Abstract: Current trends in manufacturing are characterized by
production broadening, innovation cycle shortening, and the products
having a new shape, material and functions. The production strategy
focused on time needed change from the traditional functional
production structure to flexible manufacturing cells and lines.
Production by automated manufacturing system (AMS) is one of the
most important manufacturing philosophies in the last years. The
main goals of the project we are involved in lies on building a
laboratory in which will be located a flexible manufacturing system
consisting of at least two production machines with NC control
(milling machines, lathe). These machines will be linked to a
transport system and they will be served by industrial robots. Within
this flexible manufacturing system a station for the quality control
consisting of a camera system and rack warehouse will be also
located. The design, analysis and improvement of this manufacturing
system, specially with a special focus on the communication among
devices constitute the main aims of this paper. The key determining
factors for the manufacturing system design are: the product, the
production volume, the used machines, the disposable manpower, the
disposable infrastructure and the legislative frame for the specific
cases.
Abstract: This paper develops a quality estimation method with
the application of fuzzy hierarchical clustering. Quality estimation is
essential to quality control and quality improvement as a precise
estimation can promote a right decision-making in order to help
better quality control. Normally the quality of finished products in
manufacturing system can be differentiated by quality standards. In
the real life situation, the collected data may be vague which is not
easy to be classified and they are usually represented in term of fuzzy
number. To estimate the quality of product presented by fuzzy
number is not easy. In this research, the trapezoidal fuzzy numbers
are collected in manufacturing process and classify the collected data
into different clusters so as to get the estimation. Since normal
hierarchical clustering methods can only be applied for real numbers,
fuzzy hierarchical clustering is selected to handle this problem based
on quality standards.
Abstract: In some real applications of Statistical Process Control
it is necessary to design a control chart to not detect small process
shifts, but keeping a good performance to detect moderate and large
shifts in the quality. In this work we develop a new quality control
chart, the synthetic T2 control chart, that can be designed to cope with
this objective. A multi-objective optimization is carried out employing
Genetic Algorithms, finding the Pareto-optimal front of
non-dominated solutions for this optimization problem.
Abstract: Hot Mix Asphalt (HMA) is one of the most
commonest constructed asphalts in Iran and the quality control of
constructed roads with HMA have been always paid due attention by
researchers. The quality control of constructed roads with this
method is being usually carried out by measuring volumetric
parameters of HMA marshall samples. One of the important
parameters that has a critical role in changing these volumetric
parameters is “compaction temperature"; which as a result of its
changing, volumetric parameters of Marshall Samples and
subsequently constructed asphalt is encountered with variations. In
this study, considering the necessity of preservation of the
compaction temperature, the effect of various temperatures on Hot
Mix Asphalt (HMA) samples properties has been evaluated. As well,
to evaluate the effect of this parameter on different grading, two
different grading (Top coat index grading and binder index grading)
have been used and samples were compacted at 5 various
temperatures.
Abstract: Numerous concrete structures projects are currently running in Libya as part of a US$50 billion government funding. The
quality of concrete used in 20 different construction projects were assessed based mainly on the concrete compressive strength achieved. The projects are scattered all over the country and are at
various levels of completeness. For most of these projects, the
concrete compressive strength was obtained from test results of a
150mm standard cube mold. Statistical analysis of collected concrete
compressive strengths reveals that the data in general followed a
normal distribution pattern. The study covers comparison and assessment of concrete quality aspects such as: quality control, strength range, data standard deviation, data scatter, and ratio of minimum strength to design strength. Site quality control for these projects ranged from very good to poor according to ACI214 criteria [1]. The ranges (Rg) of the strength (max. strength – min. strength) divided by average strength are from (34% to 160%). Data scatter is
measured as the range (Rg) divided by standard deviation () and is
found to be (1.82 to 11.04), indicating that the range is ±3σ.
International construction companies working in Libya follow
different assessment criteria for concrete compressive strength in lieu
of national unified procedure. The study reveals that assessments of
concrete quality conducted by these construction companies usually
meet their adopted (internal) standards, but sometimes fail to meet
internationally known standard requirements. The assessment of
concrete presented in this paper is based on ACI, British standards
and proposed Libyan concrete strength assessment criteria.
Abstract: Quality control charts are very effective in detecting
out of control signals but when a control chart signals an out of
control condition of the process mean, searching for a special cause
in the vicinity of the signal time would not always lead to prompt
identification of the source(s) of the out of control condition as the
change point in the process parameter(s) is usually different from the
signal time. It is very important to manufacturer to determine at what
point and which parameters in the past caused the signal. Early
warning of process change would expedite the search for the special
causes and enhance quality at lower cost. In this paper the quality
variables under investigation are assumed to follow a multivariate
normal distribution with known means and variance-covariance
matrix and the process means after one step change remain at the new
level until the special cause is being identified and removed, also it is
supposed that only one variable could be changed at the same time.
This research applies artificial neural network (ANN) to identify the
time the change occurred and the parameter which caused the change
or shift. The performance of the approach was assessed through a
computer simulation experiment. The results show that neural
network performs effectively and equally well for the whole shift
magnitude which has been considered.
Abstract: Foodborne Salmonella infections have become a
major problem world wide. Salmonellosis transmitted from fish are
quite common. Established quality control measures exist for export
oriented fish, none exists for fish consumed locally. This study aimed
at characterization of Salmonella isolated from Nile tilapia . The
study was carried out in selected beaches along L. Victoria in
Western Kenya between March and June 2007. One hundred and
twenty fish specimens were collected. Salmonella isolates were
confirmed using serotyping, biochemical testing in addition to malic
acid dehydrogenase (mdh) and fliC gene sequencing. Twenty
Salmonella isolates were confirmed by mdh gene sequencing. Nine
(9) were S. enterica serotype typhimurium, four (4) were S. enterica
Serotype, enteritidis and seven (7) were S. enterica serotype typhi.
Nile tilapia have a role in transmission of Salmonellosis in the study
area, poor sanitation was a major cause of pollution at the beach
inshore waters.
Abstract: In the semiconductor manufacturing process, large
amounts of data are collected from various sensors of multiple
facilities. The collected data from sensors have several different characteristics
due to variables such as types of products, former processes
and recipes. In general, Statistical Quality Control (SQC) methods
assume the normality of the data to detect out-of-control states of
processes. Although the collected data have different characteristics,
using the data as inputs of SQC will increase variations of data,
require wide control limits, and decrease performance to detect outof-
control. Therefore, it is necessary to separate similar data groups
from mixed data for more accurate process control. In the paper,
we propose a regression tree using split algorithm based on Pearson
distribution to handle non-normal distribution in parametric method.
The regression tree finds similar properties of data from different
variables. The experiments using real semiconductor manufacturing
process data show improved performance in fault detecting ability.
Abstract: Quality control charts indicate out of control
conditions if any nonrandom pattern of the points is observed or any
point is plotted beyond the control limits. Nonrandom patterns of
Shewhart control charts are tested with sensitizing rules. When the
processes are defined with fuzzy set theory, traditional sensitizing
rules are insufficient for defining all out of control conditions. This is
due to the fact that fuzzy numbers increase the number of out of
control conditions. The purpose of the study is to develop a set of
fuzzy sensitizing rules, which increase the flexibility and sensitivity
of fuzzy control charts. Fuzzy sensitizing rules simplify the
identification of out of control situations that results in a decrease in
the calculation time and number of evaluations in fuzzy control chart
approach.