Computational Modelling

There are a number of steps to deliver an accurate functional model:

1. Data Collection: Tribology machines generate copious amounts of data during tests, recording parameters such as friction coefficients, wear rates, temperatures, and more. This data is collected meticulously during controlled lab experiments.
2. Data Preparation: To make this data useful for machine learning, it needs to be preprocessed. This involves cleaning, formatting, and organizing the data into a structured dataset. Lab data often undergoes normalization and feature engineering to highlight relevant variables.
3. Feature Selection: Not all collected data may be essential for predicting real-world performance. Machine learning models can benefit from feature selection techniques that identify the most relevant variables for the prediction task.
4. Model Training: With the prepared dataset and selected features, machine learning models are trained. These models can be various types, such as regression, decision trees, neural networks, or support vector machines. During training, the models learn the relationships between input variables (tribological data) and the target variable (real-world performance).
5. Validation: The trained models are validated to ensure their predictive accuracy. This typically involves splitting the dataset into training and validation sets to assess how well the model generalizes to unseen data.
6. Model Testing: Once validated, the models can be tested using data collected from tribology machines for new lubricants or materials. These tests simulate real-world conditions as closely as possible.
7. Performance Prediction: With the trained and validated machine learning models, it becomes possible to predict how a lubricant or material will perform under specific real-world conditions. For example, the model could predict the wear rate or friction coefficient of a lubricant in an actual engine or industrial machinery, taking into account factors like temperature, load, and speed.
8. Continuous Improvement: Machine learning models are not static; they can be continually improved as more real-world data becomes available. The models can adapt and refine their predictions based on the feedback and new information they receive.

The process is complex, but once the model is validated effectively, it facilitates a huge increase in efficiency of real world development and testing.