Transform Your Manufacturing Operations with Physics-Informed AI

Introducing Hexigma

Manufacturing is evolving. The integration of physics-informed AI and digital twins marks a fundamental shift toward autonomous, adaptive production systems. Our platform grounds artificial intelligence in established physical principles while leveraging real-time sensor data, delivering unprecedented flexibility, quality, and efficiency. As your operations mature with these technologies, you'll unlock new capabilities in mass customization, sustainable production, and quality excellence.

The Power of Hybrid Intelligence

Our platform combines two complementary approaches: the interpretive power of physical laws and the predictive accuracy of machine learning. This hybrid framework overcomes the limitations of using either method alone, creating robust models that work in real manufacturing environments. We apply this technology across predictive maintenance, process optimization, quality control, and intelligent digital twins.

Four Proven Architectures

Our platform leverages multiple hybrid model architectures, each optimized for specific manufacturing challenges:

• Physics-Informed Machine Learning (PIML) embeds physical laws directly into the model's training process. The system is automatically penalized when predictions violate physical constraints, ensuring outputs remain grounded in engineering reality.
• Physics-Guided Machine Learning (PGML) uses physics-based models to generate features and augment training data. Your physics knowledge guides the machine learning process without constraining the architecture.
• Physics-Data Fusion Models combine outputs from both physics-based and data-driven models through intelligent weighting. This approach delivers more robust predictions across varying operating conditions.
• Hybrid Digital Twins integrate high-fidelity virtual models with real-time data-driven components. Machine learning continuously corrects for inaccuracies in physics-based simulations using your sensor data, providing an accurate digital replica of your physical assets.

Real Manufacturing Applications

Our platform transforms critical areas of your operations, particularly as you advance Industry 4.0 initiatives:

• Predictive Maintenance: Forecast equipment remaining useful life with greater accuracy by combining physics-based wear models with real-time operational data. Schedule maintenance more precisely and prevent costly unplanned downtime.
• Process Optimization and Control: Fuse physics-based understanding of your processes—fluid dynamics, heat transfer, material behavior—with machine learning that compensates for unmodeled effects. Precisely tune control systems to improve efficiency, reduce energy consumption, and increase yield.
• Quality Control: Predict part quality and identify potential defects by integrating models of material properties and tool wear with sensor data from temperature, vision systems, and surface measurements. In additive manufacturing, catch quality issues before they become defects.
• Hybrid Manufacturing: Optimize complex interplay between additive and subtractive processes in advanced CNC operations. Improve accuracy and surface finish while reducing cycle times.
• Virtual Sensing: Infer difficult-to-measure parameters—internal forces, temperatures, stresses—in real-time by combining physical equations with data from more accessible sensors.

Measurable Benefits for Your Operations

Backed by physics

• Enhanced Accuracy and Robustness: Our platform combines the predictive power of data with the foundational integrity of physics, delivering more reliable and precise models, especially when working with limited data.
• Improved Generalization: Physics-based constraints ensure models behave logically and make reliable predictions even for operating conditions not encountered during training. Deploy with confidence across varying scenarios.
• Increased Explainability: Physical laws add transparency to machine learning's "black box," making results easier for your engineers to understand, validate, and trust.
• Data Efficiency: Learn from smaller datasets than pure machine learning models. The physics component provides a strong, physically consistent foundation, reducing the time and cost of deployment.