# Data-Driven Manufacturing for Smart Factory - Savonia AMK

> Data-Driven Manufacturing is one of the learning modules Savonia University of Applied Sciences is responsible for developing in the Virtual Learning Environment for Smart Factory (VLEFACT) project funded by Erasmus Plus.

## Project Overview
Data-Driven Manufacturing is a learning module developed by Savonia University of Applied Sciences as part of the Erasmus Plus-funded Virtual Learning Environment for Smart Factory (VLEFACT) project. The module piloted four distinct use cases, which were tested by approximately 30 students from four educational institutions:
* Savonia University of Applied Sciences (Finland)
* Yläsavon Vocational School, Iisalmi (Finland)
* Mercantec Vocational School (Denmark)
* Carl-Benz-Schule, Gaggenau (Germany)

## Energy Management with Elseta
This use case involved deploying Elseta’s energy metering system in a smart factory setting. Elseta’s technology utilizes smart metering and analytics to monitor energy usage in real-time. By identifying high-consumption areas and developing predictive models, the project aimed to reduce costs and improve energy efficiency, supporting the broader goals of Industry 4.0.

## IoT-Based Energy Measurement
This module focused on measuring electrical energy using the Carlo Gavazzi EM24 energy analyzer and a UWPA gateway. 
* **Data Transmission:** Data was transmitted via Digita’s LoRaWAN network to IoT platforms.
* **Platforms:** The project utilized Azure IoT Central and ThingsBoard.
* **Implementation:** The process involved connecting the energy meter to the gateway, developing uplink converter code, and creating custom dashboards to visualize metrics such as voltage and amperage over time.

## Manufacturing Simulation with Visual Components
Visual Components, a 3D simulation software, was used to implement LEAN manufacturing principles. The project created two factory models of a pressing line to compare efficiency:
* **Model 1:** Human labor in the initial process and KUKA robots in the latter stage.
* **Model 2:** KUKA robots in both stages with humans focused on quality control.
The second model proved more efficient, demonstrating how real-time statistics from simulation software can optimize factory layouts and reduce waste.

## Digital Twins and Siemens Mindsphere
This study explored Siemens Mindsphere, a cloud-based system for connecting physical infrastructure with digital data. 
* **Process Simulation:** A water supply process (tank, motor, sensors, valves) was simulated to study data acquisition.
* **Digital Twin Development:** Data from Mindsphere was used to create a digital twin of a PID controller. Using the Ziegler-Nichols tuning method and MATLAB Simulink, the team modeled the process to test and adjust parameters before physical implementation, effectively saving resources and time.

## Authors and Affiliation
* **Md. Sajib M Pramanic**, IoT student, Savonia UAS (Md.Sajib.Pramanic@edu.savonia.fi)
* **Bishwatma Khanal**, IoT student, Savonia UAS (Bishwatma.Khanal@edu.savonia.fi)
* **Md. Z Khan**, IoT student, Savonia UAS (Md.Khan@edu.savonia.fi)
* **Stanislav Kolosov**, IoT student, Savonia UAS (Stanislav.Kolosov@edu.savonia.fi)
* **Shahil Sharma**, IoT student, Savonia UAS (Shahil.Sharma@edu.savonia.fi)
* **Suubi Lubaale**, IoT student, Savonia UAS (Suubi.Lubaale@edu.savonia.fi)
* **Asmita Thapa Magar**, IoT student, Savonia UAS (Asmita.ThapaMagar@edu.savonia.fi)
* **Tahsina F Tasmi**, IoT student, Savonia UAS (Tahsina.Tasmi@edu.savonia.fi)
* **Pasi Lepistö**, Senior Lecturer, Savonia UAS (Pasi.Lepisto@savonia.fi)
* **Arto Toppinen**, Principal Lecturer, Savonia UAS (Arto.Toppinen@savonia.fi)
* **Rajeev Kanth**, Senior Lecturer, Savonia UAS (Rajeev.kanth@savonia.fi)