AI-Powered Development
Data-driven methods for power electronics, drive systems and embedded applications – from algorithm to inference on the target system.
AI is not an end in itself – it solves concrete problems in electronics development. CME applies machine learning methods where classical control or analysis approaches reach their limits: detecting patterns in sensor data, optimizing nonlinear control systems, and enabling predictive maintenance of complex systems. What matters is not model complexity, but the ability to reliably run trained models on resource-constrained embedded hardware – in real time, with limited memory, and under industrial environmental conditions.
Predictive control – Model predictive approaches (MPC) for nonlinear drive systems and power converters
- Automated determination of motor parameters through AI-powered simulation instead of time-consuming manual measurement campaigns
- Virtual commissioning of drive systems: control behavior and efficiency are simulated before hardware exists
- Model-based optimization of control strategies based on real operating points and load profiles
- Direct bridge between simulation and embedded implementation – no media breaks, no data loss
Anomaly detection & predictive maintenance – Detection of degradation in drives, power supplies or power modules based on operational data
- Classical controllers reach their limits with nonlinear drives – MPC thinks several steps ahead and reacts more precisely
- Better control quality for speed, torque and efficiency – even with changing load profiles
- Less overshoot, shorter settling times and lower thermal stress
- Directly implementable on embedded hardware – no external computing unit required
Visual quality control – Image processing and object detection for solder joints, component placement and surface inspection in electronics manufacturing
- Detects early when a drive, power supply or power module behaves abnormally – before it fails
- Analyzes ongoing operational data instead of costly individual measurements
- Reduces unplanned downtime and avoids expensive consequential damage
- Retrofittable into existing systems without hardware changes
Edge inference on embedded targets – Deployment of trained models on MCUs with focus on latency, memory footprint and power consumption
- AI models run directly on the microcontroller in the device – no server, no cloud, no latency
- Works without network connection and under harsh industrial conditions
- Minimal memory and energy requirements – suitable for battery-powered or cost-sensitive products
- CME handles selection, training and deployment tailored to the target hardware
Data preparation & feature engineering – Processing raw data from test benches, field tests and series production for reproducible training pipelines
- Detects solder joint, placement and surface defects more reliably and faster than the human eye
- Trained models learn product-specific defect patterns – no rigid thresholds
- Reduces rework, scrap and complaints in series production
- Runs directly on the production line – no cloud connection required
TinyML & model compression – Quantization, pruning and distillation for deployment on Cortex-M class processors and comparable platforms
- Raw data from test benches and field tests is rarely directly usable – CME prepares it systematically
- Cleaning, normalization and enrichment for reproducible, reliable AI training
- Prevents the most common mistake: models that work in the lab but fail in production
- Documented data pipelines for traceability and reuse
Model lifecycle & versioning – Traceable model versions, test coverage and documentation for regulated industries
- Large AI models reduced to fractions of their size – without significant quality loss
- Enables AI functions on small, affordable processors (ARM Cortex-M and comparable)
- No expensive hardware upgrade needed: AI intelligence on existing embedded platform
- Quantization, pruning and distillation depending on resource and accuracy requirements
Integration into existing systems – Embedding AI functions into existing firmware, RTOS or PLC architectures without system disruption
- AI functions are embedded into existing firmware, RTOS or PLC environments – no restart from zero
- No interruption of running production lines or existing system architectures
- CME analyzes the existing system and defines the minimal integration path
- Tested handover: interfaces, timing and resource usage are validated in advance
Regulation & Documentation for AI Systems
- AI in safety-relevant products requires evidence – CME provides the necessary documentation
- Technical documents for approval processes according to EU AI Act, ISO 26262 and IEC 62061
- Explainability of model decisions as a prerequisite for certifications
- Support with risk classification and conformity assessment for authorities
Frequently Asked Questions
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