Improve quality & quality control with RAMPCO
Artificial Intelligence (AI)-based Software to Digitize Production Process
Improving product quality
Optimizing the production process
Reducing the production cost
Decreasing the pollution resulting from the production
Self-learning of the software over time as the data volume increases
Increase manufacturing throughput driven by RAMPCO
Data-driven control is an emerging field of recent current interest in industrial control that seeks to develop controllers without knowing dynamical models of the system. We intend to program software models using innovative advanced analytics and deep learning techniques to generate high accuracy and fast simulation through. These models are subsequently implemented in a pioneering integrated production optimization tool, supporting field operations. Our Regression Algorithms will find a relationship between inputs and outputs and extract the corresponding rules. Machine Learning (ML) learns from examples.
The computer retrieves the rules instead of us programming it. However, training the ML is essential. ML has three major branches; supervised, unsupervised, and reinforcement learning. We use supervised learning. Supervised learning has two main sub-branches, namely, regression and classification.
We can solve many operational problems by implementing optimization methods and optimal solutions.
State-of-the-art machine learning algorithms identify issues on a real-time basis in production and highlight efficiency variations. Machine Learning, a subset of AI, can be trained (supervised training), so the software can be used daily to identify production issues and continuously improve processes quickly.