MODELING AND OPTIMIZATION OF A MOBILE CROP RESIDUE DISINTEGRATOR

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International Journal of Mechanical Engineering and Technology (IJMET)
Volume 7, Issue 3, May–June 2016, pp.320–328, Article ID: IJMET_07_03_029
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ISSN Print: 0976-6340 and ISSN Online: 0976-6359
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MODELING AND OPTIMIZATION OF A
MOBILE CROP RESIDUE DISINTEGRATOR
Nilesh Awate
Research Scholar, Department of Mechanical Engineering, G. H .Raisoni College of
Engineering, Nagpur, India.
Dr. D. J. Tidke
Ex. Professor, Department of Mechanical Engineering, V.N.I.T, Nagpur, India.
ABSTRACT
In the present work, experiments and analyses have been made to
investigate the influence of parameter on performance of a mobile crop
residue disintegrator .The experiments have been conducted as per Box-
Behnken design matrix with input parameters as impact load, blade angle and
cuttingpeed. Mathematical modeling has been done by response surface
methodology (RSM) to develop relationships between process parameters and
output response(s). The adequacy of the developed models has been tested
with analysis of variance. The contour and surface plots for a mobile crop
residue disintegrator have been made to reveal how output responses vary
with change in the parameters. The proposed optimization methodology
(RSM) seems to be useful for analyzing and optimizing any process where two
or more input parameters influence more than one important output
responses.
Key words: cutting speed, impact load, Vibration, Box-Behnken, design
Response surface, methodology
Cite this Article: Nilesh Awate and Dr. D .J. Tidke, Modeling and
Optimization of a Mobile Crop Residue Disintegrator. International Journal of
Mechanical Engineering and Technology, 7(3), 2016, pp. 320–328.
http://www.iaeme.com/currentissue.asp?JType=IJMET&VType=7&IType=3
1. INTRODUCTION
The present study of design and development of a crop residue mobile disintegrator
machine.The main objective of the research work were to develop a suitable size
tractor operated crop residue disintegrator and to study the performance of the
machine in actual field conditions with utilizing the optimized blade angle. The
present machine was designed and developed by considering the different parameters.
On the basis of theoretical considerations and assumptions made on different
parameter for selection and development of components of mobile disintegrator

Modeling and Optimization of a Mobile Crop Residue Disintegrator
machine various reviews taken in account of in the development procedure. As
accordingly the various parts are developed and selected from the market during the
development procedure. For optimization of design the cutting blade angle with
different speed and load plays a vital role. For this the analysis was made on the
combinations of blade angels, impact load and cutting speed and observed the
responses on actual force, cutting efficiency and collection efficiency by utilizing the
trial version of Design Expert 10.0.2 software for experimental design. The factorial
Box-Behnken design matrices method was selected for optimized solution of the
blade angle with 17 runs with three variables. By using this software the optimized
solution of the blade angle with an impact load at a speed was detrain. Due to this an
optimized blade was used for the final prototype development and to observe the field
performance on large field.
1.1. Literature Survey
The present work is to optimize the mobile disintegrator machine design by considering
various parameter to get optimize design of the machine by considering BBD method. Several
attempts had been made by different researchers to find out the parametric influence on the
desired quality characteristics of ground parts. Also, several works were done to develop
mathematical models of various machines etc. In the following paragraphs, a literature survey
is reported in the context of above mentioned matters. This concerns research application of
BBD method to optimize their design, optimization techniques applied in grinding, and
relevant matters.
Sergio Luis Costa Ferreira et. al (2007) the study describes fundamentals and
applications of multivariate statistical techniques for the optimization of
chromatographic systems. An optimization example involving a real separation
process is exhaustively described In most real applications these differences are
probably not decisive in determining which design to use, at least for this number of
factors. However, since Box–Behnken designs do not contain combinations where all
the factors are at their higher or lower levels, they may be useful in avoiding
experiments under extreme conditions, for which unsatisfactory results might occur.
Conversely, they are not indicated for situations in which we would like to know the
responses at the extremes, that is, at the vertices of the cube.
Box–Behnken designs for four and five factors can be arranged in orthogonal
blocks, as shown in Table 4. In this table, each (±1, ±1) combination within a row
represents a full 22 design. Dashed lines separate the different blocks. Because of the
block orthogonality, the second-order model can be augmented to include block
effects without affecting the parameter estimates, that is, the effects themselves are
orthogonal to the block effects
M Manohar et. al (2013) in this study it was reported that the use of Box Behnken
design approach to plan the experiments for turning Inconel 718 alloy with an overall
objective of optimizing the process to yield higher metal removal, better surface
quality and lower cutting forces. Response Surface methodology (RSM) has been
adopted to express the output parameters (responses) that are decided by the input
process parameters. It is said that the RSM also quantifies the relationship between
the variable input parameters and the corresponding output parameters. RSM designs
allow us to estimate interaction and even the quadratic effects, and hence, give us an
idea of the shape of the response surface we are investigating. Box-Behnken design is
having the maximum efficiency for an experiment involving three factors and three
levels; further, the number of experiments conducted for this is much lesser compared
to a central composite design. The proposed Box-Behnken design requires 15 runs of

Nilesh Awate and Dr. D. J. Tidke
experiment for data acquisition and modeling the response surface. Design expert
software was used to design the experiment and randomize the runs.
Box Behnken design was successfully adopted and the experiments were designed
choosing the input variables for the levels selected. With minimum number of
experiments, data was collected and the models were developed. Response Surface
Models evolved for responses show the effect of each input parameter and its
interaction with other parameters, depicting the trend of response...
Guowei Shu et al (2013), the most common designs, that is central composite
design (CCD) and Box-Behnken experimental design (BBD), of the principal
response surface methodology have been widely used in various experiments. Box-
Behnken design, a spherical and revolving design, has been applied in optimization of
media; the extraction of polysaccharides from Jili was optimized using Design Expert
version7.1 software. In this study the three variables involved in the optimization
were liquid: solid ratio (x1), cellulase concentration (x2) and reaction time (x3). The F
and p value indicated that the variable with the largest effect was the liquid: solid ratio
(x1). This was followed by the quadratic effect of liquid: solid ratio (x12), the
cellulase concentration (x2) and interaction effect of liquid: solid ratio and cellulase
concentration (x1x2).
Perincek Oguz and Colak Metin (2013) Reported that use of Box–Behnken a full
range of response surface methodology using Box–Behnken experimental design to
express the net harmonic current (3rd
and 5th
) as an empirical model. It was reported
that the model provided an excellent explanation of the relationship among the
number of loads and the net harmonic currents. The results of experiments confirmed
that the Box-Behnken experimental design can be used for the determination of loads
responsibility and interactions of loads (A×B, A×C, B×C, etc.) for the 3rd
and 5th
harmonic currents.
Myalowenkosi I. Sabela (2014), the study present describes an optimized Box-
Behnken design using a catalytic-differential pulse polarograhic technique for the
simultaneous determination of chromium (III) and (VI) in wastewater samples using
ammonium piperidine dithiocarbamate as a complexing agent. The optimization
strategy was carried out using a two level full factorial design. In this study a Box-
Behnken design with fifteen runs, three independent variables (pH, concentration of
NH4Cl-NH4OH and APDC) and three replicates at a centre point was used in this
study. The experiment was randomized to reduce confounding variables by equalizing
the three independent variables that have not been accounted for in the experimental
design
Rudrapati Ramesh et. al (2015) reported that the experiments and analyses have
been made to investigate the influence of machining parameter. The experiments have
been conducted as per Box- Behnken design matrix with input parameter .In this work
mathematical modeling has been done by response surface method (RSM). In this
study BBD with three factors, three level, and 15 runs are selected. In the BBD matrix
the experiment consist of a set of point lying at the midpoint of each edge and
replicated center point of multidimensional cube.

2. EXPERIMENTAL PLAN, SETUP AND PROCEDURE
This section describes the experimental design used for development of mobile
disintegrator .The effect of various parameters for development of mobile
disintegrator fail type of blade, rotating speed of cutting drum , diameter of cotton
stalk , force acting on blade , collection efficiency, and collecting efficiency. For the
disintegrator as the cutting efficiency, collection efficiency and actual force required
at variable speed are described under the experimental design along with the level and
combination of treatment.
The experimental design was applied after selection of the ranges. Seventeen
experiments were required to completely randomized design (CRD) with three
variable of two level of each variable and three replications. Actually in the present
study around 27trails was carried out on different plot size this work has been done at
varied levels of input parameters as per Box-Behnken design matrix, which is discussed
subsequently.
2.1. Design of Experiments
Design of experiments is widely used for controlling the effects of input parameters in any
machining process and other processes as well. Its usage decreases the number of
experiments, time, and material resources. Furthermore, the analysis performed on the results
of such experiments is easily realized and the experimental errors are minimized. Statistical
methods measure the effects of change in operating variables and their mutual interactions on
the process. RSM is one of the important methods in design of experiments. It is a collection
of mathematical and statistical techniques that are useful for modeling and analysis of
problems in which output or response is influenced by several variables and the goal is to find
the correlation between the response and the variables. It can be used for optimizing the re-
sponse. It is an empirical model-developing technique devoted to the evaluation of relations
existing between a group of controlled experimental factors and the observed results of one or
more selected criteria. A prior knowledge of the process is thus necessary to achieve a
realistic model [29].
In the present study, RSM's Box-Behnken experimental design with three factors, three
levels, and 17runs arc selected. The machine parameters used and their levels arc given in
Table 1, and the Box-Behnken design matrix are shown in Table 2. The experimental design
consists of a set of points lying at the midpoint of each edge and replicated center point of a
multidimensional cube. The mathematical model is then developed that illustrates the
relationship between the process variables and response by using the application of RSM.
In the RSM, the quantitative form of relationship between the output response and input
variables can be represented as follows.
Y=f (A, B, C) (1)
Where A, B, and C are input parameters and Y is the output response which is required
to be optimized. Here, it is assumed that the independent variables (input parameters) are
continuous and controllable by experiments with negligible errors.
RSM creates second order quadratic model of the form:
Y = 6o+ b, A +hB + b} C + b, A2 + bnB2 (2)
+ b»C2 + baAB + buAC + bnBC

Table 1 Input parameters and their levels
Name of the
variables
Range Code
(Xi)
Levels Intervals
Xi1 Xi2 Xi3
-1 0 +1
Blade angle
Degree
100-110 X1 100 105 110 05
Impact load (N) 10-30 X2 10 20 30 10
Rotating Speed
in RPM
1600-
2000
X3 1600 1800 2000 200
Where Y is the dependent variable; A, B, and C are the independent variables; and b are
the coefficients of linear, quadratic, and interaction of input parameters A, B, and C. The
term b0 is the intercept term; b, b2, and b3 are the liner terms; b, &22> ar
>d ^33 are the
squared terms; and bi, bit and 623 are the interaction terms between the independent/input
variables. All the b values have been determined, on the basis of least squares method [30,
31].
The actual values of variables at given coded levels are calculated as below,
Yaij=Xij x Vi Ya3 . (4.1)
Where,
I - 1 to 4 numbers of variables
J - 1 to 3 numbers of levels
- actual value of ith variable at given jth coded level
- coded value of ith variable at given jth coded level
- interval of variation for ith var
Table 1: Treatments combinations required for design of experiment of straw cutting
mechanisms in CRD design for response surface modeling BBD Matrix
Expt. No run X1 X2 X3
Blade angel,
(Degree)
impact
Load, N
Speed,
(RPM)
1 14 -1 -1 0 100 10 1800
2 13 1 -1 0 110 10 1800
3 3 -1 1 0 100 30 1800
4 2 1 1 0 110 30 1800
5 12 -1 0 -1 100 20 1600
6 8 1 0 -1 110 20 1600
7 6 -1 0 1 100 20 2000
8 16 1 0 1 110 20 2000
9 11 0 -1 -1 105 10 1600
10 4 0 1 -1 105 30 1600
11 17 0 -1 1 105 10 2000
12 5 0 1 1 105 30 2000
13 10 0 0 0 105 20 1800
14 1 0 0 0 105 20 1800
15 9 0 0 0 105 20 1800
16 15 0 0 0 105 20 1800
17 7 0 0 0 105 20 1800

The experiments of cutting blade were conducted according to CRD design (Table
4.6) and Response surface modeling (RSM) was applied to the experimental data
using commercial statistical package, Design expert-version 10.0.2 (Statease Inc.,
Minneapolis, USA). The relative effect of the variables (Blade angle, Impact Load,
drum speed) on the responses was studied and the mobile disintegrator parameters
were optimized in order to get best straw cutting mechanism. The responses studied
were actual impact load, cutting efficiency, collection efficiency.
The following second order polynomial response surface model (Eq. 4.2) was
fitted to each of the response variable (Yk) with the independent variables (Xi).
= + Σ 4 =1+Σ 24 =1+ Σ 4 ≠1=1 (4.2)
Where bk0, bki, bkiiand bkijare the constant, linear, quadratic and cross product
regression coefficients, respectively and Xi are the coded independent variables of
X1, X2, X3.
3. PROCEDURE
Testing of a Crop Residue Mobile di Integrator Machine in Select Filed
The procedure adopted for testing of a crop residue Mobile disintegrator Machine is
given below. The experiment test plot was 25*25m. Each observation was replicated
taken as five time .Operation of Testing of A crop residue mobile disintegrator
machine selected cotton plot was prepared for testing the A crop residue mobile
disintegrator machine as per the procedure given above and details of testing is
discussed in the section above section
According to BBD firstly select different plot of different cotton variety with
different moisture content. To measure Height of plant by the help of measuring tape,
Also measure the plot by 25m x25mm In the selected plot take a sample of cotton
stalk for the measurement of moisture content in the specific plot. To prepared various
size of blade with different angel like 1000
, 1050
, 1100.
.In the completion of first trial
the time was recorded, live cutting speed was recorded, level of fuel after trail check
it. No of plant cut is also noted to determination of cutting efficiency. The collected
crop residue in the bag was collected and mark it Trial one sample. The plot no one
testing is completed and collects all data for all different blade angle and speed
according toBBD The collected sample was mark and check it weight and size after
sampling.
Table 2 ANOVAs for cutting force
SL no Cutting force Cutting efficiency Collection
efficacy
Model( S) 0.001# 0.003# 0.004#
Std. Dev. 0.53 .49 1.23
Mean 10.09 96.37 77.12
R-Squared 0.94 .96 .92
Adj R-Squared 0.86 .92 .82
Lack of Fit( NS) 0.12 @ 0.0158@ 0065@
#Significant @: Non Significant

Design-Expert® Software
Factor Coding: Actual
Actual force (N)
Design points above predicted value
Design points below predicted value
12.95
7.92
X1 = A: blade angle
X2 = B: Impact Load
Actual Factor
C: Speed = 1800
10
15
20
25
30
100
102
104
106
108
110
7
8
9
10
11
12
13
14
Actualforce(N)
A: blade angle (Degree)
B: Impact Load (N)
Cutting Efficiency (%)
98.38
93.12
X1 = A: blade angle
X2 = B: Impact Load
Actual Factor
C: Speed = 1800
10
15
20
25
30
100
102
104
106
108
110
93
94
95
96
97
98
99
CuttingEfficiency(%)
A: blade angle (Degree)B: Impact Load (N)
Collection Efficiency (%)
82.08
73.18
X1 = A: blade angle
X2 = B: Impact Load
Actual Factor
C: Speed = 1800
10
15
20
25
30
100
102
104
106
108
110
72
74
76
78
80
82
84
CollectionEfficiency(%)
A: blade angle (Degree)B: Impact Load (N)
Figure 1 Effect of Impact load and Blade angle on Cutting force
Figure 2 Effect of Impact load and Blade angle on Cutting efficiency
Figure 3 Effect of Impact load and Blade angle on Cutting efficiency

4. RESULTS AND ANALYSIS
4.1. Analysis of Variance
Regression analysis and analysis of variance (ANOVA) were conducted for fitting the
models represented examines the statistical significance of the model terms. The
adequacy of the models were determined using model analysis, lack of fit test and R2
(coefficient of determination) analysis. The lack of fit is a measure of failure of a
model to represent data in the experiment domain at which at which points were not
included in the regression or variations in the models cannot be accounted for by
random error. If there is a significant lack of fit, as indicated by a low probability
value, the response predictor is discarded. The R2
is defined as the ratio of the
explained variation to the total variation and is a measure of the degree of fit.
Coefficient of variation (CV) indicates the relative dispersion of the experimental
points from the prediction of the model. Response surfaces were generated with the
help of commercial statistical package, Design Expert-version 10.0.2.
The results of this analysis are shown in Table 5 and in Table 6 It is known that if the P
value (significance probability value) is less than 0.05, the corresponding parameter or
variable is considered to be "significant" in influencing the output response, at 95 %
confidence level [29].From Table 5, it is evident that blade angle (A) and its square
combination (A2
) are "most significant" in the context of the response: cutting speed , as
corresponding P values are zero. The square combination of work speed (C2
) is also very
significant as its P value is very close to zero. And, individual effect of impact load (B) and
its square combination (B2
) are found to be significant as relevant P values are less than 0,05.
The cutting speed (C) and interactions like infeed-longitudinal feed (A x B), infeed-work
speed (A x C), and longitudinal feed-work speed (B x C) do not have significant effect on
vibration as their P values are more than 0.05.
4.2. Response Surface Plots
Response surface plots arc drawn based on the developed model equations for cutting force ,
cutting efficiency, and collection efficiency .These plots (and also the earlier contour plots)
arc generated for various combinations of the input parameters and different hold values of
the third parameter(s). Few of the response surface plots are given in Figs. 8, 9, and 10 for
vibration and in Figs. 11, 12, and 13 for surface roughness. Since each model has three
variables, one
5. CONCLUSIONS
The following conclusions are made from the present study
The use of Design Expert 10.0.2 software for experimental design with the
factorial Box-Behnken design matrices method was suitable for optimized solution of
the blade angle. This provides the optimized solution of the blade angle of 1050 with
an impact load of 20 N at a speed of 1800 rpm. This reduces the efforts required in
vast testing procedures with utilizing the all type of blade on the large field. The
software generated optimum conditions of independent variables with the predicted
values of responses. Solution number one, having the maximum desirability value
(0.92) with minimum blade angle of 9.2kg/ha and 98.207% cutting efficiency and
maximum collection efficiency was selected as the optimum conditions for mobile
disintegrator
Contour and surface plots for cutting force, cutting efficiency and collection efficiency of
mobile disintegrator machine show that the interactions of input parameters arc prominent for
both the responses. These plots may help to predict response(s) at some selected parametric
combination(s).

The above optimum parametric combination has been validated by confirmatory tests.
REFERENCE
[1] Oguz Perincek, Metin Colak (2013) Use of Experimental Box-Behnken Design
for the Estimation of Interactions Between Harmonic Currents Produced by
Single Phase Loads International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -
April 2013, pp.158-165 158 | P a g e
[2] Guowei Shu*, Chunji Dai, He Chen and Xu Wang (2013)Application of Box-
Behnken design in optimization for crude polysaccharides from fruits of Tribulus
terristris L. Journal of Chemical and Pharmaceutical Research, 2013, 5(10):342-
350Research Article ISSN : 0975-7384
[3] Sergio Luis Costa Ferreira a, Roy Edward Bruns b, Erik Galv˜ao Paranhos da
Silva a,Walter Nei Lopes dos Santos c, Cristina Maria Quintella a, Jorge
Mauricio David a, Jailson Bittencourt de Andrade a, Marcia Cristina Breitkreitz
b,Isabel Cristina Sales Fontes Jardim b, Benicio Barros Neto d(2007) Statistical
designs and response surface techniques for the optimization of chromatographic
systems, Journal of Chromatography A, 1158 (2007) 2–14
[4] Rodrapati ramesh, pal pradip kumar, Bandyopadyay Ashis, (2015) Modeling and
optimization of machining parameters in cylindrical grinding process. ,
International journal of manufacturing technology DOI 10.2007/S00170-015-
7500
[5] Myalowenkosi Sabela I., Kanchi Suvardhan *, Ayyappa Bathinapatla, Bisetty
Krishna(2014)
[6] A Box-Behnken Design and Response Surface Approach for the Simultaneous
Determination of Chromium (III) and (VI) Using Catalytic Differential Pulse
Polarography international journal of electrochemical science, Int. J.
Electrochem. Sci., 9 (2014) 6751 - 6764
[7] Received: 5 August 2014 / Accepted: 12 September 2014 / Published: 29
September 2014
[8] Montgomery DC (2001) Design and analysis of experiments. Wiley, New York
Khayet M, Seman MNA, I lilal N (2010) Response surface modeling and
optimization of composite nano-filtration modified membranes. J Memb Sci
349(1-2):! 13-122
[9] Cclep O, Asian N. Alp I, Tasdemir G (2011) Optimization of some parameters of
stirred mill for ultra-fine grinding of refractory Au/Ag ores. Powder Technol
208(1):121-127.

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