Focus Coffee Roaster

Documentation Study EP7
From Roast Curve to Cup Quality

The reference roast shown below demonstrates excellent profile repeatability, reaching nearly the same drop temperature and following a very similar roast trajectory. The question I'm trying to answer now is whether that level of repeatability also translates into the same roast development and, ultimately, the same sensory experience in the cup.

This roast serves as my current full automation reference profile. At first glance, the curves appear highly repeatable, with nearly identical end temperatures and a very similar roast trajectory. However, one observation continues to stand out. Reaching the same drop temperature and following the same profile does not automatically guarantee the same roast development or the same cup quality.

From an engineering perspective, repeatability begins with the control system. From a roasting perspective, repeatability is only validated when the coffee delivers the same sensory result in the cup. Those are two very different objectives, and bridging that gap is what this research is trying to understand.
For the current stage of development, my priority is not to achieve full automation at all costs. My focus is first to understand energy input, heat transfer, bean development, and sensory outcome before allowing automation to take over more of the roasting process.

One interesting observation during this stage of testing was how the automation itself behaved. During one of the full automation reference tests, Artisan automatically executed the DROP command earlier than expected after the target profile conditions had already been satisfied. This happened even without an active DROP alarm configured. At this point, I can't say with certainty whether the behavior was related to playback logic, profile completion, PID behavior, or another internal automation sequence. Rather than assuming a cause, I documented the behavior and added it to the list of items that require further investigation.
Observations like this remind me that understanding the software is just as important as understanding the hardware. Sometimes the expected behavior is obvious, and sometimes it raises new questions that deserve a closer look.

This is also why I don't recommend leaving a roaster completely unattended during automated roasting. Automation can execute commands consistently, but it cannot yet replace the judgment of an experienced roaster when unexpected behavior or process variations occur during a roast.

Every modification—whether in firmware, control logic, PID behavior, sensor filtering, software configuration, or roast strategy—is documented, tested, and evaluated before moving to the next iteration. That's simply how engineering works.
Ultimately, the graph is only one part of the equation. The real objective is to produce the same thermal history inside the bean, the same level of development, and ultimately the same cup profile. A roast can look nearly identical on screen yet still express different sweetness, acidity, body, balance, or finish during cupping.
This project is not about proving that automation is superior to manual roasting. It's about understanding how engineering and roasting science can work together to improve consistency while preserving the experience, intuition, and sensory judgment that remain essential to every roast.

The more I test, the more I realize that automation isn't the destination—understanding the coffee is. Every roast, whether successful or not, adds another piece to that understanding.
This research is still a work in progress, and I'm always open to comments, suggestions, constructive criticism, and technical discussions. If you notice something I may have overlooked or have a different approach, I'd genuinely appreciate hearing your perspective. Continuous learning and collaboration are part of what make this journey worthwhile.

Documentation Study – Live Monitoring Dashboard

Objective: Improve real-time monitoring of the Pilot X coffee roaster.

Observation: After two weeks of continuous roasting, a few areas were identified that needed closer monitoring during the roasting process.

Development: A live engineering dashboard was developed to display key roasting parameters in real time. The dashboard is now running live on an Android phone while receiving live data from the roaster through Artisan, allowing continuous monitoring during each roast.

Status: Ongoing research and development. The system is still being tested and refined to find the most practical and reliable solution. Future development may include a dedicated Android and iOS application.

Note: This project is still a work in progress. Every roast provides new data and new lessons for the next improvement.

Coffee starts long before the beans reach the grinder.
Coffee is mostly water.

Approximately:
• Brewed Coffee: 98–99% water
• Espresso: 90–94% water

One thing we learned from coffee competitions is that we taste the water before we brew the coffee.
Recently, my Aeropress started tasting a little earthy and unusually sour.

My first instinct was to look at the coffee, the recipe, water temperature and the brewing technique.
It turned out to be the water filter.
A simple reminder that sometimes we can overlook the ingredient that makes up most of the cup.
The same coffee can taste very different depending on the water, the grind size, and the brewing approach.

Before the brew.
Before the extraction.
Before the first sip.
It starts with water.

This was a good reminder for me that sometimes the smallest details make the biggest difference. it reminds me of going back to basics.

Sharing a small part of my daily coffee life.
The more we share what works—and what doesn't—the more we all learn together.

I was already preparing to tear down my machine. Turns out, it was just the water filter. 😅

Big shout shout mga champions 🏆
Ccto 📸

— Focus Coffee Roaster

Documentation Study — Learning Through One System

One of the more interesting observations from our recent development work involves the machine's thermal behavior and how energy is introduced into the roasting system.

Over the years, we have accumulated a significant amount of roast data, environmental observations, and operational notes from the same platform. This long-term documentation allowed us to observe not only roast outcomes, but also the subtle behavior of the machine itself.

A roasting system is more than a heat source and a drum.

It is a dynamic thermal environment where energy transfer, airflow, thermal mass, and operator decisions continuously interact with one another.

What appears to be a small engineering adjustment can influence how energy is distributed, how the system responds to changes in demand, and how thermal conditions behave throughout the roast.

Our recent work focused on studying these interactions more closely.

Initial observations suggest a smoother thermal response, reduced energy discontinuities, and a more stable transfer of heat throughout different roast phases. While these observations remain under continuous evaluation, they provide valuable insight into how the system behaves under varying operating conditions.

As always, observations alone are not conclusions.

Meaningful understanding requires documentation, repeatability, and validation over time.

What makes this project interesting is that none of it started with the intention of replacing the machine.

In fact, we intentionally stayed with the same platform.

We chose to resist the temptation of constantly searching for the next machine, the next technology, or the next shortcut.

Instead, we focused on understanding what we already had.

Because if we keep changing systems every time we encounter a question, we may never stay with one long enough to understand the answer.

The greatest challenge was not improving the machine.

The greatest challenge was improving ourselves.

Learning how to observe more carefully.

Learning how to document more accurately.

Learning how to interpret data with less assumption and more curiosity.

Learning how to ask better questions.

Over time, we realized that many of the limitations we initially attributed to equipment were actually limitations in our own understanding of the process.

As our understanding improved, our approach improved.

As our approach improved, the machine improved with it.

The goal was never to prove that one system is better than another.

The goal was to deepen our understanding of one system and continuously refine it through study, observation, and engineering discipline.

The machine remains a tool.

The real development happens in the people who take the time to understand it.

And perhaps that is the most important lesson this project continues to teach us.

Not to change faster.

But to learn deeper.