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: This paper presents an optimization of the hull
separation, i.e. transverse clearance. The main objective is to identify
the feasible speed ranges and find the optimum transverse clearance
considering the minimum wave-making resistance. The dimensions
and the weight of hardware systems installed in the catamaran
structured fuel cell powered USV (Unmanned Surface Vehicle) were
considered as constraints. As the CAE (Computer Aided Engineering)
platform FRIENDSHIP-Framework was used. The hull surface
modeling, DoE (Design of Experiment), Tangent search optimization,
tool integration and the process automation were performed by
FRIENDSHIP-Framework. The hydrodynamic result was evaluated
by XPAN the potential solver of SHIPFLOW.
Abstract: The use of electronic sensors in the electronics
industry has become increasingly popular over the past few years,
and it has become a high competition product. The frequency
adjustment process is regarded as one of the most important process
in the electronic sensor manufacturing process. Due to inaccuracies
in the frequency adjustment process, up to 80% waste can be caused
due to rework processes; therefore, this study aims to provide a
preliminary understanding of the role of parameters used in the
frequency adjustment process, and also make suggestions in order to
further improve performance. Four parameters are considered in this
study: air pressure, dispensing time, vacuum force, and the distance
between the needle tip and the product. A full factorial design for
experiment 2k was considered to determine those parameters that
significantly affect the accuracy of the frequency adjustment process,
where a deviation in the frequency after adjustment and the target
frequency is expected to be 0 kHz. The experiment was conducted on
two levels, using two replications and with five center-points added.
In total, 37 experiments were carried out. The results reveal that air
pressure and dispensing time significantly affect the frequency
adjustment process. The mathematical relationship between these
two parameters was formulated, and the optimal parameters for air
pressure and dispensing time were found to be 0.45 MPa and 458 ms,
respectively. The optimal parameters were examined by carrying out
a confirmation experiment in which an average deviation of 0.082
kHz was achieved.
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: Many medical devices are driven by motor pumps. Some researchers reported that the vibration mainly affected medical devices using a motor pump. The purpose of this study was to examine the effect of stiffness and damping coefficient in a 3-dimensional (3D) model of a motor pump and spring. In the present paper, experimental and mathematical tests for the moments of inertia of the 3D model and the material properties were investigated by an INSTRON machine. The response surfaces could be generated by using 3D multi-body analysis and the design of experiment method. It showed that differences in contours of the response surface were clearly found for the particular area. Displacement of the center of the motor pump was decreased at K≈2000 N/M, C≈12.5 N-sec/M. However, the frequency was increased at K≈2000 N/M, C≈15 N-sec/M. In this study, this study suggested experimental technique for vibration reduction for a motor pump in medical device. The combined method suggested in this study will greatly contribute to design of medical devices concerning vibration and noise intervention.
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.
Abstract: Multiprocessor task scheduling is a NP-hard problem and Genetic Algorithm (GA) has been revealed as an excellent technique for finding an optimal solution. In the past, several methods have been considered for the solution of this problem based on GAs. But, all these methods consider single criteria and in the present work, minimization of the bi-criteria multiprocessor task scheduling problem has been considered which includes weighted sum of makespan & total completion time. Efficiency and effectiveness of genetic algorithm can be achieved by optimization of its different parameters such as crossover, mutation, crossover probability, selection function etc. The effects of GA parameters on minimization of bi-criteria fitness function and subsequent setting of parameters have been accomplished by central composite design (CCD) approach of response surface methodology (RSM) of Design of Experiments. The experiments have been performed with different levels of GA parameters and analysis of variance has been performed for significant parameters for minimisation of makespan and total completion time simultaneously.
Abstract: Deep cold rolling (DCR) and low plasticity burnishing (LPB) process are cold working processes, which easily produce a smooth and work-hardened surface by plastic deformation of surface irregularities. The present study focuses on the surface roughness and surface hardness aspects of AISI 4140 work material, using fractional factorial design of experiments. The assessment of the surface integrity aspects on work material was done, in order to identify the predominant factors amongst the selected parameters. They were then categorized in order of significance followed by setting the levels of the factors for minimizing surface roughness and/or maximizing surface hardness. In the present work, the influence of main process parameters (force, feed rate, number of tool passes/overruns, initial roughness of the work piece, ball material, ball diameter and lubricant used) on the surface roughness and the hardness of AISI 4140 steel were studied for both LPB and DCR process and the results are compared. It was observed that by using LPB process surface hardness has been improved by 167% and in DCR process surface hardness has been improved by 442%. It was also found that the force, ball diameter, number of tool passes and initial roughness of the workpiece are the most pronounced parameters, which has a significant effect on the work piece-s surface during deep cold rolling and low plasticity burnishing process.
Abstract: A mathematical model of the surface roughness
has been developed by using response surface methodology
(RSM) in grinding of AISI D2 cold work tool steels. Analysis
of variance (ANOVA) was used to check the validity of the
model. Low and high value for work speed and feed rate are
decided from design of experiment. The influences of all
machining parameters on surface roughness have been
analyzed based on the developed mathematical model. The
developed prediction equation shows that both the feed rate
and work speed are the most important factor that influences
the surface roughness. The surface roughness was found to be
the lowers with the used of low feed rate and low work speed.
Accuracy of the best model was proved with the testing data.
Abstract: Response surface methodology (RSM) is a very
efficient tool to provide a good practical insight into developing new
process and optimizing them. This methodology could help
engineers to raise a mathematical model to represent the behavior of
system as a convincing function of process parameters.
Through this paper the sequential nature of the RSM surveyed for process
engineers and its relationship to design of experiments (DOE), regression
analysis and robust design reviewed. The proposed four-step procedure in
two different phases could help system analyst to resolve the parameter
design problem involving responses. In order to check accuracy of the
designed model, residual analysis and prediction error sum of squares
(PRESS) described.
It is believed that the proposed procedure in this study can resolve a
complex parameter design problem with one or more 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 purpose of this research is to reduce the amount of incomplete coating of stainless steel washers in the electrodeposition painting process by using an experimental design technique. The surface preparation was found to be a major cause of painted surface quality. The influence of pretreating and painting process parameters, which are cleaning time, chemical concentration and shape of hanger were studied. A 23 factorial design with two replications was performed. The analysis of variance for the designed experiment showed the great influence of cleaning time and shape of hanger. From this study, optimized cleaning time was determined and a newly designed electrical conductive hanger was proved to be superior to the original one. The experimental verification results showed that the amount of incomplete coating defects decreased from 4% to 1.02% and operation cost decreased by 10.5%.