How I Plan to Collect Data for My Final Year Project
At this stage of my project, I am starting to think more seriously about how I will collect, analyze, and present my data, not just what machine or device I want to make. My project is about the design and development of a device for extracting oils from fibrous rhizomes, but I have already started narrowing the scope by focusing mainly on ginger and by using the existing workshop equipment where possible for the crushing or juice-extraction stage.
Because of that, I now realize that the quality of my project will depend heavily on the quality of the data I collect. I cannot just say that I tested ginger in a machine and it “worked” or “did not work.” I need to show clearly what I measured, why I measured it, how I measured it, whether the measurements are reliable, and what the results actually mean.
My Current Thinking About Data Collection
What I have understood is that all final year projects require data, even if the project is design-based. The data might come from experiments, measurements, simulations, interviews, or surveys. In my case, the main part of my work is becoming experimental, because I need to test how ginger behaves in the existing equipment and then determine how the oil can be extracted afterward.
So I have started thinking of my project in terms of variables and data.
A variable is the thing I am measuring.
The data is the collection of all the values I obtain for that variable.
For my project, some of the variables I may need to measure include:
the mass of ginger fed into the machine
the time taken to process the ginger
the mass or volume of juice extracted
the mass of the leftover fibre or pomace
the amount of oil recovered after distillation
the power consumed, if I can obtain or estimate it
observations such as clogging, splashing, ease of cleaning, safety, and consistency of output
This means my project will involve mainly quantitative data, since most of these are numerical measurements, but it may also include some qualitative data, since I also need to record observations about how the machine behaves and how practical the process is.
Why I Need to Be More Systematic
What really stood out to me from the guidance is that collecting data is not just about writing down numbers. I need to be able to explain the methodology behind the data. In other words, I need to document exactly how the data was obtained.
That means when I test the equipment, I should not do it casually. I need to treat it like a proper experiment.
So my thought process is now shifting from:
“Let me try the machine with ginger and see what happens.”
to:
“Let me define what I want to measure, control the conditions as much as possible, repeat the measurements, and collect data that I can actually defend in my report.”
That is a major shift in how I am viewing this stage of the project.
What Data I Need to Collect First
The first thing I need is a baseline test using ginger from the market. I need to understand how the existing workshop equipment performs before I decide what exactly my design contribution will be.
So when I do the test, I need to collect data such as:
the initial mass of ginger before feeding it into the machine
the total processing time
the amount of juice produced
the amount of remaining solid material
the condition of the ginger after crushing
whether the machine clogs
whether the output is uniform
whether the machine appears hygienic and practical to use
My reasoning is that these measurements will help me calculate performance measures such as:
throughput = mass of ginger processed ÷ time
juice yield = mass of juice ÷ mass of ginger input × 100%
pomace fraction = mass of leftover fibre ÷ mass of ginger input × 100%
material balance = comparing total input to total output and losses
These calculations will give me a proper way to describe how well the existing machine performs.
Why Repeatability Matters to Me
One of the most important ideas I took from the guidance is that I should not take one measurement and assume it is enough.
If I test the machine once and record one juice yield, that does not necessarily prove anything. I need to repeat the test under the same conditions and see whether I get similar results. If the values vary too much, then something is not being controlled properly.
So my thought process here is:
if I repeat the same test and the results are close to each other, then the process is repeatable
if the results vary widely, then I need to ask what uncontrolled factor is affecting the outcome
That could mean differences in:
ginger size
moisture content
feeding rate
operator handling
machine condition
measurement method
This is important because I do not just want results — I want results that are credible.
Accuracy and Validation
Another thing I now understand is that I need to think about accuracy, not just repeatability.
Repeatability tells me whether I get similar values each time.
Accuracy tells me whether the values are actually close to the true value or are reasonable.
In my case, I may not always know the exact “true” value for something like oil yield beforehand, but I can still validate my results by:
comparing them with values from the literature
comparing them with accepted ranges from previous studies
comparing the machine’s output with what I originally expected from my design targets
So if my results differ from literature values, I should not panic. Instead, I need to ask:
were my assumptions valid?
was my ginger different from the ginger used in the studies?
was the test setup different?
were there losses in the process?
is my methodology introducing error?
That way, even if my data does not match perfectly, I can still explain it properly.
The Existing Workshop Equipment
Another important part of my thinking now is that since I may use the existing workshop equipment for crushing the ginger, I need to document that equipment properly.
I do not necessarily need to reverse-engineer every single component, but I do need to record basic specifications such as:
machine type
motor rating
voltage
speed or rpm, if available
key dimensions
feed arrangement
output arrangement
any noticeable material or construction features
My reasoning is that if I am using this machine as part of my methodology, then I need to describe it clearly in the report. I also need those details if I want to compare the performance of the existing machine with the original design concept I had in mind.
My Original Design vs What I Am Actually Doing Now
This is another area where my thinking has become clearer.
Originally, I was considering a full design involving both:
a juicer/crushing system
an oil extraction or distillation system
But now that I may be using existing equipment for the crushing stage, I do not think it makes sense to spend too much time fully detailing old ideas that I am no longer building.
So my thought process is this:
I can still include the original juicer and extractor ideas as part of my concept development
I can show that I considered alternatives
I can explain why I moved toward using available equipment
but I should focus my detailed drawings, CAD, and bill of materials on the system or subsystem I am actually carrying forward
That means I need to be careful not to waste effort producing highly detailed SolidWorks models and BOMs for concepts that are no longer central to the project.
My Thinking About the Distillation Stage
The next major question for me is the oil extraction stage.
Once the ginger is crushed and I obtain juice or fibrous residue, I still need to recover the oil. This means I have to decide whether I will:
make a distillation setup in the workshop, or
buy or use an existing setup and use it as a test rig
At the moment, I think the better academic decision is to first see whether I can adapt or fabricate a simple bench-scale hydrodistillation setup using local workshop or lab resources. That would align better with the project’s engineering and design focus.
If I simply order a ready-made unit without good reason, it could weaken the design contribution of the project unless I clearly present it as a testing apparatus rather than my final engineered solution.
So my current thinking is:
first test the crushing stage
then decide whether the distillation device should be designed and fabricated, adapted, or used as an existing experimental setup
then align the report and drawings with that decision
How I Need to Present the Data
Another thing I am becoming more aware of is that collecting good data is only half the work. I also need to present it properly.
From the guidance, I understand that graphs are often more effective than tables, because they make trends easier to see. Tables are still useful, especially when I need to compare exact values, but graphs help show relationships more clearly.
So for my project, I may need to use:
line graphs to show how one parameter changes with another
bar charts to compare yields or output quantities
tables to present exact measurements or machine specifications
annotated photographs to show the setup and test locations
I also need to remember that every figure or table should actually say something. I should not include a graph just because it looks technical. If I include it, I should be able to explain:
what it shows
why it matters
what trend or conclusion I am drawing from it
That means every figure in my report should have at least one clear paragraph discussing it.
How I Should Interpret My Results
When I finally have the data, I cannot stop at presenting numbers. I have to interpret them.
So I need to ask questions like:
did the existing machine perform well enough for my purposes?
what operating conditions gave the best results?
what problems appeared during testing?
how do my results compare with literature?
did I meet the objectives I originally set?
if not, why not?
what are the limitations of my work?
what future improvements would make the system better?
This is important because the project is not just about getting a result. It is about showing that I understand what the result means.
What This Means for Me Going Forward
Right now, I think my next step is clear.
I need to treat the workshop test with ginger as a structured baseline experiment, not just an informal trial. I need to measure carefully, repeat measurements where possible, document the equipment, and calculate meaningful performance values.
From there, I can decide whether my final project contribution will focus more on:
improving or analyzing the crushing stage,
designing the oil extraction stage,
or integrating both stages in a realistic small-scale process
At this point, my biggest realization is that the project is becoming less about “just building something” and more about making sound engineering decisions based on good data.
That is the mindset I need going forward
