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Model-Based Engineering
Model-Based Engineering (MBE) is a disciplined way to design, analyze and control intelligent machines and high-tech mechatronic systems using mathematical models. Instead of relying on trial‑and‑error and building and tweaking hardware until it “works”, we capture the behavior of your machine in physics-based and data-driven models. These models, also called Digital Twins, let us explore design choices in simulation, quantify trade-offs, and verify control strategies before touching hardware. This approach shortens development time, reduces the overall cost, improves performance during the entire lifecycle, and removes surprises during the first hardware test.
At INGENIQS, we developped our own MBE-technology that spans the full lifecycle. By combining first‑principles models with measurement data, our technology creates an accurate digital twin that guides decisions from concept to commissioning, control design, software generation, hardware-in-the-loop testing, deployment and on-machine validation. Even in operation, the same model acts as an intelligent digital twin, fusing measurement data with the model to monitor performance, diagnose issues and refine control, safely and continuously.
The future of high-tech systems is built on intelligent digital twins that learn from data and predict behaviour - in real time.
Classic engineering approach
INGENIQ's MODEL-BASED ENGINEERING METHODOLOGY
BENEFITS
- Shorter time to market: less development time with fewer prototypes
- Lower development cost: reduced non-recurring engineering (NRE) through fewer hardware iterations
- Higher quality: validate requirements early and reduce integration surprises
- Optimal performance: design optimization and real-time performance optimization based on measurement data
- Lower risk: de-risk commissioning with hardware-in-the-loop testing
- Sustainable: lower energy consumption
- Lifecycle: monitor and continuous improve during the entire lifecycle of your machine
- Predictive maintenance: detect anomalies early and prevent unplanned downtime
Use case
Model Predictive Control
Ball-balancing tripod demonstrating superior model predictive control performance using real-time controller and advanced models.
Thermal validation tool
Design of a thermal validation tool for a lithography module to verify its performance on millikelvin temperature stability.
Machine Retrofits
You don’t need a new machine to gain performance. With our MBE-technology we upgrade the performance of what you already have. We reduce cycle times by optimizing motion profiles with jerk‑limited trajectories, look‑ahead, and use model-based control strategies. We tighten precision and stability by compensating friction and backlash, improving disturbance rejection and feedforward, estimating states with observers, and suppressing vibrations. We raise yield and quality by tuning parameters with data, compensating thermal drift, and fusing multiple sensors to get the most reliable signal. We also lower energy consumption and extend lifetime of your machine.
Upgrade, don’t replace: retrofit for higher performance and lower cost - in any industry.
FAQ's
- Is this only for new designs or machines? No. Many improvements focus on retrofitting better control and calibration to existing machines.
- Do we need perfect models? No. We aim for the right‑sized fidelity and refine with data until the model is accurate enough for your application.
- Will it fit our stack? Yes. We integrate with your controllers, PLC/IPC, and data systems; there is no need for a rip‑and‑replace.