condition of the students so that students can maintain following reasons (functional conditions):

a positive mood.

 To avoid school-based stimuli that provoke a This research estimates when and where general sense of negative affectivity (anxiety and universities students have negative emotions leading depression); them to school dropout. The estimation of the time  To escape aversive school-based social and/or and places causing negative emotion enables evaluative situations; university staff to know what kind of events in campus  To pursue attention from significant others; activities bring them negative emotion. They can  To pursue tangible reinforcers outside of school. provide the students with mental care, such as The first two functional conditions refer to school emotional support and introduction of counseling dropout behavior maintained by negative agency, which prevents the students from dropping out. reinforcement, or the reduction of unpleasant physical Emotions are estimated through periodical

Corresponding author: Emi Takemoto, M.Sc., research inspection with questionnaires, behavior observation,

fields: human sensing and data engineering. and measurement of physiological responses. Since

Estimating Emotion for Each Personality by Analyzing BVP

frequent questionnaires are a burden to students, they estimated only with the student answering a quick are not suitable to finely grasp emotions changing over

personality test.

time. Settlement of equipment, such as cameras is In this research, experimental results show necessary to observe behaviors. However, it records

classifiers for each personality type improved the the behavior of many students, which violates their

accuracy of emotion estimation for new users. For privacy. On the other hand, physiological responses

each personality type, there was a difference in can be obtained through measurement of biological

estimation accuracy, the pattern of the physiological data using a wearable device. Biological data response, and important variables. Among elements correlated with emotion enables us to estimate

composing personality, the extroversion and the emotions online. The combination with positioning

neuroticism seem to play a vital effect on the tools, such as GPS and WIFI identifies the place where

estimation accuracy. People strongly extroverted are negative emotions occur.

likely to have positive emotion, while those who have

A classifier based on machine learning is a high neuroticism are likely to be sensitive to promising means for the estimation of emotion from

stimulation. It is inferred that there was a difference in measured biological data. However, the transition

biological data and variables of importance because pattern of biological data for the occurrence of specific

neuroticism certainly affects heart rate and LF emotion varies with individuals [5]. If we want to

(low-frequency) component/HF (high-frequency) estimate emotions from physiological responses, it is

component of heart rate variability, while extroversion, necessary to train a classifier with the biological data

openness, and agreeableness certainly affect the brought by emotions of each student in advance for a

natural log of LF.

certain period of time [6]. In a classifier trained with

A classifier for each personality type would tell the biological data of any student, the estimation accuracy

time and place for which university students have would be significantly low, while training of a

negative emotion in an on-line manner. For students classifier with biological data of each student is a big

who have negative emotions, such as anger and burden for the student. We need to overcome the

sorrow, it would be possible to provide support, such problem to estimate emotion from a physiological

as keeping an eye on them, giving them a phone call, response of students.

and introducing them to a counseling agency. To solve this problem, this research trains a

The rest of the paper is structured as follows. In classifier with biological data collected from students

Section 2, we show existing research about of identical personality, because transition patterns of

determinate emotions. In Section 3, we show the use physiological response depend on personality types [7,

case and method of my study. In Section 4, we show 8]. Students of various kinds of personalities have

the experimental result. In Section 5, we discuss from diversity in patterns of the physiological response to

experimental result. Finally, we conclude in Section 6. the occurrence of a specific emotion. If we train a