机械可靠性英文文献及翻译Word格式.docx
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tedious
mathematical
calculation
more
economical
than
experimental
methods.
It
can
be
used
as
a
complement
traditional
methods.
Keywords—MechanicalReliability;
Finite
Element
Method;
Carlo
I.
INTRODUCTION
Based
national
standard
GB/T
3187-
1994
[1],
been
defined
as
“the
probability
that
product
will
satisfactorily
perform
its
intended
function
under
given
circumstances
for
specified
period
time”.
Reliability
viewed
one
most
critical
parameters
the
products.
For
example,
it
said
in
US
that
will
focus
competition
future
Most
Japanese
companies
take
reliable
their
main
objective
company
development.
Therefore,
accurately
predict
product,
optimize
design
based
reliability
requirements
very
meaningful.
many
parts.
A
system’s
is
determined
parameters,
such
number
components,
quality
components
also
how
those
are
assembled.
related
material
properties,
manufacturing
technologies,
dimension
tolerances
so
on.
references
currently
available
are
focused
theoretical
derivation
calculation,
however,
due
complexity,
it
Normally
structural
maximum
stress
and
material
strength
distributed
with
certain
density.
Figure
1
shows
an
example
distribution,
in
requires
mathematical
calculation.
Another
obtain
which
f
（σ
）
g（
s）
represent
experiments,
which
requires
enough
samples
testing
time,
thus
only
applicable
to
systems.
This
strength
an
illustrates
simulation.
stress
strength,
respectively.
In
Figure
1（a）,
since
always
larger
structure
1.
However,
1（b）,
not
employs
FEM
software,
ANSYS,
takes
advantage
integrated
simulation
function.
simple
analyzed
verify
correctness
guaranteed
）
.
There
（σ
method.
then
complex
system
random
parameters.
II.
STRENGTH-STRESS
INTERFERENCE
THEORY
interference
zone
shown
shaded
area
zoomed
2.
Thus
less
than
derivation,
calculated
integration
distribution.
978-1-61284-666-8/11$26.002011
IEEE
1147
R
=
P（σ<
s）
=
σ
+g（
s）ds
dσ
−∞
+
III.
METHOD
DESCRIPTION
package
ANSYS
PDS
）g（s）
（a）
（Probabilistic
Design
Simulation）
module
[2].
defines
dimension,
properties
other
variables
distributions.
output
displacement
results
variables.
Samples
collected
based
Monte
distribution
（b）
g（s
output
variables.
With
PDS
module,
suitable
analysis
file
should
firstly
generated
define
process
If
necessary,
correlation
between
defined.
then
employed
collect
Since
each
run
independent,
Stress
distribution
g（s）f
2
Stress-strength
interference
zone
To
create
define
parameters
distributions
correlations
input
variables
probabilistic
simulation
Response
surface
Results
review
3.
Flow
chart
parallel
save
time.
Generally
50~200
insure
correctness
results.
some
cases,
variable
smooth
limited
Basically
flowchart
ANSYS
3,
dashed
steps
indicate
they
optional.
IV.
VERIFICATIONS
AND
APPLICATIONS
verified
tension
rod
reference
[3].
The
response
can
approximately
evaluated
tension
P
~
N（29400,441）N
By
assessing
coefficients
function
sampling
points
runs,
response
S
N（1054.48,41.36）
N
mm2
radius
rod
numerous
runs
conducted
r
N（3.195,0.016）mm
better
calculation
0.999.
1148
pressure
vessel.
shape
parameter
distributions
6.
Bar
element
P
4.
model
（a）
strength-stress
difference,
where
KPa
unit.
Themethodisthenappliedto
analyzethe
（b）
Surface
5.
Histogram
difference
modeled
bar
element
fixed
end.
at
axial
calculated
force,
PDS.
difference
between
stress.
value
less
0
failure.
One
hundred
show
follows
normal
137.73N
variance
45.42N.
Later
on,
previous
10000
conducted.
obtained
137.65N
44.52N,
From
concluded
0.9988
0.9990
two
result
matches
well
demonstrates
4
model,
5
histogram
r
L
Fixed
U[1990,2010]mm
L
U[9990,10010]mm
Thickness~
U[7.5,8.5]mm
Pressure~
U[2800,3200]KPa
Young'
sModule~
N（200,10）GPa
YieldStres
N（1000,50）MPa
6
Pressure
Vessel
accordance
6.
50
sampling
points,
shows
approximately
described
（229.22,67.251）MPa
further
surface
match
N
（228.86,65.823）MPa
7.
From
these
distributions,
1149
vessel
0.99967
0.99974,
respectively.
Strength-stress
deviation
vessel
V.
CONCLUSIONS
integrated
mechanical
Although
this
considers
static
cases,
dynamic
cases
file.
article
prevents
calculation.
time
money.
complement
REFERENCES
[1]
Maintainability
terms,
GB/T
3187-94
[2]
Help,
Corp
[3]
Liu
P.,
Basic
Engineering,
Metrology
Press,
2002
中文翻译：
机械可靠性仿真
上海工业自动化仪表研究所
上海，中华人民共和国
摘要：
本文提供了一种多输入随机参数模拟复杂机械系统的可靠性计算方法。
该方法基于可靠性理论，采用商业有限元软件
ANSYS。
机械系统的可靠性定量
是通过蒙特卡罗模拟。
该方法是通过理论分析验证和实际应用的结构。
该方法
避免了繁琐的数学计算和比实验方法更经济。
它可以作为传统实验方法的一种
补充。
关键词：
机械可靠性；
有限元；
蒙特卡罗仿真方法。
一、引言
根据国家标准
GB／T
3187-1994
[
]，可靠性被定义为“在一定的条件
下在指定的一段时间内，一个产品很好地履行其预期的功能的概率”。
可靠性
一直被看作是一个产品的最重要的参数。
例如，据说在美国，可靠性将是今后
产品竞争的焦点。
大多数日本企业以更可靠的产品作为公司发展的主要目标。
因此，为了准确地预测产品的可靠性，并优化基于可靠性要求的设计是非常有
意义的。
一般的机械系统是由许多部分。
一个系统的可靠性取决于许多参数，如组
件的数量，质量的部件和这些部件组装。
这也与材料的性质，相关的制造技术，
尺寸公差等有关。
大多数现有的文献主要集中在可靠性理论推导计算，然而，
由于它的复杂性，需要繁琐的数学计算。
还有一种实验法来获得系统的可靠性
试验，需要足够的样本和测试时间，因此，实验方法只能适用于那些最重要的
系统。
本文采用机械强度可靠性为例，说明了有限元法可以应用于可靠性仿真。
该方法采用商业有限元软件，ANSYS，并利用
模拟功能集成。
一个
简单的实例分析验证了该方法的正确性。
这种验证的方法随后便应用到实际结
构，来获得多输入随机参数的一个复杂系统的可靠性定量。
二、拉压干涉理论。
通常情况下，结构的最大应力和材料强度的分布有一定的概率密度。
图
1
显示的分布的一个例子，在
）和
s）分别代表最大应力和材料强度的情
况下。
1（a）中因为
s）总是大于
），结构的可靠度是
1。
然而，在
1（b），它是不能保证
s）大于
）。
图中分布着一个干扰区，如
图所示，在阴影区域和放大的图
2。
因此，结构的可靠度小于
基于数学推导
的可靠性，可以按照分布做一体化计算。
=−∞+f
）σ
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