Smart Manufacturing: An AI-Powered Production

At WoodFrog, we specialize in delivering AI-driven solutions tailored for the manufacturing industry, where precision, efficiency, and reliability are critical. Our team leverages cutting-edge state-of-the-art artificial intelligence techniques to address real-world production challenges, from predictive maintenance to quality control and workflow optimization. Our approach is rooted in a deep understanding of manufacturing operations, allowing us to provide intelligent, data-driven solutions that align seamlessly with industry demands.

What We Offer

Predictive Maintenance for Downtime Reduction in Aluminum Casting

Frequent mold failures in the aluminum casting process were causing significant operational downtime and financial losses. The primary challenge was to predict these failures in advance, allowing the client to take preventive actions that would reduce unplanned downtime, improve efficiency, and minimize associated costs.

Aluminum casting manufacturing workflow

We mapped the entire casting workflow, identifying machinery involved, operational settings, and historical maintenance records. In collaboration with production managers, engineers, and quality control teams, we gathered insights into common causes of mold failure and downtime patterns. Critical data points included sensor data from mold machines (temperature, pressure, and position readings), historical maintenance logs, production quality inspection reports, and real-time operational parameters.

Smart Monitoring: ML for Early Fault Detection in Gas Turbines

Our client, a global energy provider, operates a fleet of gas turbines equipped with sensors measuring critical operational parameters such as rotational speed (RPM), exhaust temperature, and pressure ratios. The challenge was to develop a system that detects anomalies early enough to prevent failures, enabling predictive maintenance.

Turbines were equipped with high-frequency sensors (100–500 Hz) capturing vibration, RPM, temperature, and pressure. Outlier detection and noise filtering using Z-score analysis and low-pass filters were applied, along with feature engineering via Fourier Transform (FFT) and Wavelet Transforms. Models evaluated included Autoencoders, LSTM networks, and Isolation Forests, with metrics covering accuracy, precision, recall, F1-score, and processing latency.

Gas turbine facility monitoring

Business Outcomes