This catalogue contains the main use-cases and application of the project AIMS5.0, providing tools to implement AI applications using reusable tools. For getting started with the AI Toolbox, check the getting started guide.
Geofencing is the problem of triggering some actions when a person or and object enters into or leaves a predefined place. For implementing a geofencing application, please check the Sentinel Tool.
With the advent of large language models (LLM), chatbots ara capable of imitating human behavior. While LLMs have a great common knowledge, answering to questions regarding special knowledge requires fine-tuning of language models. For context based question answering, a typical method is to use prompt injection, or retrieval augmented generation. For getting started with the method, see the LLM Tool and the Context Injection Tool.
For determine the object pose in the camera of the robotic arm for various robotic problems, check 6D Pose Estimation Tool.
This application focuses on developing sustainable and energy-efficient methods throughout the AI model lifecycle. It includes tools for monitoring energy usage and optimizing lifecycle stages like training, deployment, and maintenance. See the Green AI Tool.
Applies AI to optimize each element of the value chain—from procurement through production to distribution. Tools in this category enhance traceability, forecasting, and cost-efficiency. See Value Chain AI Tool.
In progress...
Strengthens the integration of production systems and logistics using predictive AI models to improve flow efficiency and reduce lead times. See Logistics-Production Bridge Tool.
Implements AI in various levels of machine tools—control, monitoring, and optimization. See Machine Tool AI Tool.
A robotic system capable of adaptive conveyor feeding using AI for object recognition and dynamic planning. Check 6D Pose Estimation Tool.
In progress...
Enhances human-machine collaboration by aligning AI models with worker routines and manufacturing rhythms. See Human-Cycle AI Tool.
Optimizes the scheduling of batch processing machines in semiconductor fabs by balancing energy efficiency and throughput. Refer to Wafer Scheduling Tool.
Extends traditional Manufacturing Execution Systems (MES) with AI-driven insights to streamline production cycles. See AI MES Tool.
Combines AI and IoT to automate and optimize conditions for indoor agriculture, such as lighting, irrigation, and climate. Check Indoor Farming Tool.
Focuses on robust and competitive network designs in semiconductor manufacturing by blending human knowledge with AI planning. See Human-AI Semiconductor Tool.
Uses AI-enhanced sensors to improve the robustness and fault tolerance of wafer transport and storage automation. Check Wafer Sensor Tool.
In progress...
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Uses semantic models to enable smarter supply chain decisions through AI-enhanced understanding of economic and ecological trade-offs. Refer to Semantic Supply Tool.
Extends reference ontologies to support AI-based business planning and smart incident response. Check Business Ontology Tool.
In progress...