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Matlab代写-MN5610
时间：2021-04-26
Academic Year 2020/21
1
ASSIGNMENT/COURSEWORK PROFORMA
Module code:
MN5610
Assessment title:
Advanced measurement systems and data
analysis
Module tutors:
Dr QingPing Yang
Main objectives of the assessment:
To enable students to demonstrate their in-depth knowledge of the principles of advanced
measurement system and data analysis and abilities to apply them to solve practical problems related
to advanced measurement system and data analysis.
Brief Description of the assessment:
Given some measurement tasks in an industrial context, you will need to complete two tasks: 1) to
identify and evaluate the suitable measurement solutions and 2) to design/develop the Matlab
program for the measurement data analysis. Both tasks are related to the key topics of the module.
Learning outcomes for the assessment (refer to the
appropriate module learning outcomes)
Students will be able to demonstrate the following:
• To gain systematic understanding of measurement errors
and uncertainties;
• To gain comprehensive understanding of the operating
principles of advanced sensors;
• To develop critical awareness of the range and capabilities
of advanced measuring instruments;
• To gain systematic understanding of linear/nonlinear models
for advanced data analysis.
• To perform error analysis and uncertainty evaluation of
practical measurement systems;
• To critically apply and evaluate different linear/nonlinear
models for advanced data analysis.
• To gain practical experience in the use of measuring
instruments;
• To develop critical awareness of the wide applications of
advanced measuring instruments.
Assessment criteria:
1. Understanding of measurement
systems (including sensors /
transducers, CMMs and surface
measuring machines) (40%)
2. Consideration of error sources
and calibration (10%)
3. Selection considerations (10%)
4. Understanding of nonlinear
least square method (15%)
5. Data analysis algorithm / coding
(15%)
6. Discussions and presentation
(10%)
Assessment method by which a student can demonstrate the learning
outcomes:
Identification and evaluation of the practical measuring systems for the
given measurement tasks. Design and implementation of algorithms and
Matlab programs for practical data analysis.
Weighting:
40% of module marks
Format of the assessment/coursework: (Guidelines on the expected format and length of
submission): *Note: full reports may not exceed 30 pages (including appendices)
Format is a formal written report including diagrams/code; calculations (with data; formula; workings
and assumptions) and discussion/ comments. Report to be written using Word in a 12 point font.
Typical length of report is 2000 words, comprising Title Page; Introduction; Task 1, Task 2,
Discussions, Conclusions and References.
Assessment date/submission deadline:
Please submit by Friday 30th April 2021 via WISEflow
Academic Year 2020/21
2
Indicative reading list:
• Fraden J. (2015). Handbook of modern sensors: physics, designs, and applications. Springer.
• Morris A S. (2012). Measurement and instrumentation: theory and application. Academic Press.
• Leach R. (2014). Fundamental principles of engineering nanometrology (Micro and Nano
Technologies). Elsevier.
• Flack D R and Hannaford J. (2005). Fundamental good practice in dimensional metrology”. NPL
Good practice guide No. 80 (free download from www.npl.co.uk).
• Bell S A. (2001). A beginner’s guide to uncertainty in measurement. NPL good practice guide No.
11 (free download from www.npl.co.uk).
• Barker R M, Cox M G, Forbes A B and Harris P M. (2007). Software Support for Metrology Best
Practice Guide No. 10: Discrete Modelling and Experimental Data Analysis. National Physical
Laboratory, Teddington.
• Higham D J, Higham N J. (2016). MATLAB Guide, Third edition. Philadelphia: Society for
Industrial and Applied Mathematics.
Other information
Use your student ids for anonymity (i.e. no student names on the assignment itself).
Academic Year 2020/21
3
ASSIGNMENT
Task 1:
Over recent years your company has been making substantial investments in automated machine tools, but
the inspection techniques have remained virtually unchanged. Most operators still use go/no-go gauges and
comparators etc. The emphasis has been on the detection of non-conforming components which are either
scrapped or reworked.
The largest overall linear dimension on any components manufactured by the company is 400 mm. The main
products are turned and milled components, with batch sizes in thousands and a substantial number of
features with tolerances in the region of 20 µm. Some precision spindles have tolerances of only 5 microns
with a batch size in the range 1 - 50. The surface roughness ranges from 12.5-0.025 µm.
The Managing Director has given you the task of preparing a report on the types of equipment which could
be employed to improve the measurement and product quality within the company. You should aim to
demonstrate comprehensive knowledge of relevant measuring techniques with sound / innovative solution
(paying attention to new and emerging technologies) and also skills for broader considerations, e.g.
implications due to batch sizes, component types and materials, commercial and industrial constraints. Your
report should cover the use of suitable sensors and transducers (e.g. LVDTs and/or optical encoders),
coordinate measuring machines (CMMs) and surface measuring machines, including their operating
principles, characteristics and performance. Since CMMs are complicated measuring machine, you should
discuss their common error sources and how you are going to calibrate them. You should also describe the
important surface parameters the company will likely use.
Task 2:
Your company has a manual 2D microscope which can measure the coordinates of 2D profiles. You have
been asked to develop a least squares algorithm in either Matlab or Excel to calculate the centre and radius
of circular features.
Describe briefly how measurement uncertainty is evaluated. Given the standard uncertainty of x and y
coordinates is 30 µm, calculate the uncertainties associated with the radius and centre coordinates. Describe
how to reduce the measurement uncertainties.
To test your algorithm, you can use the following measurement results, the XY coordinates recorded for a
ring gauge:
Academic Year 2020/21
4
Measurement Results (unit: mm)
X Y
84.047
73.303
44.041
4.015
-35.996
-65.250
-75.972
-65.241
-35.979
4.025
44.029
73.330
84.006
5.038
45.008
74.326
85.021
74.319
45.003
5.038
-34.997
-64.263
-74.969
-64.241
-34.975
5.006
学霸联盟
1
ASSIGNMENT/COURSEWORK PROFORMA
Module code:
MN5610
Assessment title:
Advanced measurement systems and data
analysis
Module tutors:
Dr QingPing Yang
Main objectives of the assessment:
To enable students to demonstrate their in-depth knowledge of the principles of advanced
measurement system and data analysis and abilities to apply them to solve practical problems related
to advanced measurement system and data analysis.
Brief Description of the assessment:
Given some measurement tasks in an industrial context, you will need to complete two tasks: 1) to
identify and evaluate the suitable measurement solutions and 2) to design/develop the Matlab
program for the measurement data analysis. Both tasks are related to the key topics of the module.
Learning outcomes for the assessment (refer to the
appropriate module learning outcomes)
Students will be able to demonstrate the following:
• To gain systematic understanding of measurement errors
and uncertainties;
• To gain comprehensive understanding of the operating
principles of advanced sensors;
• To develop critical awareness of the range and capabilities
of advanced measuring instruments;
• To gain systematic understanding of linear/nonlinear models
for advanced data analysis.
• To perform error analysis and uncertainty evaluation of
practical measurement systems;
• To critically apply and evaluate different linear/nonlinear
models for advanced data analysis.
• To gain practical experience in the use of measuring
instruments;
• To develop critical awareness of the wide applications of
advanced measuring instruments.
Assessment criteria:
1. Understanding of measurement
systems (including sensors /
transducers, CMMs and surface
measuring machines) (40%)
2. Consideration of error sources
and calibration (10%)
3. Selection considerations (10%)
4. Understanding of nonlinear
least square method (15%)
5. Data analysis algorithm / coding
(15%)
6. Discussions and presentation
(10%)
Assessment method by which a student can demonstrate the learning
outcomes:
Identification and evaluation of the practical measuring systems for the
given measurement tasks. Design and implementation of algorithms and
Matlab programs for practical data analysis.
Weighting:
40% of module marks
Format of the assessment/coursework: (Guidelines on the expected format and length of
submission): *Note: full reports may not exceed 30 pages (including appendices)
Format is a formal written report including diagrams/code; calculations (with data; formula; workings
and assumptions) and discussion/ comments. Report to be written using Word in a 12 point font.
Typical length of report is 2000 words, comprising Title Page; Introduction; Task 1, Task 2,
Discussions, Conclusions and References.
Assessment date/submission deadline:
Please submit by Friday 30th April 2021 via WISEflow
Academic Year 2020/21
2
Indicative reading list:
• Fraden J. (2015). Handbook of modern sensors: physics, designs, and applications. Springer.
• Morris A S. (2012). Measurement and instrumentation: theory and application. Academic Press.
• Leach R. (2014). Fundamental principles of engineering nanometrology (Micro and Nano
Technologies). Elsevier.
• Flack D R and Hannaford J. (2005). Fundamental good practice in dimensional metrology”. NPL
Good practice guide No. 80 (free download from www.npl.co.uk).
• Bell S A. (2001). A beginner’s guide to uncertainty in measurement. NPL good practice guide No.
11 (free download from www.npl.co.uk).
• Barker R M, Cox M G, Forbes A B and Harris P M. (2007). Software Support for Metrology Best
Practice Guide No. 10: Discrete Modelling and Experimental Data Analysis. National Physical
Laboratory, Teddington.
• Higham D J, Higham N J. (2016). MATLAB Guide, Third edition. Philadelphia: Society for
Industrial and Applied Mathematics.
Other information
Use your student ids for anonymity (i.e. no student names on the assignment itself).
Academic Year 2020/21
3
ASSIGNMENT
Task 1:
Over recent years your company has been making substantial investments in automated machine tools, but
the inspection techniques have remained virtually unchanged. Most operators still use go/no-go gauges and
comparators etc. The emphasis has been on the detection of non-conforming components which are either
scrapped or reworked.
The largest overall linear dimension on any components manufactured by the company is 400 mm. The main
products are turned and milled components, with batch sizes in thousands and a substantial number of
features with tolerances in the region of 20 µm. Some precision spindles have tolerances of only 5 microns
with a batch size in the range 1 - 50. The surface roughness ranges from 12.5-0.025 µm.
The Managing Director has given you the task of preparing a report on the types of equipment which could
be employed to improve the measurement and product quality within the company. You should aim to
demonstrate comprehensive knowledge of relevant measuring techniques with sound / innovative solution
(paying attention to new and emerging technologies) and also skills for broader considerations, e.g.
implications due to batch sizes, component types and materials, commercial and industrial constraints. Your
report should cover the use of suitable sensors and transducers (e.g. LVDTs and/or optical encoders),
coordinate measuring machines (CMMs) and surface measuring machines, including their operating
principles, characteristics and performance. Since CMMs are complicated measuring machine, you should
discuss their common error sources and how you are going to calibrate them. You should also describe the
important surface parameters the company will likely use.
Task 2:
Your company has a manual 2D microscope which can measure the coordinates of 2D profiles. You have
been asked to develop a least squares algorithm in either Matlab or Excel to calculate the centre and radius
of circular features.
Describe briefly how measurement uncertainty is evaluated. Given the standard uncertainty of x and y
coordinates is 30 µm, calculate the uncertainties associated with the radius and centre coordinates. Describe
how to reduce the measurement uncertainties.
To test your algorithm, you can use the following measurement results, the XY coordinates recorded for a
ring gauge:
Academic Year 2020/21
4
Measurement Results (unit: mm)
X Y
84.047
73.303
44.041
4.015
-35.996
-65.250
-75.972
-65.241
-35.979
4.025
44.029
73.330
84.006
5.038
45.008
74.326
85.021
74.319
45.003
5.038
-34.997
-64.263
-74.969
-64.241
-34.975
5.006
学霸联盟
