www.researchgate.net Open in urlscan Pro
2606:4700::6811:2069  Public Scan

Form analysis
3 forms found in the DOM

GET search

<form method="GET" action="search" class="lite-page__header-search-input-wrapper"><input type="hidden" name="context" readonly="" value="publicSearchHeader"><input placeholder="Search for publications, researchers, or questions" name="q"
    autocomplete="off" class="lite-page__header-search-input"><button
    class="nova-legacy-c-button nova-legacy-c-button--align-center nova-legacy-c-button--radius-m nova-legacy-c-button--size-m nova-legacy-c-button--color-green nova-legacy-c-button--theme-bare nova-legacy-c-button--width-square lite-page__header-search-button"
    type="submit"><span class="nova-legacy-c-button__label"><svg aria-hidden="true" class="nova-legacy-e-icon nova-legacy-e-icon--size-s nova-legacy-e-icon--theme-bare nova-legacy-e-icon--color-inherit nova-legacy-e-icon--luminosity-medium">
        <use xlink:href="/m/4210735495654424/images/icons/nova/icon-stack-s.svg#magnifier-s"></use>
      </svg></span></button></form>

Name: loginForm — POST https://www.researchgate.net/login?_sg=W9VSyhMzHCMAZmkDWNEz_K7K-w6Y7OyA1bwKc1RPDaZOzGErrFyaaN8VyyaekeDVZwgTFtaEaRjvWw

<form method="post" action="https://www.researchgate.net/login?_sg=W9VSyhMzHCMAZmkDWNEz_K7K-w6Y7OyA1bwKc1RPDaZOzGErrFyaaN8VyyaekeDVZwgTFtaEaRjvWw" name="loginForm" id="headerLoginForm"><input type="hidden" name="request_token"
    value="aad-FC12Qv2F5wZnjhPko2LpfQQicoCjAvPJZUkrYl10Ne7yqjLQsC6lFpHwqlPHLTWnEAzc5dsKUpRaUb7mcRjOtrU41iorQ88GXeN4og0+emyKSJusdMInNjRiH9efydtE8zmSfTO9EXr5KDX0XXW45SVsvKhDA4VJGTX9vIXli93dHDP9ugt8Sf5tuubfhCqs6uZaR4X12PeDiLC6CjqA8IqPilWGt7QvaE13dZ5DYzqsseGTnRTHBynyL+P/GHoDMz7tI6V/ZDd/tV8khvw="><input
    type="hidden" name="urlAfterLogin" value="publication/362667405_Design_of_FLC_with_Mamdani_Approach_for_the_Estimation_of_weld_ductility_of_MIG_welded_Al65032_Alloy"><input type="hidden" name="invalidPasswordCount" value="0"><input type="hidden"
    name="headerLogin" value="yes">
  <div class="lite-page__header-login-item"><label class="nova-legacy-e-text nova-legacy-e-text--size-m nova-legacy-e-text--family-sans-serif nova-legacy-e-text--spacing-none nova-legacy-e-text--color-inherit lite-page__header-login-label"
      for="input-header-login">Email <div class="lite-page-tooltip "><svg aria-hidden="true" class="nova-legacy-e-icon nova-legacy-e-icon--size-s nova-legacy-e-icon--theme-bare nova-legacy-e-icon--color-inherit nova-legacy-e-icon--luminosity-medium">
          <use xlink:href="/m/4210735495654424/images/icons/nova/icon-stack-s.svg#info-circle-s"></use>
        </svg>
        <div class="lite-page-tooltip__content lite-page-tooltip__content--above">
          <div class="nova-legacy-e-text nova-legacy-e-text--size-s nova-legacy-e-text--family-sans-serif nova-legacy-e-text--spacing-none nova-legacy-e-text--color-inherit"><b>Tip:</b> Most researchers use their institutional email address as their
            ResearchGate login</div>
          <div class="lite-page-tooltip__arrow lite-page-tooltip__arrow--above">
            <div class="lite-page-tooltip__arrow-tip"></div>
          </div>
        </div>
      </div></label></div><input type="email" required="" id="input-header-login" name="login" autocomplete="email" tabindex="1" placeholder=""
    class="nova-legacy-e-input__field nova-legacy-e-input__field--size-m lite-page__header-login-item nova-legacy-e-input__ambient nova-legacy-e-input__ambient--theme-default">
  <div class="lite-page__header-login-item"><label class="lite-page__header-login-label"
      for="input-header-password">Password</label><a class="nova-legacy-e-link nova-legacy-e-link--color-blue nova-legacy-e-link--theme-bare lite-page__header-login-forgot" href="application.LostPassword.html">Forgot password?</a></div><input
    type="password" required="" id="input-header-password" name="password" autocomplete="current-password" tabindex="2" placeholder=""
    class="nova-legacy-e-input__field nova-legacy-e-input__field--size-m lite-page__header-login-item nova-legacy-e-input__ambient nova-legacy-e-input__ambient--theme-default">
  <div><label class="nova-legacy-e-checkbox lite-page__header-login-checkbox"><input type="checkbox" class="nova-legacy-e-checkbox__input" aria-invalid="false" name="setLoginCookie" tabindex="3" value="yes" checked=""><span
        class="nova-legacy-e-checkbox__checkmark"></span><span class="nova-legacy-e-checkbox__label"> Keep me logged in</span></label></div>
  <div
    class="nova-legacy-l-flex__item nova-legacy-l-flex nova-legacy-l-flex--gutter-m nova-legacy-l-flex--direction-column@s-up nova-legacy-l-flex--align-items-stretch@s-up nova-legacy-l-flex--justify-content-center@s-up nova-legacy-l-flex--wrap-nowrap@s-up">
    <div class="nova-legacy-l-flex__item"><button
        class="nova-legacy-c-button nova-legacy-c-button--align-center nova-legacy-c-button--radius-m nova-legacy-c-button--size-m nova-legacy-c-button--color-blue nova-legacy-c-button--theme-solid nova-legacy-c-button--width-full" type="submit"
        tabindex="4"><span class="nova-legacy-c-button__label">Log in</span></button></div>
    <div class="nova-legacy-l-flex__item nova-legacy-l-flex__item--align-self-center@s-up">
      <div class="nova-legacy-e-text nova-legacy-e-text--size-s nova-legacy-e-text--family-sans-serif nova-legacy-e-text--spacing-none nova-legacy-e-text--color-inherit">or</div>
    </div>
    <div class="nova-legacy-l-flex__item">
      <div
        class="nova-legacy-l-flex__item nova-legacy-l-flex nova-legacy-l-flex--gutter-m nova-legacy-l-flex--direction-column@s-up nova-legacy-l-flex--align-items-center@s-up nova-legacy-l-flex--justify-content-flex-start@s-up nova-legacy-l-flex--wrap-nowrap@s-up">
        <div class="nova-legacy-l-flex__item">
          <a href="connector/google"><div style="display:inline-block;width:247px;height:40px;text-align:left;border-radius:2px;white-space:nowrap;color:#444;background:#4285F4"><span style="margin:1px 0 0 1px;display:inline-block;vertical-align:middle;width:38px;height:38px;background:url('images/socialNetworks/logos-official-2019-05/google-logo.svg') transparent 50% no-repeat"></span><span style="color:#FFF;display:inline-block;vertical-align:middle;padding-left:15px;padding-right:42px;font-size:16px;font-family:Roboto, sans-serif">Continue with Google</span></div></a>
        </div>
      </div>
    </div>
  </div>
</form>

Name: loginForm — POST https://www.researchgate.net/login?_sg=W9VSyhMzHCMAZmkDWNEz_K7K-w6Y7OyA1bwKc1RPDaZOzGErrFyaaN8VyyaekeDVZwgTFtaEaRjvWw

<form method="post" action="https://www.researchgate.net/login?_sg=W9VSyhMzHCMAZmkDWNEz_K7K-w6Y7OyA1bwKc1RPDaZOzGErrFyaaN8VyyaekeDVZwgTFtaEaRjvWw" name="loginForm" id="modalLoginForm"><input type="hidden" name="request_token"
    value="aad-FC12Qv2F5wZnjhPko2LpfQQicoCjAvPJZUkrYl10Ne7yqjLQsC6lFpHwqlPHLTWnEAzc5dsKUpRaUb7mcRjOtrU41iorQ88GXeN4og0+emyKSJusdMInNjRiH9efydtE8zmSfTO9EXr5KDX0XXW45SVsvKhDA4VJGTX9vIXli93dHDP9ugt8Sf5tuubfhCqs6uZaR4X12PeDiLC6CjqA8IqPilWGt7QvaE13dZ5DYzqsseGTnRTHBynyL+P/GHoDMz7tI6V/ZDd/tV8khvw="><input
    type="hidden" name="urlAfterLogin" value="publication/362667405_Design_of_FLC_with_Mamdani_Approach_for_the_Estimation_of_weld_ductility_of_MIG_welded_Al65032_Alloy"><input type="hidden" name="invalidPasswordCount" value="0"><input type="hidden"
    name="modalLogin" value="yes">
  <div class="nova-legacy-l-form-group nova-legacy-l-form-group--layout-stack nova-legacy-l-form-group--gutter-s">
    <div class="nova-legacy-l-form-group__item nova-legacy-l-form-group__item--width-auto@m-up"><label
        class="nova-legacy-e-text nova-legacy-e-text--size-m nova-legacy-e-text--family-sans-serif nova-legacy-e-text--spacing-xxs nova-legacy-e-text--color-inherit nova-legacy-e-label" for="input-modal-login-label"><span
          class="nova-legacy-e-label__text">Email <div class="lite-page-tooltip "><span class="nova-legacy-e-text nova-legacy-e-text--size-m nova-legacy-e-text--family-sans-serif nova-legacy-e-text--spacing-none nova-legacy-e-text--color-grey-500">·
              Hint</span>
            <div class="lite-page-tooltip__content lite-page-tooltip__content--above">
              <div class="nova-legacy-e-text nova-legacy-e-text--size-s nova-legacy-e-text--family-sans-serif nova-legacy-e-text--spacing-none nova-legacy-e-text--color-inherit"><b>Tip:</b> Most researchers use their institutional email address as
                their ResearchGate login</div>
              <div class="lite-page-tooltip__arrow lite-page-tooltip__arrow--above">
                <div class="lite-page-tooltip__arrow-tip"></div>
              </div>
            </div>
          </div></span></label><input type="email" required="" id="input-modal-login" name="login" autocomplete="email" tabindex="1" placeholder="Enter your email"
        class="nova-legacy-e-input__field nova-legacy-e-input__field--size-m nova-legacy-e-input__ambient nova-legacy-e-input__ambient--theme-default"></div>
    <div class="nova-legacy-l-form-group__item nova-legacy-l-form-group__item--width-auto@m-up">
      <div class="lite-page-modal__forgot"><label class="nova-legacy-e-text nova-legacy-e-text--size-m nova-legacy-e-text--family-sans-serif nova-legacy-e-text--spacing-xxs nova-legacy-e-text--color-inherit nova-legacy-e-label"
          for="input-modal-password-label"><span
            class="nova-legacy-e-label__text">Password</span></label><a class="nova-legacy-e-link nova-legacy-e-link--color-blue nova-legacy-e-link--theme-bare lite-page-modal__forgot-link" href="application.LostPassword.html">Forgot password?</a>
      </div><input type="password" required="" id="input-modal-password" name="password" autocomplete="current-password" tabindex="2" placeholder=""
        class="nova-legacy-e-input__field nova-legacy-e-input__field--size-m nova-legacy-e-input__ambient nova-legacy-e-input__ambient--theme-default">
    </div>
    <div class="nova-legacy-l-form-group__item nova-legacy-l-form-group__item--width-auto@m-up">
      <div><label class="nova-legacy-e-checkbox"><input type="checkbox" class="nova-legacy-e-checkbox__input" aria-invalid="false" checked="" value="yes" name="setLoginCookie" tabindex="3"><span class="nova-legacy-e-checkbox__checkmark"></span><span
            class="nova-legacy-e-checkbox__label"> Keep me logged in</span></label></div>
    </div>
    <div class="nova-legacy-l-form-group__item nova-legacy-l-form-group__item--width-auto@m-up"><button
        class="nova-legacy-c-button nova-legacy-c-button--align-center nova-legacy-c-button--radius-m nova-legacy-c-button--size-m nova-legacy-c-button--color-blue nova-legacy-c-button--theme-solid nova-legacy-c-button--width-full" type="submit"
        tabindex="4"><span class="nova-legacy-c-button__label">Log in</span></button></div>
    <div class="nova-legacy-l-form-group__item nova-legacy-l-form-group__item--width-auto@m-up">
      <div
        class="nova-legacy-l-flex__item nova-legacy-l-flex nova-legacy-l-flex--gutter-m nova-legacy-l-flex--direction-column@s-up nova-legacy-l-flex--align-items-center@s-up nova-legacy-l-flex--justify-content-flex-start@s-up nova-legacy-l-flex--wrap-nowrap@s-up">
        <div class="nova-legacy-l-flex__item">
          <div class="nova-legacy-e-text nova-legacy-e-text--size-s nova-legacy-e-text--family-sans-serif nova-legacy-e-text--spacing-none nova-legacy-e-text--color-inherit">or</div>
        </div>
        <div class="nova-legacy-l-flex__item">
          <div
            class="nova-legacy-l-flex__item nova-legacy-l-flex nova-legacy-l-flex--gutter-m nova-legacy-l-flex--direction-column@s-up nova-legacy-l-flex--align-items-center@s-up nova-legacy-l-flex--justify-content-flex-start@s-up nova-legacy-l-flex--wrap-nowrap@s-up">
            <div class="nova-legacy-l-flex__item">
              <a href="connector/google"><div style="display:inline-block;width:247px;height:40px;text-align:left;border-radius:2px;white-space:nowrap;color:#444;background:#4285F4"><span style="margin:1px 0 0 1px;display:inline-block;vertical-align:middle;width:38px;height:38px;background:url('images/socialNetworks/logos-official-2019-05/google-logo.svg') transparent 50% no-repeat"></span><span style="color:#FFF;display:inline-block;vertical-align:middle;padding-left:15px;padding-right:42px;font-size:16px;font-family:Roboto, sans-serif">Continue with Google</span></div></a>
            </div>
          </div>
        </div>
        <div class="nova-legacy-l-flex__item">
          <div class="nova-legacy-e-text nova-legacy-e-text--size-s nova-legacy-e-text--family-sans-serif nova-legacy-e-text--spacing-none nova-legacy-e-text--color-grey-500 lite-page-center">No account?
            <a class="nova-legacy-e-link nova-legacy-e-link--color-blue nova-legacy-e-link--theme-decorated" href="signup.SignUp.html?hdrsu=1&amp;_sg%5B0%5D=oI60zibDz4ltkeyxLMRFrzdlWuCoZm2TBa5Rxx7PgTRJihJUblU-hxQPPZeirR7dCmzPgL7Q7X_Ye9n7DTK4nUb7mdA">Sign up</a>
          </div>
        </div>
      </div>
    </div>
  </div>
</form>

Text Content

WE VALUE YOUR PRIVACY

We and our partners store and/or access information on a device, such as cookies
and process personal data, such as unique identifiers and standard information
sent by a device for personalised ads and content, ad and content measurement,
and audience insights, as well as to develop and improve products. With your
permission we and our partners may use precise geolocation data and
identification through device scanning. You may click to consent to our and our
partners’ processing as described above. Alternatively you may click to refuse
to consent or access more detailed information and change your preferences
before consenting.
Please note that some processing of your personal data may not require your
consent, but you have a right to object to such processing. Your preferences
will apply to a group of websites. You can change your preferences at any time
by returning to this site or visit our privacy policy.
MORE OPTIONSDISAGREEAGREE
ArticlePDF Available


DESIGN OF FLC WITH MAMDANI APPROACH FOR THE ESTIMATION OF WELD DUCTILITY OF MIG
WELDED AL65032 ALLOY

 * August 2022

Authors:
Ankamma Kandula
 * Mahatma Gandhi Institute of Technology



Download full-text PDFRead full-text
Download full-text PDF
Read full-text
Download citation
Copy link Link copied
Read full-text
Download citation
Copy link Link copied
References (7)




Discover the world's research

 * 20+ million members
 * 135+ million publications
 * 700k+ research projects

Join for free


Public Full-text 1



Content uploaded by Ankamma Kandula
Author content

All content in this area was uploaded by Ankamma Kandula on Aug 13, 2022
Content may be subject to copyright.
Copyrights @Kalahari Journals Vol.7 No.6 (June, 2022)
International Journal of Mechanical Engineering

ISSN: 0974-5823 Vol. 7 No. 6 June, 2022
International Journal of Mechanical Engineering
Design of FLC with Mamdani Approach for the
Estimation of weld ductility of MIG welded Al-
65032 Alloy
P.V.R.Ravindra Reddy
Department of Mechanical Engineering, Chaitanya Bharathi Institute of
Technology, Gandipet, Hyderabad-75
K.Ankamma*
Department of Mechanical Engineering,Mahatma Gandhi Institute of Technology,
Gandipet, Hyderabad-75
*: corresponding author

Abstract: The fuzzy logic controller (FLC) is particularly suited to situations
when there is a high level of
uncertainty in the process. Welding parameters such as shielding gas pressure,
current, torch angle, electrode
size, arc length, electrode wire feed, and others affect the material properties
of a weldment in Metal Inert Gas
Welding (MIG). Joint characteristics such as groove angle, land, root gap, and
preheating temperature all have
an impact. However, a variety of noise characteristics, such as variations in
base material properties, inert gas
quality, ambient conditions, worker skill, and so on, add uncertainty into the
process. An FLC is designed and
validated to deal with such uncertainty. The effect of four input parameters,
namely inert gas pressure, current,
groove angle of the joint, and preheating temperature of base metal, on the
percentage of elongation, which is
a measure of ductility, is investigated in the current work. Each parameter is
described using three language
phrases. An L-9 orthogonal array is chosen for experimentation to reduce the
number of experiments in data
base architecture. MIG welding is used, and a data base containing nine rules is
created. The FLC is designed
in MATLAB and analytically validated. The Fuzzy controller is built using the
Mamdani technique.
Key words: Crisp value, Fuzzy logic controller, GMAW, Mamdani approach,
Membership function,
Orthogonal array, Triangular function.
Introduction
A fuzzy logic controller is defined as a set of rules of the kind IF (condition)
THEN (action) that are used to
convert a human expert's linguistic control strategy into a well-adapted
automatic control strategy [1]. Fuzzy
logic controllers have a wide range of applications in engineering [2-6].
Al-65032 is a precipitation-hardening
aluminium alloy that is one of the most widely used for general-purpose
applications. Aluminium alloys are
difficult to weld materials. Gas Metal Arc Welding is extensively used for
welding aluminium alloys. MIG
welding process is influenced by number of parameters individually and
combinedly with a high complexity
of interactions. The complex interaction of the parameters results into a wide
variation in the weldment
properties, geometry, and metallurgical features.
Input Parameter selection
The input variable selected is pressure current groove angle and preheating.
Three linguistic terms for the FLC
design, are selected for each parameter; Low, Medium and High. For 4 parameters
with 3 linguistic terms, the
size of the rule base is 43. i.e 64. So, a minimum of 64 experiments are to be
conducted for developing the rule
base which involves a huge cost and time. So for reducing the no. of experiments
an orthogonal array L-9 is
selected for experimentation. Experiments conducted with the Taguchi Orthogonal
arrays will give the
reasonably accurate results even in partial factorial case. The hypothesis was
validated by Ankamma et.al [7].
In the current work the FLC is designed using MATLAB and validated with the
analytical results.

210



Table 1: The input variables

S.No
Input Parameter
Level 1
Level 2
Level 3
1
Pressure (KPa)
90
104
125
2
Current (Amps)
220
230
245
3
Groove angle (Deg)
45
60
70
4
Pre-heating (OC)
125
150
175

The three levels of the parameters selected after preliminary experiments are
given in table 1. With four
parameters and three levels Orthogonal array L9 was selected for the
experimentation and the levels of the
parameters shown in table 1 are assigned to the OA and presented in table 2.
Experimentation
Standard test pieces with dimensions 150mm X 150mm X 6mm are cut from the
Al-65032 alloy sheet are
prepared with an a saw machine. The plates are grooved to the desired angle on a
milling machine. The milled
pieces were engraved with a specific number for identification. The pieces were
pickled. Hydrochloric Acid
is used for the process. A ready to weld sample of weld specimen is presented in
Fig 1 and the test pieces are
shown in Fig 2.
Table 2: OA after assigning the values

Run
Pressure
(KPa)
Current
(Amps)
Groove angle
(Deg)
Pre-heating
(OC)
1
90
220
45
125
2
90
230
60
150
3
90
245
70
175
4
104
220
60
175
5
104
230
70
125
6
104
245
45
150
7
125
220
70
150
8
125
230
45
175
9
125
245
60
125

The tensile test was carried out. The % Elongation values which is an indication
of ductility of a material, for
various trials are presented in Table 3. For all the parameters output values at
the levels 1,2,3 are summed up
and averaged. The averaged values are presented in the table 3 against A1, A2
and A3 and the values are
plotted in Fig 3 to know the variation.

Type your text
211
Copyrights @Kalahari Journals Vol.7 No.6 (June, 2022)
International Journal of Mechanical Engineering
Fig 1 A sample of specimen before welding Fig 2: Tensile Test pieces


Copyrights @Kalahari Journals Vol.7 No.6 (June, 2022)
International Journal of Mechanical Engineering




Table 3: % Elongation values for various trials

Run
Pressure
Current
Angle
Pre- heating
% EL
1
1
1
1
1
17.23
2
1
2
2
2
17.57
3
1
3
3
3
19.49
4
2
1
2
3
18.09
5
2
2
3
1
16.17
6
2
3
1
2
15.63
7
3
1
3
2
14.98
8
3
2
1
3
17.89
9
3
3
2
1
17.45
A1
18.10
16.77
16.92
16.95

A2
16.63
17.21
17.70
16.06

A3
16.77
17.52
16.88
18.49















Fig 3 : Variation of % Elongation at various levels
Design of Fuzzy Logic Controller
Mamdani approach is used for the design of FLC (Fuzzy logic controller). Fig 3
reveals that the variation %
elongation is almost linear with current and nonlinear with other parameters. As
the experiments are
conducted at three levels, for each input three linguistic terms are used to
denote low, medium and high. Table
4 presents the linguistic terms selected for the input parameters.
4.1 FLC Design with triangular member function
The triangular membership functions of the pressure; Current, Groove angle and
preheating are given in Fig
4, Fig 5, Fig 6and Fig 7 respectively. The triangular member ship function of
the output, percentage
elongation is presented in Fig 8.

Pressure
Levels of the parameters
% Elongation
212


Copyrights @Kalahari Journals Vol.7 No.6 (June, 2022)
International Journal of Mechanical Engineering

Table 4: input & output variables and their linguistic terms


S.No
Input variable
Low
Medium
High
1.
Pressure
LP
MP
HP
2.
Current
LC
MC
HC
3.
Groove angle
LG
MG
HG
4.
Pre-heating
LH
MH
HH
5.
% Elongation
LE
ME
HE



From the results of the experiments shown in table 3, the rule base is designed
and given in table 5. Since for
the reduction of no. of experiments, partial factorial experimentation is done a
rule base of 9 rules can only be
obtained instead of 64 rules.
Table 5: Rule Base

Run
Pressur
e
Curren
t
Angle
Pre-
heating
UTS
1
LP
LC
LG
LH
ME
2
LP
MC
MG
MH
ME
3
LP
HC
HG
HH
HE
4
MP
LC
MG
HH
HE
5
MP
MC
HG
LH
LE
213


Copyrights @Kalahari Journals Vol.7 No.6 (June, 2022)
International Journal of Mechanical Engineering

1
6
MP
HC
LG
MH
LE
7
HP
LC
HG
MH
LE
8
HP
MC
LG
HH
ME
9
HP
HC
MG
LH
ME
Further experiments are conducted for validation of the FLC. The Experimental
results are presented in table 6

Table 6: Results of further experiments

Run
Pressure
(KPa)
Current
(Amp)
Angle
(Degree)
Pre-
heating
(0C)
% Elongation
(Experimental)
1
95
220
50
130
15.98
2
100
225
55
170
17.79
3
100
230
60
140
17.00
4
120
240
50
160
15.44
5
100
220
70
140
16.02
Calculation of out using analytical formulae
A sample calculation is provided here under for the first case i.e Pressure 95
KPa, Current 200 A, groove
angle 500 and preheating 1300 C
From the Fig 9 it is noted that 95 Kpa pressure can be termed as low pressure or
medium pressure with
different membership functions. The member ship functions can be calculated by
similarity of triangles and
found out as µLp=0.714286 and µMP=0.285714.

Similarly membership functions pressure, current, groove angle and preheating
can be calculated as µLC=0.8
and µMC=0.2; µLG=0.666667 and µMG=0.333333; µLH=0.8 and µMH=0.2.

So there 16 possible rules those can be fired and are presented in table 7.
LP MP HP


µ



90 195 104 125

Fig 9: Sample calculation for Pressure
Firing strength of each rule can be found out by taking the minimum value of the
member ship of functions of
each rule. For example firing strength of rule 1 given in table 7 can be found
out as
Min (µLP, µLC, µLA, µLH) = min (0.714286, 0.8,0.666667,0.8) = 0.666667
Similarly the firing strength of each rule is found out and are given in the
table 7
µLP
µMP
214


Copyrights @Kalahari Journals Vol.7 No.6 (June, 2022)
International Journal of Mechanical Engineering


Table 7: Firing strength of the rules

Rule
Pressure
Current
Angle
Pre-heating
Firing strength
1
LP
LC
LG
LH
0.666667
2
LP
LC
LG
MH
0.2
3
LP
LC
MG
LH
0.333333
4
LP
MC
LG
LH
0.2
5
MP
LC
LG
LH
0.285714
6
LP
LC
MG
MH
0.2
7
LP
MC
MG
LH
0.2
8
MP
MC
LG
LH
0.2
9
MP
LC
MG
LH
0.2
10.
LP
MC
LG
MH
0.2
11.
MP
LC
LG
MH
0.2
12.
LP
MC
MG
MH
0.2
13.
MP
MC
MG
LH
0.2
14.
MP
LC
MG
MH
0.2
15.
MP
MC
LG
MH
0.2
16
MP
MC
MG
MH
0.2

But the database only consists of 2 rules Fuzzified outputs as evident from
table 3; Rule 1 and rule 12
calculations are done on these two rules
From Fig 3 the two rules can be stated as
Rule 1: If Pressure is LP and current is LC and Groove angle is LG and
preheating is LH then the Impact
Energy is ME
Rule 12: If Pressure is LP and current is MC and Groove angle is MG and
preheating is MH then the Impact
Energy is ME
The representation the above two rules on the triangular membership function are
graphically presented in Fig
10 and Fig 11


µ
666





14.98






17.24




19.49

µ
.2
14.98




17.24




19.49
Fig 10: Rule 1 Fig 11: Rule 12
215


Copyrights @Kalahari Journals Vol.7 No.6 (June, 2022)
International Journal of Mechanical Engineering

Centre of sums method is applied for defuzzificaiton. The hatched areas of the
membership functions and the
centres of areas shown in the Fig 10 and 11 are computed and presented in the
table 8. Areas can be easily
calculated by the geometry i.e Sum of area of a triangle and a rectangle for
each case. Length of the rectangle
and the base of the triangle can be found out by similarity of triangles. Centre
of the rectangle is at half of its
length and centre of the triangle is 1/3 of its length.
The centre of whole area is obtained by weighted average Centre of area = (area
of rectangle X centre of
rectangle+ area of the triangle and centre of the triangle)/ (area of the
rectangle + Area of the triangle)
Table 8: Area and centre of areas

Rule
Area
Centre
1
13.342
17.24
12
11.453
17.24

The fuzzified output can be calculated by the equation (1)

1
 
1
+ 
12
 
12

  =

1
+ 
12


Defuzzified output for this case is computed to be 17.24
Design of FLC using MATLAB
The FLC design is carried out using MATLAB, The input parameters and out
parameter of FLC is shown in
Fig 12. Triangular membership functions selected for input variables pressure,
current, groove angle and
preheating are presented in Fig 13, Fig 14, Fig 15 and Fig 16 respectively. The
triangular membership
function of output, percentage elongation is presented in Fig 17.

Fig 12: Inputs and Output of FLC Fig 13: Triangular Membership function of
pressure


Fig 14: Triangular Membership function of current Fig 15: Triangular Membership
Function of
Groove angle
216


Copyrights @Kalahari Journals Vol.7 No.6 (June, 2022)
International Journal of Mechanical Engineering





Fig 16: Triangular Membership function of Preheating Fig 17: Triangular
Membership function
of % Elongation
The rules stated in table 5 are input into the MATLAB and are graphically
presented in Fig 18.
Fig 18: Graphical representation of rules

Validation of FLC
The FLC designed using MATLAB is validated with analytical result obtained in
section 4.2. For the same
case i.e for the run 1 in table 5 the result obtained MATLAB is presented in Fig
19.


Fig 19: Result obtained with MATLAB for run-1

217


Copyrights @Kalahari Journals Vol.7 No.6 (June, 2022)
International Journal of Mechanical Engineering

The result obtained from the analytical calculation is 17.24 and the result
obtained from the MATLAB for the
same case is 17.2. Hence it is treated that the FLC design with MATLAB is
validated and further readings can
be from the MATLAB FLC to compare with the experimental results presented in
table 6. The results
obtained from the FLC for the runs 2,3,4 and 5 presented in table 6 are shown in
Fig 20, Fig 21, Fig 22 and
Fig 23 respectively and the values are tabulate in table 9 against the
experimental values. From the table 9 it is
noted that the percentage error between FLC and experimental results vary from
0.06% to 9.47%. The error
may be accepted. This error may also be due to the assumption of linearity. But
from the Fig 3 linearity was
strictly observed for current only.


Fig 20: FLC result for run 2 Fig 21: FLC result for run 3

Fig 22: FLC result for run 4 Fig 23: FLC result for run 5

Table 9: Validation of FLC with experimental results

Run
Pressure
Current
Angle
Pre-
heating
% Elongation
(Experimental)
%
Elongation
From the
FLC
% error
1
95
220
50
130
15.98
17.2
7.6
2
100
225
55
170
17.79
17.8
0.06
3
100
230
60
140
17.00
17.2
1.18
4
120
240
50
160
15.45
17.1
9.47
5
100
220
70
140
16.03
15.9
-0.93
The area plots of percentage elongation with current and pressure, with groove
angle and current, with
preheating and current are presented in Fig 24, Fig 25 and Fig 26 respectively.
218


Copyrights @Kalahari Journals Vol.7 No.6 (June, 2022)
International Journal of Mechanical Engineering












Conclusions

In the current work a Fuzzy logic controller is developed with the help MATLAB
for predicting the
percentage elongation of the aluminium alloy AL 65032 weldment, using Mamdani
approach. The FLC
developed using the tool MATLAB is validated analytically and experimentally and
the validation result is
found satisfactory. As design FLC becomes complex with the increase of number of
input parameters, the
concept of orthogonal array used for experimentation in the development of data
base and rule base. Even
though a partial data base is developed with the reduced experimentation to save
the time, cost and effort, the
maximum error in the prediction is found out to be 7.86%. So development of
knowledge base using Taguchi
technique proved to be accurate enough to design a low cost FLC. Further
investigations may be carried out to
tune this controller using neural networks or genetic algorithms as the data is
getting generated in due course.
This off line FLC can be integrated in intelligent manufacturing systems for
controlling the process in auto
mode and at the same time tuning the FLC continuously to produce the synergic
effect.

References
[1]. Kheireddine Lamamra, Farida Batat, Fouad Mokhtari, “New technique with
improved control quality of nonlinear systems using an
optimized fuzzy logic controller” Expert Systems with Applications, vol.145
(2020) pp.1-9
[2]. Stefano Pietrosanti, Feras Alasali, Willam Holerbaum, “Power Management
system for RTG crane using fuzzy logic controller”,
Sustainable Energy Technologies and Assessments, vol.37, Feb 2020.pp 1-15.
[3]. Tianhu Zhang, Yuanjun Liu, Yandi Rao, Xiaopeng Li, Qingxin Zhao, “Optimal
design of building environment with hybrid genetic
algorithm, artificial neural network, multivariate regression analysis and fuzzy
logic controller” Building and Environment Vol. 175 (2020),
pp.1-10
[4]. A.K.D. Velayudhan, “Design of a supervisory fuzzy logic controller for
monitoring the inflow and purging of gas through lift bags for a
safe and viable salvaging operation”, Ocean Engineering, vol.171 (2019),
pp.193-200.
[5]. Najib El Ouanjli, Saad Motahhir, Aziz Derouich, Abdelaziz El Ghzizal, Ali
Chebabhi, Mohammed Taoussi “Improved DTC strategy of
doubly fed induction motor using fuzzy logic controller” Energy Reports 5 (2019)
pp.271–279
[6]. Jorge Martinez-Gil, Jose Manuel Chaves-Gonzalez, “Automatic design of
semantic similarity controllers based on fuzzy logics”, Expert
Systems with Applications 131 (2019) pp.45–59.
[7]. K. Ankamma, P.V.R. Ravindra Reddy, “Use of Orthogonal Arrays in Design of a
Fuzzy Logic Controller to Predict the Proof Stress for the
TIG Welded Al-65032”, International Journal of Innovative Technology and
Exploring Engineering (IJITEE) Volume-9 Issue-7, May 2020,
pp.996-1001


Fig 24. Area plot of % El
with pressure and current
Fig 25. Area plot of % El
with Groove angle and
current

Fig 26. Area plot of % El with
preheating and current


219




CITATIONS (0)


REFERENCES (7)




ResearchGate has not been able to resolve any citations for this publication.
Power management system for RTG crane using fuzzy logic controller
Article
Full-text available
 * Jan 2020

 * Stefano Pietrosanti
 * Feras Alasali
 * William Holderbaum

In this research, there are two major objectives have been investigated for a
Rubber Tyred Gantry (RTG) crane system: energy consumption reduction and
decrease the stress on the primary source. These objectives can be met by using
an advance control system that reads the status of the crane and outputs a power
reference value which is fed to the storage device. This paper presents Fuzzy
Logic Controller (FLC) approach to maximise the potential benefits of adding
energy storage units to RTG cranes. In this work, FLC is described and
simulated, with the results analysed to highlight the behaviour of the storage
in association with the specific control system. An actual collected data at the
Port of Felixstowe, UK has been used to develop the crane and ESS models and
test the proposed control strategies in this paper. Furthermore, a comparison
analysis between the FLC and the standard control system (PI) for RTG crane and
ESS applications will be presented with respect to energy consumption, fuel
saving and the control impact on the energy device. The simulation results of
the FLC control strategy for the collected data shows that it successfully
increases the energy savings by 32% and outperforms the PI controller by 26%.
View
Show abstract
Improved DTC strategy of doubly fed induction motor using fuzzy logic controller
Article
Full-text available
 * Feb 2019

 * Najib El Ouanjli
 * Saad Motahhir
 * Aziz Derouich
 * Mohammed Taoussi

This paper presents an improved Direct Torque Control (DTC) strategy for a
Doubly Fed Induction Machine (DFIM) powered by two voltage source inverters
(VSI) at two levels. This strategy is based on the fuzzy logic controller. The
main objective is to improve the performance of the system by reducing
electromagnetic torque ripples and improving the currents shape by optimization
of the total harmonic distortion (THD). The hysteresis regulators and voltage
vectors selection table of the conventional DTC are replaced by fuzzy logic
blocks to realize fuzzy DTC control. The two control strategies are simulated in
the MATLAB/SIMULINK environment followed by a comparative analysis to validate
the effectiveness of the proposed strategy. Many improvements in term of rise
time, torque ripples, flux ripples and current harmonics have been done, namely
stator and rotor flux ripple and torque ripple have been reduced more than 50%,
69.2% and 47.7% respectively. The stator and rotor currents THD have been
reduced around 84.5% and 84.3% respectively.
View
Show abstract
Optimal design of building environment with hybrid genetic algorithm, artificial
neural network, multivariate regression analysis and fuzzy logic controller
Article
 * Mar 2020
 * BUILD ENVIRON

 * Zhang Tianhu
 * Yuanjun Liu
 * Yandi Rao
 * Qingxin Zhao

Computational cost poses a major obstacle to the design of indoor environments
with the current optimal method and computational fluid dynamics (CFD). A novel
optimization method integrating a genetic algorithm (GA), an artificial neural
network (ANN), multivariate regression analysis (MRA), and a fuzzy logic
controller (FLC) was proposed in this paper to optimize the indoor environment
and energy consumption based on simulation results. Thermal comfort (predicted
mean vote) was set as the restrictive design objective. Indoor air quality (air
age) and energy consumption were set as the optimal design objectives. Air
supply parameters, such as ventilation rate, inlet temperature, and angle, were
used as the design variables. The GA process was used to search for the optimal
solution (individual), while the ANN and CFD tool were used to obtain the values
of the objectives for each individual. MRA was used to reduce the variable
space, and FLC was used to control the execution routine of the CFD process to
reduce the computational cost. The results indicated that the ventilation rate
has a lower impact on the design result compared with the other two design
variables. When the MRA and FLC were included in the design process, the
variable space and computational cost were reduced by 50% and 35.7%,
respectively. The design efficiency was improved while the best found solution
was maintained.
View
Show abstract
A new technique with improved control quality of nonlinear systems using an
optimized fuzzy logic controller
Article
 * Dec 2019
 * EXPERT SYST APPL

 * Kheireddine Lamamra
 * Farida Batat
 * Fouad Mokhtari

Fuzzy logic controllers are increasingly applied to control complex systems
because they have several advantages. The objective of this work is to propose a
new technique to optimize a Takagi-Sugeno fuzzy logic controller with quality
using the Non-dominated Sorting Genetic Algorithm-II by optimizing three
objectives functions which are a cost function, the number of fuzzy inference
rules, and the maximum instantaneous quadratic error. In this technique, the
Multi-Criteria Decision-Making approach is used to choose one of the best
controllers from the Pareto set of the last generation of the genetic algorithm.
The proposed technique ensure: (i) that the output of the controlled system
correctly follows the desired reference. (ii) the acceleration of the control
process and (iii) avoid the existence of large overshoots; which are usually
observed when applying commands to complex processes with variable behavior. At
the end of the control process, a robustness test is performed to verify the
efficiency of the proposed technique. It is shown here that the optimization of
the third objective function, allows the improvement of the control quality.
This new technique can be used to improve expert and intelligent systems based
on fuzzy rules to control high complex systems with variable behavior which they
have disturbing overshoots during their control. This technique; allows to
accelerate the calculation of the control law of expert systems based on fuzzy
rules; while ensuring that the quality of the control and the output signal are
good.
View
Show abstract
Design of a supervisory fuzzy logic controller for monitoring the inflow and
purging of gas through lift bags for a safe and viable salvaging operation
Article
 * Jan 2019
 * OCEAN ENG

 * Arun Kumar Devaki Bhavan Velayudhan

This paper presents a mathematical model and numerical time-domain approach to
simulate the dynamics of a sunken ship/vessel being raised from seafloor by
buoyancy (gas-inflating) systems in a form which is suitable for integrating
control techniques to ensure hydrodynamic stability for a safe and viable
salvaging operation. According to the two-degree-of-freedom equations of
rigid-body vessel motion in diving plane, a conventional sliding mode controller
is designed as the primary controller to regulate flow rate of filling gas
inside the lift bags and a PID controller is designed as the secondary
controller for regulating the purging of gas through the valves fitted on lift
bags. Then a supervisory fuzzy logic controller is designed to monitor or switch
between the primary and secondary controllers based on the buoyancy requirement.
From the simulation studies, it is found that the supervisory fuzzy logic
controller is capable to maintain hydrodynamic stability by suitably defining
the linguistic fuzzy rules, which is created based on the author's experience in
conducting numerical simulation using primary and secondary controllers.
View
Show abstract
Automatic Design of Semantic Similarity Controllers based on Fuzzy Logics
Article
 * Apr 2019
 * EXPERT SYST APPL

 * Jorge Martinez-Gil
 * Jose M. Chaves-González

Recent advances in machine learning have been able to make improvements over the
state-of-the-art regarding semantic similarity measurement techniques. In fact,
we have all seen how classical techniques have given way to promising neural
techniques. Nonetheless, these new techniques have a weak point: they are hardly
interpretable. For this reason, we have oriented our research towards the design
of strategies being able to be accurate enough but without sacrificing their
interpretability. As a result, we have obtained a strategy for the automatic
design of semantic similarity controllers based on fuzzy logics, which are
automatically identified using genetic algorithms (GAs). After an exhaustive
evaluation using a number of well-known benchmark datasets, we can conclude that
our strategy fulfills both expectations: it is able of achieving reasonably good
results, and at the same time, it can offer high degrees of interpretability.
View
Show abstract
Use of Orthogonal Arrays in Design of a Fuzzy Logic Controller to Predict the
Proof Stress for the TIG Welded Al-65032
 * May 2020
 * 996-1001

 * K Ankamma
 * P V R Reddy

K. Ankamma, P.V.R. Ravindra Reddy, "Use of Orthogonal Arrays in Design of a
Fuzzy Logic Controller to Predict the Proof Stress for the TIG Welded Al-65032",
International Journal of Innovative Technology and Exploring Engineering
(IJITEE) Volume-9 Issue-7, May 2020, pp.996-1001




RECOMMENDED PUBLICATIONS

Discover more
Conference Paper
Full-text available


INFLUENCE OF WELD PARAMETERS AND FILLER-WIRE ON FATIGUE BEHAVIOR OF MIG-WELDED
AL-5083 ALLOY

October 2018
 * Vidit Gaur
 * Manabu Enoki
 * Toshiya Okada
 * [...]
 * Syohei Yomogida

MIG welded Al-5083 alloy was investigated to study the effect of weld groove
angle (70° and 90°), welding passes and filler-wire material (Al-5183 and
Al-5.8%Mg) on its fatigue properties under mean stress. No significant
difference of these parameters could be observed on the fatigue lives but the
fatigue lives as well as the endurance limit get reduced when R-ratio was
increased. A ... [Show full abstract] non-monotonic variation in slopes of S-N
curves was observed: first increased and then decreased. Walker’s model, a
popular fatigue model, was tested using the experimental data but it did not
give best results. Thus, a new model was proposed with best ability to capture
mean stress effect and predict fatigue lives. Most of the fatigue failures at
low R-ratios (<0.1) occurred from surface while for higher R-ratios (>0.1),
defect-induced (gas/shrinkage porosity) failures were dominant. The possible
reasons for this change in damage mechanism are discussed.
View full-text
Article
Full-text available


INFLUENCE THE FILLER METAL CONTAINING ZIRCONIUM ON THE WELD METAL POROSITY OF
THE MIG WELDED ALUMINU...

October 2020 · IOP Conference Series Materials Science and Engineering
 * Wahidun Adam
 * Winarto Winarto
 * [...]
 * Sunoto Mudiantoro

The aluminum alloy series 5083 has high corrosion resistance and high specific
strength properties; hence, it is widely used for ship structures, marine
components, cryogenics of LNG tanks, and unfired pressure vessels. This study
was undertaken to investigate the effect of zirconium on weld metal porosity and
hardness of MIG welded aluminum Alloy 5083. Zirconium is one of the alloying
elements ... [Show full abstract] that are mostly used for grain refining in the
aluminum casting process. Zirconium element was previously added in the
electrode wire as a filler metal. The experiments were carried out by MIG
welding for several aluminum alloy plates to produce the full penetration
weldments. Two different filler wires used in the welding: one is containing
zirconium, and another is no containing zirconium. The grain size of weld
microstructures was measured using a Linear Intercept Method (ASTM E 112), and
the hardness measurement is conducted using micro-Vickers along the cross
section of AA-5083 weldment. Results showed that the grain size of AA-5083
weldment using the filler metal containing zirconium was considerably finer size
than that of using the filler metal without zirconium. Moreover, the hardness of
AA-5083 weldment using the filler metal containing zirconium is generally higher
than that of using the filler metal without zirconium.
View full-text
Article
Full-text available


CHARACTERISATION OF MICROSTRUCTURE, MECHANICAL AND CORROSION PROPERTIES OF
PULSED MIG WELDED MODIFIE...

February 2018 · IOP Conference Series Materials Science and Engineering
 * P. Sundararaj
 * [...]
 * Muthukumar M.

Varying the shielding gas composition with argon and carbon dioxide influences
the properties of the weldments which are evaluated using microstructural,
micro-hardness and corrosion studies. The modified P91 steel samples are welded
by Pulsed Metal Inert Gas welding process with different shielding gas mixture,
i.e., 95%Ar-5%CO2, 80%Ar-20%CO2 and 60%Ar- 40%CO2. The welded steels are studied
... [Show full abstract] metallographically by observing microstructures at
three different regions namely at the base metal, Heat Affected Zone (HAZ) and
the Weld zone. Hardness measurements are also done using Vicker's micro-hardness
tester. Corrosion studies in acidic media (sulphuric and nitric acid media of
four different normalities 0.5, 1.0,1.5 and 2.0) are done in the welded region
and the parent metal to compare the corrosion resistance. Of all the welded
samples, welds made with the shielding gas composition of 95%Ar-5%CO2 exhibits
good corrosion resistance over the other two welds while the weld made with the
shielding gas composition of 60%Ar -40%, shows very poor corrosion resistance.
View full-text
Article
Full-text available


A CHARACTERIZATION OF MICROSTRUCTURE AND MECHANICAL PROPERTIES OF A6N01S-T5
ALUMINUM ALLOY HYBRID FI...

September 2016 · The International Journal of Advanced Manufacturing Technology
 * Qiuying Wang
 * Hui Chen
 * Zongtao Zhu
 * [...]
 * Yunlong Cui

A6N01S-T5 aluminum alloy is used widely on components of high-speed train body,
such as the sidewall and underframe. Welded joints are often the positions where
fracture and failure occur. In this paper, fiber laser-metal inert gas (MIG)
welding was used as a new welding method to weld A6N01S-T5 alloy and compared
with the traditional MIG welding joints. Optical microscopy, scanning and ...
[Show full abstract] transmission electron microscopy, hardness tester, and
electronic universal testing were used for the microstructure observation and
mechanical property examination. Softening effect in the heat affected zone
(HAZ) and weld fusion zone is observed. The precipitations and dissolution of β′
phases are proved to be the reason of softening. The tensile strength of the
fiber laser-MIG welded joint was approximately 83 % of base metal, which is 33
MPa higher than that of the MIG welded joint. The fracture position of the
joints is in accordance with the lowest hardness position.
View full-text
Last Updated: 13 Aug 2022

Discover the world's research
Join ResearchGate to find the people and research you need to help your work.
Join for free

ResearchGate iOS App
Get it from the App Store now.
Install
Keep up with your stats and more
Access scientific knowledge from anywhere

or
Discover by subject area
 * Recruit researchers
 * Join for free
 * Login
   Email
   Tip: Most researchers use their institutional email address as their
   ResearchGate login
   
   PasswordForgot password?
   Keep me logged in
   Log in
   or
   Continue with Google
   
   Welcome back! Please log in.
   Email
   · Hint
   Tip: Most researchers use their institutional email address as their
   ResearchGate login
   
   PasswordForgot password?
   Keep me logged in
   Log in
   or
   Continue with Google
   No account? Sign up
   

Company
About us
News
Careers
Support
Help Center
Business solutions
Advertising
Recruiting

© 2008-2022 ResearchGate GmbH. All rights reserved.
 * Terms
 * Privacy
 * Copyright
 * Imprint