Automating My Morning Routine: How I Hacked My Coffee Machine with Raspberry Pi


I’ve always been a huge coffee lover. The smell of freshly brewed coffee in the morning is something I can’t start my day without. However, like many others, I often find myself pressed for time, especially in the mornings. The usual process of making coffee with my machine, while not overly time-consuming, involved pressing a few buttons and waiting around for about three minutes. It’s not a lot of time, but enough to make me think: "Can this be done faster?"


So, I decided to take on a fun and slightly geeky challenge to automate my coffee-making process using a Raspberry Pi. What started as a simple idea for a hackathon at work has now become a fully functional setup that I’ve been using for over six months!

The Coffee Machine Hack


Here’s how I turned my regular coffee machine into a remote-controlled, automated coffee-making station:

Step 1: Disassembling the Coffee Machine


The first step was to carefully disassemble my coffee machine. I needed to access the internal controls to see how I could automate the process. My plan was to control the power and coffee-making buttons electronically.

Step 2: Integrating Raspberry Pi Zero


I decided to use a Raspberry Pi Zero to control the coffee machine. The Raspberry Pi Zero is a versatile, tiny computer that I connected to the power button and the coffee-making button via relays. Relays are essentially electrically operated switches that allowed me to control these buttons programmatically.

Step 3: Automating the Cup Change


One of the trickiest parts was figuring out how to automate the process of changing cups under the coffee nozzle. I solved this by building a simple moving belt powered by a 5V motor. The motor, just like the buttons, was also controlled by the Raspberry Pi Zero. This way, when the coffee was ready, the belt would move the cup away.

Step 4: Coding the Automation


With the hardware in place and the Raspberry Pi connected to the local Wi-Fi network, it was time to write some code. Using a Python library, I quickly set up a local HTTP server on the Raspberry Pi. This server allows the Raspberry Pi to receive commands, such as when to start making coffee.

Step 5: Building the Android App


To make this setup even more user-friendly, I created a simple Android app. The app’s only function is to call the endpoint on the Raspberry Pi’s server, which triggers the coffee-making process. Now, I can start making my coffee from the comfort of my bed, knowing that by the time I get to the kitchen, my coffee will be ready and waiting.

Conclusion: A Successful (and Delicious) Hack


What started as a fun project for a hackathon has now become an integral part of my daily routine. This little project saved me a few precious minutes each morning, and I have to admit, there’s something incredibly satisfying about knowing that I automated the entire process.

Plus, it’s a great conversation starter! Not many people can say they control their coffee machine with their smartphone. While this project might seem over-the-top for some, it perfectly represents my love for coffee and tech.










Comments

Post a Comment

Popular posts from this blog

Skate Tricks Recognition Using Gyroscope

Image
In this article, we’ll describe how to recognize skateboard tricks using a gyroscope. This sensor is already present in most smartphones, but if you’re not familiar with it, here is a description . Before we dive in, a small disclaimer: This project originated as part of a Slido hackathon with the goal of demonstrating that it’s possible to recognize skateboard tricks using a gyroscope. For simplicity, we’ll focus on recognizing just two basic tricks and won’t consider the skater’s stance on the board. We’ll break down the problem into several smaller tasks: recording the trick, storing the data, describing the trick, analyzing it, and, finally, recognizing it. Recording the Trick Let’s start with trick recording. To obtain precise data, the device with the gyroscope needs to be attached directly to the skateboard. Place it on the bottom of the board to avoid interfering with the execution of the trick. The device should also be compact and lightweight to avoid altering the board’s cen...
Read more

Play table

Image
Using proximity sensors for playing midi tones combined with LED visualization Description The goal of this project was to create a table-sized device equipped with multiple proximity sensors capable of playing MIDI tones. Each sensor is accompanied by LEDs that indicate the distance of the user's hand above the table. This interactive table can be used by one or more people simultaneously. Hardware Setup I began with a cardboard prototype to test the sensors and the underlying logic. Afterward, I ordered a custom plottered sticker with a design which was painted with bare conductive paint. I drilled holes and connected the touchboard to seven Arduino Nanos, each controlling 13 LEDs. Additionally, I incorporated two potentiometers: one for volume control and another for changing the note setup. Programming The programming task was divided into two parts: Master Program (Touchboard): Reads values from the proximity sensors and sends messages to the slave Arduinos. Sl...
Read more

Counting dice and train wagons using computer vision

Image
Computer vision exercises with preprocessing Before starting my next project, I decided to work on some computer vision exercises. Each example is based on straightforward image preprocessing techniques. No complex data structures or machine learning are involved. Dice Detection I got this idea while browsing the net and became curious about how challenging it would be to write such a script. Here’s a step-by-step breakdown of the algorithm: Movement Detection : By comparing several frames with thresholds, we can determine if there is any movement in the frame. Adding a small time buffer after the movement stops gives us more accurate information. Removing the Background : Thresholding the grayscale frame helps to remove the background, leaving us with a binary image that highlights the objects. Cropping the Objects : Using contours, we can detect and isolate the objects by cropping them. Detecting Dots : By inverting the image, the dots on the dice become more distinguish...
Read more