Study Of The Effect Of Temperature And Relative Humidity On The Measurement Variance Conducted In The Metrology Lab, FKP.
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
STUDY OF THE EFFECT OF TEMPERATURE AND RELATIVE
HUMIDITY ON THE MEASUREMENT VARIANCE CONDUCTED
IN THE METROLOGY LAB, FKP
This report submitted in accordance with requirement of the Universiti Teknikal
Malaysia Melaka (UTeM) for the Bachelor Degree of Manufacturing Engineering
(Manufacturing Process) with Honours.
By
AMER ADLI BIN MOHD DZAINUDIN
FACULTY OF MANUFACTURING ENGINEERING
MEI 2010
UTeM Library (Pind.1/2005)
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
BORANG PENGESAHAN STATUS TESIS*
JUDUL: Study of the Effect of Temperature and Relative Humidity on the
Measurement Variance Conducted in Metrology Lab, FKP
SESI PENGAJIAN: 2009-2010
Saya
_____________________________________________________________________
AMER ADLI BIN MOHD DZAINUDIN
mengaku membenarkan tesis (PSM/Sarjana/Doktor Falsafah) ini disimpan di
Perpustakaan Universiti Teknikal Malaysia Melaka (UTeM) dengan syarat-syarat
kegunaan seperti berikut:
1. Tesis adalah hak milik Universiti Teknikal Malaysia Melaka.
2. Perpustakaan Universiti Teknikal Malaysia Melaka dibenarkan membuat salinan
untuk tujuan pengajian sahaja.
3. Perpustakaan dibenarkan membuat salinan tesis ini sebagai bahan pertukaran
antara institusi pengajian tinggi.
4. **Sila tandakan (√)
SULIT
TERHAD
√
(Mengandungi maklumat yang berdarjah keselamatan
atau kepentingan Malaysia yang termaktub di dalam
AKTA RAHSIA RASMI 1972)
(Mengandungi maklumat TERHAD yang telah ditentukan
oleh organisasi/badan di mana penyelidikan dijalankan)
TIDAK TERHAD
Disahkan oleh:
(TANDATANGAN PENULIS)
(TANDATANGAN PENYELIA)
Alamat Tetap:
Blok 26-7-6, Taman Desa Tasek,
Sungai Besi, 57100
Kuala Lumpur
Cop Rasmi:
Tarikh: _______________________
25 Mei 2010
25 Mei 2010
Tarikh: _______________________
* Tesis dimaksudkan sebagai tesis bagi Ijazah Doktor Falsafah dan Sarjana secara penyelidikan, atau
disertasi bagi pengajian secara kerja kursus dan penyelidikan, atau Laporan Projek Sarjana Muda (PSM).
** Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi berkenaan
dengan menyatakan sekali sebab dan tempoh tesis ini perlu dikelaskan sebagai SULIT atau TERHAD.
DECLARATION
I hereby declare that this report entitled “STUDY OF THE EFFECT OF
TEMPERATURE AND RELATIVE HUMIDITY ON THE MEASUREMENT
VARIANCE CONDUCTED IN THE METROLOGY LAB, FKP” is the result of my
own research except as cited in the references.
Signature
:
Author’s name
:
Amer Adli bin Mohd Dzainudin
Date
:
25 May 2010
APPROVAL
This report is submitted to the Faculty of Manufacturing Engineering of UTem as a
partial fulfillment of the requirements for the degree of Bachelor of Manufacturing
Engineering (Manufacturing Process). The members of the supervisory committee are as
follow:
Mohd Fairuz Bin Dimin
(PSM Supervisor)
25 May 2010
ABSTRACT
The aim of this project is to study the effect of temperature and relative humidity of the
environment on the measurement variance and error conducted in the Metrology Lab,
Faculty of Manufacturing Engineering, UTeM. The scope of this project is to analyze
the statistical error and correlation between the temperature and relative humidity
variances of the environment against the accuracy of selected metrology lab apparatus
using statistical software SPSS. Measurements are taken using vernier calliper in
different temperature and relative humidity conditions at several different locations and
hours of the day. The results obtained were compared against other type of variances
such as the repeatability and equipment error for their type and magnitude of correlations
and errors
i
ABSTRAK
Projek ini adalah untuk menganalisis kesan suhu dan juga kelembapan ke atas varian
pengukuran yang diambil di dalam Makmal Metrologi di Fakulti Kejuruteraan
Pembuatan. Tujuan projek ini dibangunkan adalah untuk menentukan pengaruh suhu dan
kelembapan ke atas ketepatan instrumen pengukuran. Disebabkan Makmal Metrologi
telah berpindah tempat, projek ini telah dibangunkan untuk menganalisis dan meneliti
adakah suhu dan kelembapan di dalam Makmal Metrologi baru yang mungkin
disebabkan penyaman udara dan alam sekitarnya akan memberikan kesan yang besar ke
atas ketepatan instrumen sehingga membolehkan pertikaian berlaku ke atas pengukuran.
Analisis akan dilakukan dengan penggunaan instrument, iaitu “Vernier Caliper”. Proses
pengukuran in akan dijalankan pada beberapa kondisi dengan suhu dan kelembapan dan
berlainan. Data yang didapati kemudian akan di analisis menggunakan perisian computer
untuk menentukan sama ada suhu dan kelembapan yang mempengaruhi ketepatan
instrumen pengukuran masih lagi berada di dalam julat ketidakpastian suhu dan
kelembapan yang dibenarkan atau tidak.
ii
TABLE OF CONTENT
Abstract
i
Abstrak
ii
Table of Content
iii
List of Tables
vii
List of Figures
ix
List of Abbreviations
xi
1.
1
INTRODUCTION
1.1
Introduction
1
1.2
Problem Statement
11
1.3
Objective of the Project
11
1.4
Scope of the Project
11
2.
LITERATURE REVIEW
12
2.1
Introduction
12
2.2
Classifying Sources of Uncertainty
13
2.3
Thermal Expansion
15
2.4
Others Relevant Journal
16
iii
3.
METHODOLOGY
23
3.1
Introduction
23
3.2
Design of Experiment
25
3.3
4.
3.2.1 Select Problem
25
3.2.2 Determining Dependent Variables
25
3.2.3 Determining Independent Variables
26
3.2.4 Determining the Number of Levels of Independent Variables
26
3.2.5 Determining the Possible Combinations
27
3.2.6 Determining the Number of Observations
27
3.2.7 Data Verification
28
Methodology
29
3.3.1 Finding Condition
29
3.3.2 Measuring Instrument
32
3.3.3 Data Analysis
33
3.3.4 Expected Results
35
RESULT
36
4.1
Introduction
36
4.2
Result
39
4.3
Analysis
40
4.3.1 Temperature Analysis
41
iv
4.3.2 Humidity Analysis
5.
47
DISCUSSION
53
5.1
Introduction
53
5.2
Observation Analysis
54
5.3
Data Analysis
55
5.4
5.3.1
Correlation Analysis
55
5.3.2
Simple Linear Regression Analysis
57
5.3.2.1
Temperature Linear Regression Analysis
58
5.3.2.2
Humidity Linear Regression Analysis
59
Response Time
61
6.
CONCLUSION
62
6.1
Conclusion
62
REFERENCES
64
APPENDICES
Appendix A
-
PSM 1 Gantt Chart
Appendix B
-
PSM 2 Gantt Chart
Appendix C
-
Psychometric Chart
v
Appendix D
-
Pcychometric Chart Details
Appendix E
-
Raw Data Table
Appendix F
-
Raw Analysis of Temperature Parameter
Appendix G
-
Raw Analysis of Humidity Parameter
vi
LIST OF TABLES
Table 2.1
Uncertainty sources in NIST dimensional calibrations
14
Table 3.1
Table of Data Collected
33
Table 4.1
Result of measurement in day 1
39
Table 4.2
Value of r and their strength
41
Table 4.3
Analysis of Correlations between Standard Deviation and
Temperature (near air-cond)
Table 4.4
Table 4.5
Table 4.6
42
Analysis of Correlations between Standard Deviation and
Temperature (far from air-cond)
43
Analysis of Correlations between Standard Deviation and
Temperature (Outside Metrology Lab)
44
Analysis of Correlations between Standard Deviation and
Temperature (all location)
Table 4.7
45
Analysis of Correlations between Standard Deviation and
Humidity (near an air-cond)
Table 4.8
48
Analysis of Correlations between Standard Deviation and
Humidity (far from air-cond)
Table 4.9
49
Analysis of Correlations between Standard Deviation and
Humidity (Outside Metrology Lab)
vii
50
Table 4.10
Analysis of Correlations between Standard Deviation and
Humidity (all location)
51
Table 5.1
Location correlation for temperature analysis
56
Table 5.2
Location correlation for humidity analysis
56
Table 5.3
Result of Hypothesis Testing for temperature and humidity
61
viii
LIST OF FIGURES
Figure 1.1
Amount of water in air at 100% relative humidity
7
Figure 3.1
Method Flow chart
24
Figure 3.2
Four main stages for Methodology
29
Figure 3.3
A sling psychrometer
30
Figure 3.4
The interior of a Stevenson showing motorized psychrometer
30
Figure 3.5
Flow of work in finding condition
31
Figure 3.6
Flow of work in preparing measuring instrument
32
Figure 3.7
Flow of work in measurement process
34
Figure 4.1
Near air-cond location
37
Figure 4.2
Far from air-cond location
37
Figure 4.3
Outside lab location
38
Figure 4.4
Graph Standard Deviation for near air-cond data versus
Temperature
Figure 4.5
42
Graph Standard Deviation for far from air-cond data versus
Temperature
Figure 4.6
43
Graph Standard Deviation for outside Metrology Lab data versus
Temperature
44
ix
Figure 4.7
Graph Standard Deviation data for all location versus
Temperature
Figure 4.8
45
Graph Standard Deviation data for near an air-cond versus
Humidity
Figure 4.9
48
Graph Standard Deviation for far from air-cond data versus
Humidity
Figure 4.10
Figure 4.11
49
Graph Standard Deviation for outside Metrology Lab data versus
Humidity
50
Graph Standard Deviation data for all location versus Humidity
51
x
LIST OF ABBREVIATIONS
UTEM
-
Universiti Teknikal Malaysia
FKP
-
Fakulti Kejuruteraan Pembuatan
VIM
-
The International Vocabulary of Basic and General Terms in
Metrology
NIST
-
National Institute of Standards Technology
CTE
-
Coefficient of Thermal Expansion
U.S
-
United States of America
B.S
-
British Standard
SPSS
-
Statistical Package for the Social Sciences
ANOVA
-
Analysis of Variables
NBS
-
National Bureau of Statistic
ISO
-
International Organization for Standardization
DC
-
Direct current
CMM
-
Coordinate measuring machine
FE
-
Finite element
NMR
-
Nuclear Magnetic Resonance
MOSFET
-
Metal-Oxide-Semiconductor Field-Effect Transistor
xi
CHAPTER 1
INTRODUCTION
1.1
Introduction
The International Vocabulary of Basic and General Terms in Metrology (VIM) define
measurement uncertainties as a parameter, associated with the result of measurement
that characterizes the dispersion of the values that could reasonably be attributed to the
measurand. It also defines measurand as a particular quantity subject to measurement. In
other words, uncertainty is a quantitative term that represents a range of values wherein
the true value is lie. Uncertainty and confidence is determined using statistical technique.
In practice, the uncertainties of the result may arise from many possible sources such as
reference standards and measurement equipment, measurement setup, measurement
process and environmental conditions: temperature; relative humidity.
In physics, temperature is a physical property of a system that underlies the common
notions of hot and cold; something that feels hotter generally has the higher temperature.
Temperature is one of the principal parameters of thermodynamics. If no heat flow
occurs between two objects, the objects have the same temperature; otherwise heat flows
from the hotter object to the colder object. This is the content of the zeroth law of
thermodynamics. On the microscopic scale, temperature can be defined as the average
energy in each degree of freedom in the particles in a system. Because temperature is a
statistical property, a system must contain a few particles for the question as to its
1
temperature to make any sense. For a solid, this energy is found in the vibrations of its
atoms about their equilibrium positions. In an ideal monatomic gas, energy is found in
the translational motions of the particles; with molecular gases, vibrational and rotational
motions also provide thermodynamic degrees of freedom.
Molecules, such as O2, have more degrees of freedom than single atoms: they can have
rotational and vibrational motions as well as translational motion. An increase in
temperature will cause the average translational energy to increase. It will also cause the
energy associated with vibrational and rotational modes to increase. Thus a diatomic gas,
with extra degrees of freedom rotation and vibration, will require a higher energy input
to change the temperature by a certain amount, i.e. it will have a higher heat capacity
than a monatomic gas.
The process of cooling involves removing energy from a system. When there is no more
energy able to be removed, the system is said to be at absolute zero, which is the point
on the thermodynamic (absolute) temperature scale where all kinetic motion in the
particles comprising matter ceases and they are at complete rest in the “classic” (nonquantum mechanical) sense. By definition, absolute zero is a temperature of precisely
0 kelvins (−273.15 °C or −459.68 °F).
The formal properties of temperature follow from its mathematical definition (see below
for the zeroth law definition and the second law definition) and are studied in
thermodynamics and statistical mechanics. Contrary to other thermodynamic quantities
such as entropy and heat, whose microscopic definitions are valid even far away from
thermodynamic equilibrium, temperature being an average energy per particle can only
be defined at thermodynamic equilibrium, or at least local thermodynamic equilibrium.
As a system receives heat, its temperature rises; similarly, a loss of heat from the system
tends to decrease its temperature.
When two systems are at the same temperature, no heat transfer occurs between them.
When a temperature difference does exist, heat will tend to move from the highertemperature system to the lower-temperature system, until they are at thermal
2
equilibrium. This heat transfer may occur via conduction, convection or radiation or
combinations of them (see heat for additional discussion of the various mechanisms of
heat transfer) and some ions may vary. Temperature is also related to the amount of
internal energy and enthalpy of a system: the higher the temperature of a system, the
higher its internal energy and enthalpy. Temperature is an intensive property of a system,
meaning that it does not depend on the system size, the amount or type of material in the
system, the same as for the pressure and density. By contrast, mass, volume, and entropy
are extensive properties, and depend on the amount of material in the system.
Humidity is the amount of water vapor in the air. In daily language the term "humidity"
is normally taken to mean relative humidity. Relative humidity is defined as the ratio of
the partial pressure of water vapor in a parcel of air to the saturated vapor pressure of
water vapor at a prescribed temperature. Humidity may also be expressed as absolute
humidity and specific humidity. Relative humidity is an important metric used in
forecasting weather. Humidity indicates the likelihood of precipitation, dew, or fog.
High humidity makes people feel hotter outside in the summer because it reduces the
effectiveness of sweating to cool the body by reducing the evaporation of perspiration
from the skin. This effect is calculated in a heat index table.
Absolute humidity is the quantity of water in a particular volume of air. The most
common units are grams per cubic meter, although any mass unit and any volume unit
could be used. A pound per cubic foot is common in the U.S. and occasionally even
other units mixing the Imperial and metric systems are used.
If all the water in one cubic meter of air were condensed into a container, the container
could be weighed to determine absolute humidity. The amount of vapor in that cube of
air is the absolute humidity of that cubic meter of air. More technically: the mass of
water vapor mw, per cubic meter of air, Va.
3
Absolute humidity ranges from 0 grams per cubic meter in dry air to 30 grams per cubic
meter (0.03 ounce per cubic foot) when the vapor is saturated at 30 °C. (See also
Absolute Humidity table)
The absolute humidity changes as air pressure changes. This is very inconvenient for
chemical engineering calculations, e.g. for dryers, where temperature can vary
considerably. As a result, absolute humidity is generally defined in chemical engineering
as mass of water vapor per unit mass of dry air, also known as the mass mixing ratio (see
below), which is much more rigorous for heat and mass balance calculations. Mass of
water per unit volume as in the equation above would then be defined as volumetric
humidity. Because of the potential confusion, British Standard BS 1339 (revised 2002)
suggests avoiding the term "absolute humidity". Units should always be carefully
checked. Most humidity charts are given in g/kg or kg/kg, but any mass units may be
used. The engineering of physical and thermodynamic properties of gas-vapor mixtures
is named Psychometrics
Mixing or humidity ratio is expressed as a ratio of water vapor mass, mw, per kilogram
of dry air, md, at a given pressure. The colloquial term moisture content is also used
instead of mixing/humidity ratio. Humidity ratio is a standard axis on psychometrics
charts, and is a useful parameter in psychometrics calculations because it does not
change with temperature except when the air cools below dew point.
That ratio can be given as:
Mixing ratio can also be expressed with the partial pressure of water vapor:
4
Where
δ = 0.62197 is the ratio of molecular weights of water vapor and dry air
pw = partial pressure of water vapor in moist air
pa = atmospheric pressure of moist air
Technically speaking, this is a dimensionless quantity as it is the mass of water vapor to
the mass of dry air. So it is expressed as Kg/Kg. However, the mass of water vapor is
much less than the value of the mass of dry air and most commonly meteorologists use
g/Kg which is 10 − 3 Kg/Kg.
Relative humidity is defined as the ratio of the partial pressure of water vapor (in a
gaseous mixture of air and water vapor) to the saturated vapor pressure of water at a
given temperature. Relative humidity is expressed as a percentage and is calculated in
the following manner:
Where:
- is the partial pressure of water vapor in the gas mixture;
- is the saturation vapor pressure of water at the temperature of the gas mixture
- is the relative humidity of the gas mixture being considered.
Relative humidity is often mentioned in weather forecasts and reports, as it is an
indicator of the likelihood of precipitation, dew, or fog. In hot summer weather, it also
increases the apparent temperature to humans (and other animals) by hindering the
evaporation of perspiration from the skin as the relative humidity rises.
5
Specific humidity is the ratio of water vapor to air (including water vapor and dry air) in
a particular mass. Specific humidity ratio is expressed as a ratio of kilograms of water
vapor, mw, per kilogram of air (including water vapor), mt.
That ratio can be shown as:
Specific humidity is related to mixing ratio (and vice versa) by:
Humidity is a measure of the amount of water vapor dissolved in the air, not including
any liquid water or ice falling through the air. For clouds to form, and rain to start, the
air doesn't have to reach 100% relative humidity at the Earth's surface, but only where
the clouds and rain drops form. This normally occurs when the air rises and cools.
Typically, rain falls into air with less than saturated humidity. Some water from the rain
may evaporate into the air as it falls, increasing the humidity, but not necessarily enough
to raise the humidity to 100%. It is even possible for rain falling through warm, humid
air to be cold enough to lower the air temperature to the dew point, thus condensing
water vapor out of the air. Although that would indeed raise the relative humidity to
100%, the water lost from the air (as dew) would also lower the absolute humidity.
Associated with relative humidity is dew point (If the dew point is below freezing, it is
referred to as the frost point). Dew point is the temperature at which water vapor
saturates from an air mass into liquid or solid usually forming rain, snow, frost, or dew.
Dew point normally occurs when a mass of air has a relative humidity of 100%. This
happens in the atmosphere as a result of cooling through a number of different processes.
6
Figure 1.1: Amount of water in air at 100% relative humidity across a range of temperature
Software that used for this project is SPSS Statistics. This software used for statistical
analysis. The purpose for using this software is to analyze the data taken and find the
correlation between temperature and relative humidity with measurement. SPSS
(originally, Statistical Package for the Social Sciences) was released in its first version in
1968 after being developed by Norman H. Nie and C. Hadlai Hull. Norman Nie was then
a political science postgraduate at Stanford University, and now Research Professor in
the Department of Political Science at Stanford and Professor Emeritus of Political
Science at the University of Chicago. SPSS is among the most widely used programs for
statistical analysis in social science. It is used by market researchers, health researchers,
survey companies, government, education researchers, marketing organizations and
others. The original SPSS manual (Nie, Bent & Hull, 1970) has been described as
'Sociology's most influential book'. In addition to statistical analysis, data management
7
(case selection, file reshaping, creating derived data) and data documentation (a
metadata dictionary is stored in the datafile) are features of the base software.
Statistics included in the base software:
(a) Descriptive statistics: Cross tabulation, Frequencies, Descriptives, Explore,
Descriptive Ratio Statistics
(b) Bivariate statistics: Means, t-test, ANOVA, Correlation (bivariate, partial,
distances), Nonparametric tests
(c) Prediction for numerical outcomes: Linear regression
(d) Prediction for identifying groups: Factor analysis, cluster analysis (two-step, Kmeans, hierarchical), Discriminant
The many features of SPSS are accessible via pull-down menus or can be programmed
with a proprietary 4GL command syntax language. Command syntax programming has
the benefits of reproducibility; simplifying repetitive tasks; and handling complex data
manipulations and analyses. Additionally, some complex applications can only be
programmed in syntax and is not accessible through the menu structure. The pull-down
menu interface also generates command syntax, this can be displayed in the output
though the default settings have to be changed to make the syntax visible to the user; or
can be paste into a syntax file using the "paste" button present in each menu. Programs
can be run interactively or unattended using the supplied Production Job Facility.
Additionally a "macro" language can be used to write command language subroutines
and a Python programmability extension can access the information in the data
dictionary and data and dynamically build command syntax programs. The Python
programmability extension, introduced in SPSS 14, replaced the less functional SAX
Basic "scripts" for most purposes, although SaxBasic remains available. In addition, the
Python extension allows SPSS to run any of the statistics in the free software package R.
From version 14 onwards SPSS can be driven externally by a Python or a VB.NET
program using supplied "plug-ins".
8
SPSS places constraints on internal file structure, data types, data processing and
matching files, which together considerably simplify programming. SPSS datasets have
a 2-dimensional table structure where the rows typically represent cases (such as
individuals or households) and the columns represent measurements (such as age, sex or
household income). Only 2 data types are defined: numeric and text (or "string"). All
data processing occurs sequentially case-by-case through the file. Files can be matched
one-to-one and one-to-many, but not many-to-many.
The graphical user interface has two views which can be toggled by clicking on one of
the two tabs in the bottom left of the SPSS window. The 'Data View' shows a
spreadsheet view of the cases (rows) and variables (columns). Unlike spreadsheets, the
data cells can only contain numbers or text and formulas cannot be stored in these cells.
The 'Variable View' displays the metadata dictionary where each row represents a
variable and shows the variable name, variable label, value label(s), print width,
measurement type and a variety of other characteristics. Cells in both views can be
manually edited, defining the file structure and allowing data entry without using
command syntax. This may be sufficient for small datasets. Larger datasets such as
statistical surveys are more often created in data entry software, or entered during
computer-assisted personal interviewing, by scanning and using optical character
recognition and optical mark recognition software, or by direct capture from online
questionnaires. These datasets are then read into SPSS.
SPSS can read and write data from ASCII text files (including hierarchical files), other
statistics packages, spreadsheets and databases. SPSS can read and write to external
relational database tables via ODBC and SQL.
Statistical output is to a proprietary file format (*.spv file, supporting pivot tables) for
which, in addition to the in-package viewer, a stand-alone reader can be downloaded.
The proprietary output can be exported to text or Microsoft Word. Alternatively, output
can be captured as data (using the OMS command), as text, tab-delimited text, PDF,
9
STUDY OF THE EFFECT OF TEMPERATURE AND RELATIVE
HUMIDITY ON THE MEASUREMENT VARIANCE CONDUCTED
IN THE METROLOGY LAB, FKP
This report submitted in accordance with requirement of the Universiti Teknikal
Malaysia Melaka (UTeM) for the Bachelor Degree of Manufacturing Engineering
(Manufacturing Process) with Honours.
By
AMER ADLI BIN MOHD DZAINUDIN
FACULTY OF MANUFACTURING ENGINEERING
MEI 2010
UTeM Library (Pind.1/2005)
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
BORANG PENGESAHAN STATUS TESIS*
JUDUL: Study of the Effect of Temperature and Relative Humidity on the
Measurement Variance Conducted in Metrology Lab, FKP
SESI PENGAJIAN: 2009-2010
Saya
_____________________________________________________________________
AMER ADLI BIN MOHD DZAINUDIN
mengaku membenarkan tesis (PSM/Sarjana/Doktor Falsafah) ini disimpan di
Perpustakaan Universiti Teknikal Malaysia Melaka (UTeM) dengan syarat-syarat
kegunaan seperti berikut:
1. Tesis adalah hak milik Universiti Teknikal Malaysia Melaka.
2. Perpustakaan Universiti Teknikal Malaysia Melaka dibenarkan membuat salinan
untuk tujuan pengajian sahaja.
3. Perpustakaan dibenarkan membuat salinan tesis ini sebagai bahan pertukaran
antara institusi pengajian tinggi.
4. **Sila tandakan (√)
SULIT
TERHAD
√
(Mengandungi maklumat yang berdarjah keselamatan
atau kepentingan Malaysia yang termaktub di dalam
AKTA RAHSIA RASMI 1972)
(Mengandungi maklumat TERHAD yang telah ditentukan
oleh organisasi/badan di mana penyelidikan dijalankan)
TIDAK TERHAD
Disahkan oleh:
(TANDATANGAN PENULIS)
(TANDATANGAN PENYELIA)
Alamat Tetap:
Blok 26-7-6, Taman Desa Tasek,
Sungai Besi, 57100
Kuala Lumpur
Cop Rasmi:
Tarikh: _______________________
25 Mei 2010
25 Mei 2010
Tarikh: _______________________
* Tesis dimaksudkan sebagai tesis bagi Ijazah Doktor Falsafah dan Sarjana secara penyelidikan, atau
disertasi bagi pengajian secara kerja kursus dan penyelidikan, atau Laporan Projek Sarjana Muda (PSM).
** Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi berkenaan
dengan menyatakan sekali sebab dan tempoh tesis ini perlu dikelaskan sebagai SULIT atau TERHAD.
DECLARATION
I hereby declare that this report entitled “STUDY OF THE EFFECT OF
TEMPERATURE AND RELATIVE HUMIDITY ON THE MEASUREMENT
VARIANCE CONDUCTED IN THE METROLOGY LAB, FKP” is the result of my
own research except as cited in the references.
Signature
:
Author’s name
:
Amer Adli bin Mohd Dzainudin
Date
:
25 May 2010
APPROVAL
This report is submitted to the Faculty of Manufacturing Engineering of UTem as a
partial fulfillment of the requirements for the degree of Bachelor of Manufacturing
Engineering (Manufacturing Process). The members of the supervisory committee are as
follow:
Mohd Fairuz Bin Dimin
(PSM Supervisor)
25 May 2010
ABSTRACT
The aim of this project is to study the effect of temperature and relative humidity of the
environment on the measurement variance and error conducted in the Metrology Lab,
Faculty of Manufacturing Engineering, UTeM. The scope of this project is to analyze
the statistical error and correlation between the temperature and relative humidity
variances of the environment against the accuracy of selected metrology lab apparatus
using statistical software SPSS. Measurements are taken using vernier calliper in
different temperature and relative humidity conditions at several different locations and
hours of the day. The results obtained were compared against other type of variances
such as the repeatability and equipment error for their type and magnitude of correlations
and errors
i
ABSTRAK
Projek ini adalah untuk menganalisis kesan suhu dan juga kelembapan ke atas varian
pengukuran yang diambil di dalam Makmal Metrologi di Fakulti Kejuruteraan
Pembuatan. Tujuan projek ini dibangunkan adalah untuk menentukan pengaruh suhu dan
kelembapan ke atas ketepatan instrumen pengukuran. Disebabkan Makmal Metrologi
telah berpindah tempat, projek ini telah dibangunkan untuk menganalisis dan meneliti
adakah suhu dan kelembapan di dalam Makmal Metrologi baru yang mungkin
disebabkan penyaman udara dan alam sekitarnya akan memberikan kesan yang besar ke
atas ketepatan instrumen sehingga membolehkan pertikaian berlaku ke atas pengukuran.
Analisis akan dilakukan dengan penggunaan instrument, iaitu “Vernier Caliper”. Proses
pengukuran in akan dijalankan pada beberapa kondisi dengan suhu dan kelembapan dan
berlainan. Data yang didapati kemudian akan di analisis menggunakan perisian computer
untuk menentukan sama ada suhu dan kelembapan yang mempengaruhi ketepatan
instrumen pengukuran masih lagi berada di dalam julat ketidakpastian suhu dan
kelembapan yang dibenarkan atau tidak.
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TABLE OF CONTENT
Abstract
i
Abstrak
ii
Table of Content
iii
List of Tables
vii
List of Figures
ix
List of Abbreviations
xi
1.
1
INTRODUCTION
1.1
Introduction
1
1.2
Problem Statement
11
1.3
Objective of the Project
11
1.4
Scope of the Project
11
2.
LITERATURE REVIEW
12
2.1
Introduction
12
2.2
Classifying Sources of Uncertainty
13
2.3
Thermal Expansion
15
2.4
Others Relevant Journal
16
iii
3.
METHODOLOGY
23
3.1
Introduction
23
3.2
Design of Experiment
25
3.3
4.
3.2.1 Select Problem
25
3.2.2 Determining Dependent Variables
25
3.2.3 Determining Independent Variables
26
3.2.4 Determining the Number of Levels of Independent Variables
26
3.2.5 Determining the Possible Combinations
27
3.2.6 Determining the Number of Observations
27
3.2.7 Data Verification
28
Methodology
29
3.3.1 Finding Condition
29
3.3.2 Measuring Instrument
32
3.3.3 Data Analysis
33
3.3.4 Expected Results
35
RESULT
36
4.1
Introduction
36
4.2
Result
39
4.3
Analysis
40
4.3.1 Temperature Analysis
41
iv
4.3.2 Humidity Analysis
5.
47
DISCUSSION
53
5.1
Introduction
53
5.2
Observation Analysis
54
5.3
Data Analysis
55
5.4
5.3.1
Correlation Analysis
55
5.3.2
Simple Linear Regression Analysis
57
5.3.2.1
Temperature Linear Regression Analysis
58
5.3.2.2
Humidity Linear Regression Analysis
59
Response Time
61
6.
CONCLUSION
62
6.1
Conclusion
62
REFERENCES
64
APPENDICES
Appendix A
-
PSM 1 Gantt Chart
Appendix B
-
PSM 2 Gantt Chart
Appendix C
-
Psychometric Chart
v
Appendix D
-
Pcychometric Chart Details
Appendix E
-
Raw Data Table
Appendix F
-
Raw Analysis of Temperature Parameter
Appendix G
-
Raw Analysis of Humidity Parameter
vi
LIST OF TABLES
Table 2.1
Uncertainty sources in NIST dimensional calibrations
14
Table 3.1
Table of Data Collected
33
Table 4.1
Result of measurement in day 1
39
Table 4.2
Value of r and their strength
41
Table 4.3
Analysis of Correlations between Standard Deviation and
Temperature (near air-cond)
Table 4.4
Table 4.5
Table 4.6
42
Analysis of Correlations between Standard Deviation and
Temperature (far from air-cond)
43
Analysis of Correlations between Standard Deviation and
Temperature (Outside Metrology Lab)
44
Analysis of Correlations between Standard Deviation and
Temperature (all location)
Table 4.7
45
Analysis of Correlations between Standard Deviation and
Humidity (near an air-cond)
Table 4.8
48
Analysis of Correlations between Standard Deviation and
Humidity (far from air-cond)
Table 4.9
49
Analysis of Correlations between Standard Deviation and
Humidity (Outside Metrology Lab)
vii
50
Table 4.10
Analysis of Correlations between Standard Deviation and
Humidity (all location)
51
Table 5.1
Location correlation for temperature analysis
56
Table 5.2
Location correlation for humidity analysis
56
Table 5.3
Result of Hypothesis Testing for temperature and humidity
61
viii
LIST OF FIGURES
Figure 1.1
Amount of water in air at 100% relative humidity
7
Figure 3.1
Method Flow chart
24
Figure 3.2
Four main stages for Methodology
29
Figure 3.3
A sling psychrometer
30
Figure 3.4
The interior of a Stevenson showing motorized psychrometer
30
Figure 3.5
Flow of work in finding condition
31
Figure 3.6
Flow of work in preparing measuring instrument
32
Figure 3.7
Flow of work in measurement process
34
Figure 4.1
Near air-cond location
37
Figure 4.2
Far from air-cond location
37
Figure 4.3
Outside lab location
38
Figure 4.4
Graph Standard Deviation for near air-cond data versus
Temperature
Figure 4.5
42
Graph Standard Deviation for far from air-cond data versus
Temperature
Figure 4.6
43
Graph Standard Deviation for outside Metrology Lab data versus
Temperature
44
ix
Figure 4.7
Graph Standard Deviation data for all location versus
Temperature
Figure 4.8
45
Graph Standard Deviation data for near an air-cond versus
Humidity
Figure 4.9
48
Graph Standard Deviation for far from air-cond data versus
Humidity
Figure 4.10
Figure 4.11
49
Graph Standard Deviation for outside Metrology Lab data versus
Humidity
50
Graph Standard Deviation data for all location versus Humidity
51
x
LIST OF ABBREVIATIONS
UTEM
-
Universiti Teknikal Malaysia
FKP
-
Fakulti Kejuruteraan Pembuatan
VIM
-
The International Vocabulary of Basic and General Terms in
Metrology
NIST
-
National Institute of Standards Technology
CTE
-
Coefficient of Thermal Expansion
U.S
-
United States of America
B.S
-
British Standard
SPSS
-
Statistical Package for the Social Sciences
ANOVA
-
Analysis of Variables
NBS
-
National Bureau of Statistic
ISO
-
International Organization for Standardization
DC
-
Direct current
CMM
-
Coordinate measuring machine
FE
-
Finite element
NMR
-
Nuclear Magnetic Resonance
MOSFET
-
Metal-Oxide-Semiconductor Field-Effect Transistor
xi
CHAPTER 1
INTRODUCTION
1.1
Introduction
The International Vocabulary of Basic and General Terms in Metrology (VIM) define
measurement uncertainties as a parameter, associated with the result of measurement
that characterizes the dispersion of the values that could reasonably be attributed to the
measurand. It also defines measurand as a particular quantity subject to measurement. In
other words, uncertainty is a quantitative term that represents a range of values wherein
the true value is lie. Uncertainty and confidence is determined using statistical technique.
In practice, the uncertainties of the result may arise from many possible sources such as
reference standards and measurement equipment, measurement setup, measurement
process and environmental conditions: temperature; relative humidity.
In physics, temperature is a physical property of a system that underlies the common
notions of hot and cold; something that feels hotter generally has the higher temperature.
Temperature is one of the principal parameters of thermodynamics. If no heat flow
occurs between two objects, the objects have the same temperature; otherwise heat flows
from the hotter object to the colder object. This is the content of the zeroth law of
thermodynamics. On the microscopic scale, temperature can be defined as the average
energy in each degree of freedom in the particles in a system. Because temperature is a
statistical property, a system must contain a few particles for the question as to its
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temperature to make any sense. For a solid, this energy is found in the vibrations of its
atoms about their equilibrium positions. In an ideal monatomic gas, energy is found in
the translational motions of the particles; with molecular gases, vibrational and rotational
motions also provide thermodynamic degrees of freedom.
Molecules, such as O2, have more degrees of freedom than single atoms: they can have
rotational and vibrational motions as well as translational motion. An increase in
temperature will cause the average translational energy to increase. It will also cause the
energy associated with vibrational and rotational modes to increase. Thus a diatomic gas,
with extra degrees of freedom rotation and vibration, will require a higher energy input
to change the temperature by a certain amount, i.e. it will have a higher heat capacity
than a monatomic gas.
The process of cooling involves removing energy from a system. When there is no more
energy able to be removed, the system is said to be at absolute zero, which is the point
on the thermodynamic (absolute) temperature scale where all kinetic motion in the
particles comprising matter ceases and they are at complete rest in the “classic” (nonquantum mechanical) sense. By definition, absolute zero is a temperature of precisely
0 kelvins (−273.15 °C or −459.68 °F).
The formal properties of temperature follow from its mathematical definition (see below
for the zeroth law definition and the second law definition) and are studied in
thermodynamics and statistical mechanics. Contrary to other thermodynamic quantities
such as entropy and heat, whose microscopic definitions are valid even far away from
thermodynamic equilibrium, temperature being an average energy per particle can only
be defined at thermodynamic equilibrium, or at least local thermodynamic equilibrium.
As a system receives heat, its temperature rises; similarly, a loss of heat from the system
tends to decrease its temperature.
When two systems are at the same temperature, no heat transfer occurs between them.
When a temperature difference does exist, heat will tend to move from the highertemperature system to the lower-temperature system, until they are at thermal
2
equilibrium. This heat transfer may occur via conduction, convection or radiation or
combinations of them (see heat for additional discussion of the various mechanisms of
heat transfer) and some ions may vary. Temperature is also related to the amount of
internal energy and enthalpy of a system: the higher the temperature of a system, the
higher its internal energy and enthalpy. Temperature is an intensive property of a system,
meaning that it does not depend on the system size, the amount or type of material in the
system, the same as for the pressure and density. By contrast, mass, volume, and entropy
are extensive properties, and depend on the amount of material in the system.
Humidity is the amount of water vapor in the air. In daily language the term "humidity"
is normally taken to mean relative humidity. Relative humidity is defined as the ratio of
the partial pressure of water vapor in a parcel of air to the saturated vapor pressure of
water vapor at a prescribed temperature. Humidity may also be expressed as absolute
humidity and specific humidity. Relative humidity is an important metric used in
forecasting weather. Humidity indicates the likelihood of precipitation, dew, or fog.
High humidity makes people feel hotter outside in the summer because it reduces the
effectiveness of sweating to cool the body by reducing the evaporation of perspiration
from the skin. This effect is calculated in a heat index table.
Absolute humidity is the quantity of water in a particular volume of air. The most
common units are grams per cubic meter, although any mass unit and any volume unit
could be used. A pound per cubic foot is common in the U.S. and occasionally even
other units mixing the Imperial and metric systems are used.
If all the water in one cubic meter of air were condensed into a container, the container
could be weighed to determine absolute humidity. The amount of vapor in that cube of
air is the absolute humidity of that cubic meter of air. More technically: the mass of
water vapor mw, per cubic meter of air, Va.
3
Absolute humidity ranges from 0 grams per cubic meter in dry air to 30 grams per cubic
meter (0.03 ounce per cubic foot) when the vapor is saturated at 30 °C. (See also
Absolute Humidity table)
The absolute humidity changes as air pressure changes. This is very inconvenient for
chemical engineering calculations, e.g. for dryers, where temperature can vary
considerably. As a result, absolute humidity is generally defined in chemical engineering
as mass of water vapor per unit mass of dry air, also known as the mass mixing ratio (see
below), which is much more rigorous for heat and mass balance calculations. Mass of
water per unit volume as in the equation above would then be defined as volumetric
humidity. Because of the potential confusion, British Standard BS 1339 (revised 2002)
suggests avoiding the term "absolute humidity". Units should always be carefully
checked. Most humidity charts are given in g/kg or kg/kg, but any mass units may be
used. The engineering of physical and thermodynamic properties of gas-vapor mixtures
is named Psychometrics
Mixing or humidity ratio is expressed as a ratio of water vapor mass, mw, per kilogram
of dry air, md, at a given pressure. The colloquial term moisture content is also used
instead of mixing/humidity ratio. Humidity ratio is a standard axis on psychometrics
charts, and is a useful parameter in psychometrics calculations because it does not
change with temperature except when the air cools below dew point.
That ratio can be given as:
Mixing ratio can also be expressed with the partial pressure of water vapor:
4
Where
δ = 0.62197 is the ratio of molecular weights of water vapor and dry air
pw = partial pressure of water vapor in moist air
pa = atmospheric pressure of moist air
Technically speaking, this is a dimensionless quantity as it is the mass of water vapor to
the mass of dry air. So it is expressed as Kg/Kg. However, the mass of water vapor is
much less than the value of the mass of dry air and most commonly meteorologists use
g/Kg which is 10 − 3 Kg/Kg.
Relative humidity is defined as the ratio of the partial pressure of water vapor (in a
gaseous mixture of air and water vapor) to the saturated vapor pressure of water at a
given temperature. Relative humidity is expressed as a percentage and is calculated in
the following manner:
Where:
- is the partial pressure of water vapor in the gas mixture;
- is the saturation vapor pressure of water at the temperature of the gas mixture
- is the relative humidity of the gas mixture being considered.
Relative humidity is often mentioned in weather forecasts and reports, as it is an
indicator of the likelihood of precipitation, dew, or fog. In hot summer weather, it also
increases the apparent temperature to humans (and other animals) by hindering the
evaporation of perspiration from the skin as the relative humidity rises.
5
Specific humidity is the ratio of water vapor to air (including water vapor and dry air) in
a particular mass. Specific humidity ratio is expressed as a ratio of kilograms of water
vapor, mw, per kilogram of air (including water vapor), mt.
That ratio can be shown as:
Specific humidity is related to mixing ratio (and vice versa) by:
Humidity is a measure of the amount of water vapor dissolved in the air, not including
any liquid water or ice falling through the air. For clouds to form, and rain to start, the
air doesn't have to reach 100% relative humidity at the Earth's surface, but only where
the clouds and rain drops form. This normally occurs when the air rises and cools.
Typically, rain falls into air with less than saturated humidity. Some water from the rain
may evaporate into the air as it falls, increasing the humidity, but not necessarily enough
to raise the humidity to 100%. It is even possible for rain falling through warm, humid
air to be cold enough to lower the air temperature to the dew point, thus condensing
water vapor out of the air. Although that would indeed raise the relative humidity to
100%, the water lost from the air (as dew) would also lower the absolute humidity.
Associated with relative humidity is dew point (If the dew point is below freezing, it is
referred to as the frost point). Dew point is the temperature at which water vapor
saturates from an air mass into liquid or solid usually forming rain, snow, frost, or dew.
Dew point normally occurs when a mass of air has a relative humidity of 100%. This
happens in the atmosphere as a result of cooling through a number of different processes.
6
Figure 1.1: Amount of water in air at 100% relative humidity across a range of temperature
Software that used for this project is SPSS Statistics. This software used for statistical
analysis. The purpose for using this software is to analyze the data taken and find the
correlation between temperature and relative humidity with measurement. SPSS
(originally, Statistical Package for the Social Sciences) was released in its first version in
1968 after being developed by Norman H. Nie and C. Hadlai Hull. Norman Nie was then
a political science postgraduate at Stanford University, and now Research Professor in
the Department of Political Science at Stanford and Professor Emeritus of Political
Science at the University of Chicago. SPSS is among the most widely used programs for
statistical analysis in social science. It is used by market researchers, health researchers,
survey companies, government, education researchers, marketing organizations and
others. The original SPSS manual (Nie, Bent & Hull, 1970) has been described as
'Sociology's most influential book'. In addition to statistical analysis, data management
7
(case selection, file reshaping, creating derived data) and data documentation (a
metadata dictionary is stored in the datafile) are features of the base software.
Statistics included in the base software:
(a) Descriptive statistics: Cross tabulation, Frequencies, Descriptives, Explore,
Descriptive Ratio Statistics
(b) Bivariate statistics: Means, t-test, ANOVA, Correlation (bivariate, partial,
distances), Nonparametric tests
(c) Prediction for numerical outcomes: Linear regression
(d) Prediction for identifying groups: Factor analysis, cluster analysis (two-step, Kmeans, hierarchical), Discriminant
The many features of SPSS are accessible via pull-down menus or can be programmed
with a proprietary 4GL command syntax language. Command syntax programming has
the benefits of reproducibility; simplifying repetitive tasks; and handling complex data
manipulations and analyses. Additionally, some complex applications can only be
programmed in syntax and is not accessible through the menu structure. The pull-down
menu interface also generates command syntax, this can be displayed in the output
though the default settings have to be changed to make the syntax visible to the user; or
can be paste into a syntax file using the "paste" button present in each menu. Programs
can be run interactively or unattended using the supplied Production Job Facility.
Additionally a "macro" language can be used to write command language subroutines
and a Python programmability extension can access the information in the data
dictionary and data and dynamically build command syntax programs. The Python
programmability extension, introduced in SPSS 14, replaced the less functional SAX
Basic "scripts" for most purposes, although SaxBasic remains available. In addition, the
Python extension allows SPSS to run any of the statistics in the free software package R.
From version 14 onwards SPSS can be driven externally by a Python or a VB.NET
program using supplied "plug-ins".
8
SPSS places constraints on internal file structure, data types, data processing and
matching files, which together considerably simplify programming. SPSS datasets have
a 2-dimensional table structure where the rows typically represent cases (such as
individuals or households) and the columns represent measurements (such as age, sex or
household income). Only 2 data types are defined: numeric and text (or "string"). All
data processing occurs sequentially case-by-case through the file. Files can be matched
one-to-one and one-to-many, but not many-to-many.
The graphical user interface has two views which can be toggled by clicking on one of
the two tabs in the bottom left of the SPSS window. The 'Data View' shows a
spreadsheet view of the cases (rows) and variables (columns). Unlike spreadsheets, the
data cells can only contain numbers or text and formulas cannot be stored in these cells.
The 'Variable View' displays the metadata dictionary where each row represents a
variable and shows the variable name, variable label, value label(s), print width,
measurement type and a variety of other characteristics. Cells in both views can be
manually edited, defining the file structure and allowing data entry without using
command syntax. This may be sufficient for small datasets. Larger datasets such as
statistical surveys are more often created in data entry software, or entered during
computer-assisted personal interviewing, by scanning and using optical character
recognition and optical mark recognition software, or by direct capture from online
questionnaires. These datasets are then read into SPSS.
SPSS can read and write data from ASCII text files (including hierarchical files), other
statistics packages, spreadsheets and databases. SPSS can read and write to external
relational database tables via ODBC and SQL.
Statistical output is to a proprietary file format (*.spv file, supporting pivot tables) for
which, in addition to the in-package viewer, a stand-alone reader can be downloaded.
The proprietary output can be exported to text or Microsoft Word. Alternatively, output
can be captured as data (using the OMS command), as text, tab-delimited text, PDF,
9