Abstract: In the present study, effect of critical medium components (a total of fifteen components) on ethanol production from waste cashew apple juice (CAJ) using yeast Saccharomyces diasticus was studied. A statistical response surface methodology
(RSM) based Plackett-Burman Design (PBD) was used for the design of experiments. The design contains a total of 32 experimental trails. The effect of medium components on ethanol was studied at two different levels such as low concentration level (-) and high concentration levels (+). The dependent variables selected in this study were ethanol concentration (g/L) and cellmass concentration (g/L). Data obtained from RSM on ethanol production were subjected to analysis of variance (ANOVA). In general, initial substrate concentration significantly influenced the microbial growth and product formation. Of the medium components evaluated, CAJ concentration, yeast extract, (NH4)2SO4, and malt extract showed significant effect on ethanol fermentation. A second-order polynomial model was used to predict the experimental data and the model fitted the data with a high correlation coefficient (R2 > 0.98).
Maximum ethanol (15.3 g/L) and biomass (6.4 g/L) concentrations
were obtained at the optimum medium composition and at optimum
condition (temperature-30°C; initial pH-6.8) after 72 h fermentation
using S.diasticus.
Abstract: The functional performance of machined components, often, depends on surface topography, hardness, nature of stress and strain induced on the surface, etc. Invariably, surfaces of metallic components obtained by turning, milling, etc., consist of irregularities such as machining marks are responsible for the above. Surface finishing/coating processes used to produce improved surface quality/textures are classified as chip-removal and chip-less processes. Burnishing is chip-less cold working process carried out to improve surface finish, hardness and resistance to fatigue and corrosion; not obtainable by other surface coating and surface treatment processes. It is a very simple, but effective method which improves surface characteristics and is reported to introduce compressive stresses.
Of late, considerable attention is paid to post-machining, finishing operations, such as burnishing. During burnishing the micro-irregularities start to deform plastically, initially the crests are gradually flattened and zones of reduced deformation are formed. When all the crests are deformed, the valleys between the micro-irregularities start moving in the direction of the newly formed surface. The grain structure is then condensed, producing a smoother and harder surface with superior load-carrying and wear-resistant capabilities.
Burnishing can be performed on a lathe with a highly polished ball or roller type tool which is traversed under force over a rotating/stationary work piece. Often, several passes are used to obtain the work piece surface with the desired finish and hardness.
This paper presents the findings of an experimental investigation on the effect of ball burnishing parameters such as, burnishing speed, feed, force and number of passes; on surface roughness (Ra) and micro-hardness (Hv) of a 60/40 copper/zinc alloy, using a 2-level fractional factorial design of experiments (DoE). Mathematical models were developed to predict surface roughness and hardness generated by burnishing in terms of the above process parameters. A ball-type tool, designed and constructed from a high chrome steel material (HRC=63 and Ra=0.012 µm), was used for burnishing of fine-turned cylindrical bars (0.68-0.78µm and 145Hv). They are given by,
Ra= 0.305-0.005X1 - 0.0175X2 + 0.0525X4 + 0.0125X1X4 -0.02X2X4 - 0.0375X3X4
Hv=160.625 -2.37 5X1 + 5.125X2 + 1.875X3 + 4.375X4 - 1.625X1X4 + 4.375X2X4 - 2.375X3X4
High surface microhardness (175HV) was obtained at 400rpm, 2passes, 0.05mm/rev and 15kgf., and high surface finish (0.20µm) was achieved at 30kgf, 0.1mm/rev, 112rpm and single pass. In other words, surface finish improved by 350% and microhardness improved by 21% compared to as machined conditions.
Abstract: In production processes, assembly conceals a considerable potential for increased efficiency in terms of lowering production costs. Due to the individualisation of customer requirements, product variants have increased in recent years. Simultaneously, the portion of automated production systems has increased. A challenge is to adapt the flexibility and adaptability of automated systems to these changes. The Institute for Production Systems and Logistics developed an aerodynamic orientation system for feeding technology. When changing to other components, only four parameters must be adjusted. The expenditure of time for setting parameters is high. An objective therefore is developing an optimisation algorithm for automatic parameter configuration. Know how regarding the interaction of the four parameters and their effect on the sizes to be optimised is required in order to be able to develop a more efficient algorithm. This article introduces an analysis of the interactions between parameters and their influence on the quality of feeding.
Abstract: Design of experiments made for correlated the hormonal activity of steroids and their effect on the vaginal structure properties. Vaginal wall consists of distinct layers of cells which strongly differ regarding growth in answer to steroid hormones. The presence study carried out for functional evaluation and sustained replacement effect of the reproductive hormones on the vaginal morphometry in the rabbit. Rabbits were maintained control (Co) or ovariectomized (Ovz) and continuously treated with estradiol (Ovz E) and estradiol mixture with testosterone (Ovz ET). Relative to control rabbits with the distinct tissue distribution pattern, ovariectomized animals displayed declined epithelial thickness and atrophy of the muscularis layer that cause the vaginal wall thinning. Estradiol treatment of Orz rabbits inverted these changes to status of the preovariectomy. Histometric measurements in Ovz ET group completely including of thickness of the epithelial and muscular layers was greater in comparison to that of control animals.
Abstract: In recent years, response surface methodology (RSM) has
brought many attentions of many quality engineers in different
industries. Most of the published literature on robust design
methodology is basically concerned with optimization of a single
response or quality characteristic which is often most critical to
consumers. For most products, however, quality is multidimensional,
so it is common to observe multiple responses in an experimental
situation. Through this paper interested person will be familiarize
with this methodology via surveying of the most cited technical
papers.
It is believed that the proposed procedure in this study can resolve
a complex parameter design problem with more than two responses.
It can be applied to those areas where there are large data sets and a
number of responses are to be optimized simultaneously. In addition,
the proposed procedure is relatively simple and can be implemented
easily by using ready-made standard statistical packages.
Abstract: The operating control parameters of injection
flushing type of electrical discharge machining process on stainless
steel 304 workpiece with copper tools are being optimized
according to its individual machining characteristic i.e. material
removal rate (MRR). Lower MRR during EDM machining process
may decrease its- machining productivity. Hence, the quality
characteristic for MRR is set to higher-the-better to achieve the
optimum machining productivity. Taguchi method has been used
for the construction, layout and analysis of the experiment for each
of the machining characteristic for the MRR. The use of Taguchi
method in the experiment saves a lot of time and cost of preparing
and machining the experiment samples. Therefore, an L18
Orthogonal array which was the fundamental component in the
statistical design of experiments has been used to plan the
experiments and Analysis of Variance (ANOVA) is used to
determine the optimum machining parameters for this machining
characteristic. The control parameters selected for this
optimization experiments are polarity, pulse on duration, discharge
current, discharge voltage, machining depth, machining diameter
and dielectric liquid pressure. The result had shown that the higher
the discharge voltage, the higher will be the MRR.
Abstract: This paper presents a model for an unreliable
production line, which is operated according to demand with constant
work-in-process (CONWIP). A simulation model is developed based
on the discrete model and several case problems are analyzed using
the model. The model is utilized to optimize storage space capacities
at intermediate stages and the number of kanbans at the last stage,
which is used to trigger the production at the first stage. Furthermore,
effects of several line parameters on production rate are analyzed
using design of experiments.
Abstract: recurrent neural network (RNN) is an efficient tool for
modeling production control process as well as modeling services. In
this paper one RNN was combined with regression model and were
employed in order to be checked whether the obtained data by the
model in comparison with actual data, are valid for variable process
control chart. Therefore, one maintenance process in workshop of
Esfahan Oil Refining Co. (EORC) was taken for illustration of
models. First, the regression was made for predicting the response
time of process based upon determined factors, and then the error
between actual and predicted response time as output and also the
same factors as input were used in RNN. Finally, according to
predicted data from combined model, it is scrutinized for test values
in statistical process control whether forecasting efficiency is
acceptable. Meanwhile, in training process of RNN, design of
experiments was set so as to optimize the RNN.
Abstract: Ultrasonic machining (USM) is a non-traditional
machining process being widely used for commercial machining of
brittle and fragile materials such as glass, ceramics and
semiconductor materials. However, USM could be a viable
alternative for machining a tough material such as titanium; and this
aspect needs to be explored through experimental research. This
investigation is focused on exploring the use of ultrasonic machining
for commercial machining of pure titanium (ASTM Grade-I) and
evaluation of tool wear rate (TWR) under controlled experimental
conditions. The optimal settings of parameters are determined
through experiments planned, conducted and analyzed using Taguchi
method. In all, the paper focuses on parametric optimization of
ultrasonic machining of pure titanium metal with TWR as response,
and validation of the optimized value of TWR by conducting
confirmatory experiments.
Abstract: Deep cold rolling (DCR) is a cold working process, which easily produces a smooth and work-hardened surface by plastic deformation of surface irregularities. In the present study, the influence of main deep cold rolling process parameters on the surface roughness and the hardness of AISI 4140 steel were studied by using fractional factorial design of experiments. The assessment of the surface integrity aspects on work material was done, in terms of identifying the predominant factor amongst the selected parameters, their order of significance and setting the levels of the factors for minimizing surface roughness and/or maximizing surface hardness. It was found that the ball diameter, rolling force, initial surface roughness and number of tool passes are the most pronounced parameters, which have great effects on the work piece-s surface during the deep cold rolling process. A simple, inexpensive and newly developed DCR tool, with interchangeable collet for using different ball diameters, was used throughout the experimental work presented in this paper.
Abstract: The objective of this research is parameters optimized
of the stair shape workpiece which is cut by CNC Wire-Cut EDM
(WEDW). The experiment material is SKD-11 steel of stair-shaped
with variable height workpiece 10, 20, 30 and 40 mm. with the same
10 mm. thickness are cut by Sodick's CNC Wire-Cut EDM model
AD325L.
The experiments are designed by 3k full factorial experimental
design at 3 level 2 factors and 9 experiments with 2 replicate. The
selected two factor are servo voltage (SV) and servo feed rate (SF)
and the response is cutting thickness error. The experiment is divided
in two experiments. The first experiment determines the significant
effective factor at confidential interval 95%. The SV factor is the
significant effective factor from first result. In order to result smallest
cutting thickness error of workpieces is 17 micron with the SV value
is 46 volt. Also show that the lower SV value, the smaller different
thickness error of workpiece. Then the second experiment is done to
reduce different cutting thickness error of workpiece as small as
possible by lower SV. The second experiment result show the
significant effective factor at confidential interval 95% is the SV
factor and the smallest cutting thickness error of workpieces reduce
to 11 micron with the experiment SV value is 36 volt.
Abstract: Rutting is one of the major load-related distresses in airport flexible pavements. Rutting in paving materials develop gradually with an increasing number of load applications, usually appearing as longitudinal depressions in the wheel paths and it may be accompanied by small upheavals to the sides. Significant research has been conducted to determine the factors which affect rutting and how they can be controlled. Using the experimental design concepts, a series of tests can be conducted while varying levels of different parameters, which could be the cause for rutting in airport flexible pavements. If proper experimental design is done, the results obtained from these tests can give a better insight into the causes of rutting and the presence of interactions and synergisms among the system variables which have influence on rutting. Although traditionally, laboratory experiments are conducted in a controlled fashion to understand the statistical interaction of variables in such situations, this study is an attempt to identify the critical system variables influencing airport flexible pavement rut depth from a statistical DoE perspective using real field data from a full-scale test facility. The test results do strongly indicate that the response (rut depth) has too much noise in it and it would not allow determination of a good model. From a statistical DoE perspective, two major changes proposed for this experiment are: (1) actual replication of the tests is definitely required, (2) nuisance variables need to be identified and blocked properly. Further investigation is necessary to determine possible sources of noise in the experiment.
Abstract: The present work analyses different parameters of pressure die casting to minimize the casting defects. Pressure diecasting is usually applied for casting of aluminium alloys. Good surface finish with required tolerances and dimensional accuracy can be achieved by optimization of controllable process parameters such as solidification time, molten temperature, filling time, injection pressure and plunger velocity. Moreover, by selection of optimum process parameters the pressure die casting defects such as porosity, insufficient spread of molten material, flash etc. are also minimized. Therefore, a pressure die casting component, carburetor housing of aluminium alloy (Al2Si2O5) has been considered. The effects of selected process parameters on casting defects and subsequent setting of parameters with the levels have been accomplished by Taguchi-s parameter design approach. The experiments have been performed as per the combination of levels of different process parameters suggested by L18 orthogonal array. Analyses of variance have been performed for mean and signal-to-noise ratio to estimate the percent contribution of different process parameters. Confidence interval has also been estimated for 95% consistency level and three conformational experiments have been performed to validate the optimum level of different parameters. Overall 2.352% reduction in defects has been observed with the help of suggested optimum process parameters.
Abstract: Electro Chemical Discharge Machining (ECDM) is an
emerging hybrid machining process used in precision machining of hard and brittle non-conducting materials. The present paper gives a
critical review on materials machined by ECDM under the prevailing machining conditions; capability indicators of the process are
reported. Some results obtained while performing experiments in micro-channeling on soda lime glass using ECDM are also presented. In these experiments, Tool Wear (TW) and Material Removal (MR)
were studied using design of experiments and L–4 orthogonal array. Experimental results showed that the applied voltage was the most influencing parameter in both MR and TW studies. Field
emission scanning electron microscopy (FESEM) results obtained on the microchannels confirmed the presence of micro-cracks, primarily responsible for MR. Chemical etching was also seen along the edges.
The Energy dispersive spectroscopy (EDS) results were used to detect the elements present in the debris and specimens.
Abstract: Development of motor car safety devices has reduced
fatality rates in car accidents. Yet despite this increase in car safety,
neck injuries resulting from rear impact collisions, particularly at low
speed, remain a primary concern. In this study, FEA(Finite Element
Analysis) of seat was performed to evaluate neck injuries in rear
impact. And the FEA result was verified by comparison with the actual
test results. The dummy used in FE model and actual test is BioRID II
which is regarded suitable for rear impact collision analysis. A
threshold of the BioRID II neck injury indicators was also proposed to
upgrade seat performance in order to reduce whiplash injury. To
optimize the seat for a low-speed rear impact collision, a method was
proposed, which is multi-objective optimization idea using DOE
(Design of Experiments) results.
Abstract: In this paper, a tooth shape optimization method for
cogging torque reduction in Permanent Magnet (PM) motors is
developed by using the Reduced Basis Technique (RBT) coupled by
Finite Element Analysis (FEA) and Design of Experiments (DOE)
methods. The primary objective of the method is to reduce the
enormous number of design variables required to define the tooth
shape. RBT is a weighted combination of several basis shapes. The
aim of the method is to find the best combination using the weights
for each tooth shape as the design variables. A multi-level design
process is developed to find suitable basis shapes or trial shapes at
each level that can be used in the reduced basis technique. Each level
is treated as a separated optimization problem until the required
objective – minimum cogging torque – is achieved. The process is
started with geometrically simple basis shapes that are defined by
their shape co-ordinates. The experimental design of Taguchi method
is used to build the approximation model and to perform
optimization. This method is demonstrated on the tooth shape
optimization of a 8-poles/12-slots PM motor.
Abstract: This research studied the appropriate factors and conditions for laminated bamboo bending by Design of Experiments (DOE). The interested factors affecting the spring back in laminates bamboo were (1) time, (2) thickness, and (3) frequency. This experiment tested the specimen by using high frequency machine and measured its spring back immediately and next 24 hours for comparing the spring back ratio. Results from the experiments showed that significant factors having major influence to bending of laminates bamboo were thickness and frequency. The appropriate conditions of thickness and frequency were 4 mm. and 1.5 respectively.
Abstract: We study the spatial design of experiment and we want to select a most informative subset, having prespecified size, from a set of correlated random variables. The problem arises in many applied domains, such as meteorology, environmental statistics, and statistical geology. In these applications, observations can be collected at different locations and possibly at different times. In spatial design, when the design region and the set of interest are discrete then the covariance matrix completely describe any objective function and our goal is to choose a feasible design that minimizes the resulting uncertainty. The problem is recast as that of maximizing the determinant of the covariance matrix of the chosen subset. This problem is NP-hard. For using these designs in computer experiments, in many cases, the design space is very large and it's not possible to calculate the exact optimal solution. Heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective and exact solution not possible. We developed a GA algorithm to take advantage of the exploratory power of this algorithm. The successful application of this method is demonstrated in large design space. We consider a real case of design of experiment. In our problem, design space is very large and for solving the problem, we used proposed GA algorithm.
Abstract: Development of artificial neural network (ANN) for
prediction of aluminum workpieces' surface roughness in ultrasonicvibration
assisted turning (UAT) has been the subject of the present
study. Tool wear as the main cause of surface roughness was also
investigated. ANN was trained through experimental data obtained
on the basis of full factorial design of experiments. Various
influential machining parameters were taken into consideration. It
was illustrated that a multilayer perceptron neural network could
efficiently model the surface roughness as the response of the
network, with an error less than ten percent. The performance of the
trained network was verified by further experiments. The results of
UAT were compared with the results of conventional turning
experiments carried out with similar machining parameters except for
the vibration amplitude whence considerable reduction was observed
in the built-up edge and the surface roughness.
Abstract: In this paper, a framework for the simplification and
standardization of metaheuristic related parameter-tuning by applying
a four phase methodology, utilizing Design of Experiments and
Artificial Neural Networks, is presented. Metaheuristics are multipurpose
problem solvers that are utilized on computational optimization
problems for which no efficient problem specific algorithm
exist. Their successful application to concrete problems requires the
finding of a good initial parameter setting, which is a tedious and
time consuming task. Recent research reveals the lack of approach
when it comes to this so called parameter-tuning process. In the
majority of publications, researchers do have a weak motivation for
their respective choices, if any. Because initial parameter settings
have a significant impact on the solutions quality, this course of
action could lead to suboptimal experimental results, and thereby
a fraudulent basis for the drawing of conclusions.