Effect of chemical modification of wood flour on the mechanical properties of wood-plastic composites

235 Pages·2015·6.61 MB·English

by  Valentina Prigoževa

Agronomy Research

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ISSN 1406-894X
CONTENTS

VI PRODUCTION ENGINEERING...................................................................... 627
R. Chotěborský and M. Linda
FEM based numerical simulation for heat treatment of the agricultural tools ............ 629
H. Kallakas, M.A. Shamim, T. Olutubo, T. Poltimäe, T.M. Süld,
A. Krumme and J. Kers
Effect of chemical modification of wood flour on the mechanical properties
of wood-plastic composites ......................................................................................... 639
A. Krofová and M. Müller
Influence of dusty micro-particles contamination on adhesive bond strength ............ 654
M. Lisicins, V. Mironovs, I. Boiko and V. Lapkovskis
Sandwich wall constructions made of perforated metallic materials .......................... 662
V. Maksarov
Improving the accuracy of manufacturing of hydraulic power cylinders using
vibration-proof cutting tool ......................................................................................... 671
Č. Mizera, D. Herák, M. Müller and P. Hrabě
Mechanical behaviour of polymeric composite with fibres of false banana
(Ensete ventricosum) ................................................................................................... 680
I. Muizniece, L. Vilcane and D. Blumberga
Laboratory research of granulated heat insulation material from coniferous
forestry residue ............................................................................................................ 690
M. Müller
Hybrid composite materials on basis of reactoplastic matrix reinforced with
textile fibres from process of tyres recyclation ........................................................... 700
V.V. Pelenko, E.I. Verboloz and A.V. Baranenko
The theoretical analysis and optimization of the cutting knife-grille pair
parameters in the screws .............................................................................................. 709
P. Valášek
Polymeric microparticles composites with waste EPDM rubber powder ................... 723
J. Volf, J. Svatos, P. Koder, V. Novak, S. Papezova, V. Ryzhenko
and J. Hurtecak
Pressure distribution measurement system PLANTOGRAF V12 and its
electrodes configuration .............................................................................................. 732

624
VII ERGONOMICS ................................................................................................. 739
R. Arvola and Ü. Kristjuhan
Workload and health of older academic personnel using telework ............................. 741
M. Hruška, J. Kuchař, L. Libich and P. Jindra
Assessment of the impact of the shape of the handle on the ergonomics of
operating a handbrake .................................................................................................. 750
P. Kic
Dust pollution in University offices ............................................................................ 759
T. Koppel, I. Vilcane, M. Carlberg, P. Tint, R. Priiman, K. Riisik,
H. Haldre and L. Visnapuu
The effect of static magnetic field on heart rate variability –
an experimental study .................................................................................................. 765
E. Merisalu, M. Vähi, S. Kinnas, M. Oja, K. Sarapuu, O. Novikov,
M. Pärnapuu, E. Indermitte, K. Lea and H. Orru
Job specific risk factors, demographic parameters and musculoskeletal
disorders among military personnel depending on type of service ............................. 775
V. Nídlová and J. Hart
Reliability of biometric identification using fingerprints under adverse
conditions .................................................................................................................... 786
Õ. Paas, K. Reinhold and P. Tint
Estimation of safety performance by MISHA method and the benefits
of OHSAS 18001 implementation in Estonian manufacturing industry ..................... 792
J. Paju and S. Kalle
An analysis of engineering students’ knowledge on the topic of occupational
health and safety .......................................................................................................... 810
V. Pille, V.-R. Tuulik, S. Saarik, P. Tint, T. Vare and R. Sepper
Work-related musculoskeletal symptoms in industrial workers and the effect
of balneotherapy .......................................................................................................... 820
I. Roja, Z. Roja and H. Kalkis
Team learning and self-management for video display terminal employees
with chronic neck-shoulders pain ................................................................................ 829
J. Sepp, M. Järvis, P. Tint, V. Siirak and K. Reinhold
EMG measurements of thumb muscles of nurses and caregivers ............................... 836
I. Vilcane, V. Urbane, P. Tint, J. Ievins
The comparison study of office workers’ workplace health hazards in
different type of buildings ........................................................................................... 846

625
626
VI PRODUCTION ENGINEERING

627
628
Agronomy Research 13(3), 629–638, 2015

FEM based numerical simulation for heat treatment of the
agricultural tools

R. Chotěborský1 and M. Linda2,*

1Czech University of Life Sciences in Prague, Faculty of Engineering, Department of
Material Science and Manufacturing Technology, Kamýcka 129, CZ-16521 Praha –
Suchdol, Czech Republic
2Czech University of Life Sciences in Prague, Faculty of Engineering, Department of
Electrical Engineering and Automation, Kamýcka 129, CZ-16521 Praha – Suchdol,
Czech Republic; *Correspondence: [email protected]

Abstract. Quenching as a heat treatment method is commonly used to control the mechanical
properties of steels. This article deals with the modelling and simulation of quenching of steel
chisel using a multi–phase model. The process of the heat treatment is non stationary phase due
to temperature variation with time. In this study, the problem of heat transfer in three dimensional
phase was transformed into a two dimensional axisymmetric case. ElmerFem solver was used for
the heat transfer through different cooling media such as water, oil and salt bath. The results from
heat solver were used for austenite transformation modelling by applying Johnson–Mehl–
Avrami–Kolmogorov equation in TTT diagram. The Scheill's decomposition was used for
anisothermal transformation of austenite. The hardness prediction was done according to simple
mixture rule where total hardness of the steel was calculated based on volume of the phases and
their Vickers hardness.

Key words: numerical simulation, FEM, heat treatment, ElmerFEM.

INTRODUCTION

Quenching is used as a heat treatment method for controlling the mechanical
properties of steel such as tensile strength, toughness and hardness. The quenching
process promotes the formation of different microstructures namely ferrite, pearlite,
bainite and martensite that depend on the cooling rate as well as the chemical
composition of the steel. The quenching application of the material is subjected to heat
treatment above the austenitization temperature (approximately 900 °C) which involves
continuous and rapid cooling in a quenching media such as water, air and oil. During
quench hardening process, heat flux is rapidly transfered to the coolant which varies in
time hence the HTC (heat transfer coefficient) cannot be calculated or measured by
standard techniques. In such cases, the effective procedure is the formulation of the
boundary inverse heat conduction (Telejko, 2004; Buczek & Telejko, 2013).
Constitutive modelling of the quenching process can be performed within the scope
of standard generalized materials under the assumption that the thermodynamic state of
the material can be completely defined by a finite number of state variables
(Archambault & Azim, 1995; Fall et al., 2011; Hasan et al., 2010). Phase transformation
629
from austenite to martensite is non diffusive process meaning that the amount of volume
fraction is only a function of temperature which can be described by the Koistinen–
Marburger law (Eq. 1). On the other hand, microstructures such as ferrite, pearlite and
bainite formations are diffusion controlled transformation which are time dependent.
The diffusive transformation kinetics are described by Johnson–Mehl–Avrami–
Koglomorov (JMAK) equation (Eq. 2). The evolution of these phases transformation can
be predicted through an approximate solution using data from Time–Temperature–
Transformation diagrams (TTT).

𝑉 = 1−𝑒−𝛼×(𝑀𝑠−𝑇) (1)
𝑀

𝑉 = 1−𝑒−𝑘×𝑡𝑛 (2)
𝑃,𝐵

where: α and M are both constants determined by material type, k is the overall rate
s
constant that generally depends on temperature, n is the Avrami’s exponent
(Kolmorgorov, 1937; Avrami, 1939a; Avrami, 1939b; Johnson & Mehl, 1939; Avrami,
1940a; Avrami, 1940b; Marder & Goldstein, 1984; Kirkaldy, 2007; Sinha et al., 2007).
This article describes the modelling and simulation of quenching of steel chisel
using a multiphase constitutive model proposed by (Çetinel et al., 2000; Ferguson et al.,
2005; Carlone et al., 2010).

MATERIALS AND METHODS

The process of heat transfer during quenching of a steel chisel (Fig. 1) is
nonstationary due to the variation of temperature with time. In this work the problem of
heat transfer in a three dimensional phase was examined.

Figure 1. Real chisel computerization.

The nonstationary problem of heat transfer within a component in the quenching
process is described mathematically by simple differentiation with respect to the volume.
Based on that the heat transfer equation (Eq. 3) was derived as follows:

𝜕 𝜕𝑇 𝜕 𝜕𝑇 𝜕 𝜕𝑇 𝜕𝑇
(𝑘 )+ (𝑘 )+ (𝑘 )+𝑄 = 𝜌×𝑐× (3)
𝜕𝑥 𝜕𝑥 𝜕𝑦 𝜕𝑦 𝜕𝑧 𝜕𝑧 𝜕𝑡

630
where: k – thermal conductivity; Q – is the inner heat–generation rate per unit volume;
T – temperatuure; q – heat transfer coefficiente; 𝜌 – densitye; c – heat capacitye; t – time.

The Neumann boundary conditions were used for the simulation of the heat cycles
in quenching media like air, water and oil whiles the heat flux was determined
experimentally in cylinder shape samples by inverse methods (Telejko, 2004).
The equilibrium transformation temperatures during cooling were also determined
experimentally by thermal analysis. The ferrite transformation started below the A
c3
temperature whiles the pearlite transformation occurred at the A temperature when
c1
a volume fraction of pro–eutectoid ferrite reached an equilibrium volume fraction. The
bainite and martensite transformations occurred below bainite and martensite
temperatures respectively. Table 1 gives the transformation temperatures of different
steel samples.

Table 1. Transformation temperatures of different steel samples
Temperature phase change A =T (°C) A =T (°C) T (°C) T (°C)
c3 F c1 P B M
High boron steel – B1 810 741 606 382
High boron steel – B2 830 743 593 411
Boron27 840 675 525 335

Table 2. Chemical composition of different steel samples
Chemical composition C Si Ni Cr Mn Mo Cu
High boron steel – B1 0.6 0.3 0.05 0.4 0 0.01 0
High boron steel – B2 0.3 0.3 1 1.5 0 0.05 0
Boron27 0.2 0.2 0.1 0.3 1.3 0 0

The ElmerFEM solver (CSC – IT Center for Science (CSC), 2013) was used for
calculation of thermal field of the steel samples. The simulation results were obtained as
matrix of nodes and temperatures. Two kinds of mathematical models were used for
deducing microstructure field from temperature field based on TTT (Time, Temperature,
Transformation) curve which is used for kinetic transformation of austenite at constant
temperature. In addition, CCT (Continuous Cooling Transformation) curve was used for
kinetic transformation of austenite in water and oil quenching media (Smoljan, 2006;
Malinowski et al., 2012).
The diffusional transformation reaction was based on equation (Eq. 2). The
constants for these equations were determined from a nonlinear optimization of the
experimental data. Table 2 shows the values used for the three transformation products
resulting from a diffusion–controlled reaction of ferrite, pearlite and bainite. The
temperature dependencies of constants were fitted by Gaussian function and these
dependencies were used as input algorithm (Marder & Goldstein, 1984; Çetinel et al.,
2000; Ferguson et al., 2005). The C–curves of a calculated IT diagram for boron steel
using equation (Eq. 2) for each product structure and the data points indicate the
determined experimental values for the starting and final transformations during
isothermal heat treatments.
The actual temperature variation is continuous cooling rather than isothermal
variation. But austenite transformation under M temperature can develop a partial
s
631
transformation of bainite. By applying Scheil superposition principle (Scheil, 1935), the
actual continuous cooling transformation can be calculated by isothermal transformation
model. Here the time period was discretized based on the assumption that within each
time step is Δt at constant temperature involving isothermal transformation. For the
corresponding constant T, were parameters b, n and τ (transformation starting time, i.e.
i i i i
incubation period). By dividing the time step Δt by incubation period τ, increment of
i
inoculation rate ΔE was the volume transformation during the former time step V. By
i i
substituting it into equation (Eq. 4) then the time period was obtained for the volume
transformation reaching V under T isothermal transformation condition that is virtual
i i+1
time t as described below.
i+1

1
−𝑙𝑛(1−𝑉) 𝑛
𝑖 𝑖+1 (4)
𝑡 = [ ]
𝑖+1 𝑘
𝑖+1

The microstructures were calculated from arrays {T (t)} and {V (t)} during the
simulation period. Equations (Eq. 5 to Eq. 9) described below were included in the
computer algorithm.

0.8−𝐶 −1 0.8−𝐶 −1
𝑉𝑓 = ( ) +( ) (5)
𝑚𝑎𝑥 0.8 0.8

𝑛
𝑉𝑓 = ∑−𝐾𝑓 ×𝑁𝑓×𝑡𝑁𝑓−1 ×𝑒−𝐾𝑓×𝑡𝑁𝑓 ×(1−𝑉𝑓 ) (6)
𝑚𝑎𝑥
𝑖=1

𝑛
𝑉𝑝 = ∑−𝐾𝑝×𝑁𝑝×𝑡𝑁𝑝−1 ×𝑒−𝐾𝑝×𝑡𝑁𝑝 ×(1−𝑉𝑓) (7)
𝑖=1

𝑛
𝑉𝑏 = ∑−𝐾𝑏×𝑁𝑏×𝑡𝑁𝑏−1 ×𝑒−𝐾𝑏×𝑡𝑁𝑏 ×(1−𝑉𝑓−𝑉𝑝) (8)
𝑖=1

𝑉𝑚 = 1−𝑒−𝛽×(𝑇𝑚𝑠𝑡𝑎𝑟𝑡−𝑇) ×(1−𝑉𝑓−𝑉𝑝−𝑉𝑏) (9)

where: Vf is the volume of ferite phase, Vp is the volume of pearlite phase, Vb is the
volume of bainite phase, Vm is the volume of martensite phase, Kf (Kp and Kb) are the
overall rate constant of feritic, pearlitic and bainitic transformation that generally
depends on temperature, Nf (Np and Nb) are the Avrami’s exponent for feritic, pearlitic
and bainitic transformation that depends on temperature, t is the time, T is the
temperature, T is the temperature martensite start transformation and β is the
mstart
coefficient of martensite transformation volume that depends on temperature.
632