COGNIMAN Toolbox Components
Physical Twin
The physical twin represents the actual physical object, process, or system within the real world. This layer includes physical machinery, equipment, devices, and any other tangible components that are part of the manufacturing environment. Sensors and actuators embedded in these physical assets collect real-time data, which serves as the basis for creating and updating the digital twin.
Data Layer
The data layer serves as the foundation for the entire digital twin ecosystem. It encompasses the collection, storage, and management of raw data generated by sensors and other data sources associated with the physical twin. This data can include measurements, sensor readings, operational parameters, environmental conditions, and more. Proper data processing and storage are crucial to ensure accurate and reliable digital twin representations.
Database Design for Manufacturing Data
The system provides a unified interface for interacting with different database backends (PostgreSQL, MySQL, SQLite) ...
Aruco Marker Detection
Detects ArUco markers from an RGB stream in ROS 2 and provides real-time pose data.
Digital Twin Representation
The heart of the architecture is the digital twin itself. This layer is a virtual representation of the physical twin, constructed using the real-time data collected from sensors and combined with relevant contextual information. The digital twin accurately mirrors the behaviour, characteristics, and conditions of its physical counterpart through modeling, such as Artificial Intelligence (AI), Graph based modeling, etc. It enables simulations, analysis, and monitoring, allowing for predictive insights and informed decision-making.
A DigitalTwin for Additive Manufacturing printing process
This project aims to develop a digital twin system for monitoring and optimizing additive manufacturing (AM) printing...
Real-Time ROS2 Lichtblick Visualizer
Real-Time viewer of ROS2 in a web-based fully customizable user interface
Batch identification
The system must be able to acquire the batch 'code' at least in the 90% of cases (ABS Pilot)
Implementation of 2D map
Develop a 2D map that contains: Ground clearance of the bar (y); Abscissa of the bar (X) (ABS Pilot)
Position for different bar dimensions and sections
The system must be able to acquire batch position for different bar dimensions and sections (ABS Pilot)
Service Layer
The service layer encapsulates the functionalities and services that leverage the capabilities of the digital twin. This layer includes various algorithms, models, simulations, and analytic that use the data from the digital twin to generate insights, predictions, and optimization strategies. It is responsible for translating raw data into actionable information and facilitating interactions between different layers of the architecture.
Frame calculation based on touch probing
Calculates the new part frame based on the data from the touch probing and deburring planning (GOIMEK pilot)
Path adaptation based on digitization data
Adapts the path points of the trajectory based on digitization data (GOIMEK pilot)
Path points to robot trajectory
Generates robot trajectory programs for a Staubli robot based on the pathpoints (GOIMEK pilot)
Job Card Extractor
A Python tool for extracting job numbers and operations from manufacturing job cards (PDF documents) using OCR and ba...
Frequency update of the 2D map
The frequency of update of the map must be at least once a day (ABS Pilot)
Batch Self-positioning Service
This system provides autonomous batch positioning using drones equipped with cameras to capture product positions.
Safety rules support (triangle)
The system must be able to recognize the shape of the bar (round), wedges, height, etc. (ABS Pilot)
Safety rules validation (square section)
The system must be able to recognize the shape of the bar (square), height, etc. (ABS Pilot)
Safety Bay Service
A Python-based tool that determines whether a bar stacking bay is safe for operation.
User Interface
The user interface layer provides a means for human interaction with the digital twin ecosystem. It includes interfaces such as graphical user interfaces (GUIs), dashboards, augmented reality (AR) or virtual reality (VR) applications, and other visualization tools. This layer allows users, including operators, engineers, and managers, to monitor the state of the digital twin, access insights, and make informed decisions.
Warehouse Logistic Assistant
The Warehouse Logistic Assistant is a comprehensive web application designed to enhance operational efficiency for lo...
Human Machine Interface for Additive Manufacturing Process
Outputs live data of the process, integrated with warning signals and alerts highlighting the issue.
Connectivity and Integration
The connectivity and integration layer ensures seamless communication between the various components of the architecture. This layer facilitates data exchange between the physical twin, data layer, digital twin, service layer, user interface, and external systems. It might involve technologies such as Internet of Things (IoT) protocols, APIs (Application Programming Interfaces), middleware, and networking solutions to enable smooth and reliable data flow.
ROS2 to Cloud JSON MQTT Bridge
Software to bridge edge-cloud ROS2 and MQTT including a json serialization/deserializacion of messages
