Tribology Informatics

Credit: This article is the summary of Tribo-informatics approaches in tribology research by Nian et.al, published in Springer [1]

Introduction

Tribology is a field that involves studying friction, wear, and lubrication between interacting surfaces. A tribological system is comprised of tribo pairs, lubricating medium, and working environment, and this system exhibit changes to various factors of time, type of system, interface, etc. this tends to give a wide range of data sources and numerous theoretical models in tribology research. Integrating this with information technology could help in processing the data faster. Information technology is a method of generating, collecting, processing, and analyzing information. Merging this with tribology helps in accelerating tribology research giving rise to a new field called tribo-informatics.

What is Tribo-Informatics

Tribo-informatics is developed to improve the efficiency of the tribology process by establishing the tribology standards, building tribology databases, and using information technology to collect classify, store and retrieve, analyze and disseminate tribology information. The main purpose of tribo-informatics includes condition monitoring, behavior prediction, and optimization of tribological systems. Information technology helps in creating a model by analyzing various data outputs from the previously performed experiments and processing the data then enriching the model for future prediction.

How does tribo-informatics work

The tribo-informatics approach is a collection of methods using tribology information and processing it. This includes methods such as the Gaussian regression method, linear regression method, and least squares method, but also advanced machine learning methods. Artificial intelligence methods are the products of machine learning development. The main purpose of this tribo-informatics is to obtain the relation between various quantities in the tribological system. If the relationship between an observable state quantity and an unobservable state quantity is obtained, state monitoring of the tribological system is realized. In case of the relationship between the input quantity or current state quantity and a future state quantity is obtained, prediction of the tribological behavior can be realized. Further, the relationship between the input and target output is obtained, and the tribological system can be optimized to obtain better output.

Application of tribo-informatics in tribology

Tribo-informatics approaches have many application scenarios in the field of tribology. However large research is focused on three areas, which are the cutting process, friction stir welding, and wear analysis based on ferrographs.

1. Cutting process: Cutting is an important machining process, and it is also a very complex nonlinear process. To ensure stable and reliable machining quality, it is necessary to monitor and analyze the cutting parameters, cutting force, tool wear, and other parameters. The cutting force can reflect the state of the surface processing, and it also affects the tool life, processing roughness, and surface geometry. Tool wear significantly affects the machined surface texture, tool life, and machining costs. Selecting suitable initial settings for the cutting parameters is an important method for optimizing the cutting process. Reasonable settings can effectively improve the tool wear and prepare target surfaces.
2. Friction stir-welding: Friction is an inevitable product of the cutting process. In contrast, friction is a necessary condition for the process of friction stir welding. Friction stir welding is used for welding between workpieces, and thus the prediction and optimization of the welding quality are the main objectives of the application of tribo-informatics methods. Many machine learning methods have been used to predict the welding quality of friction stir welding, including artificial neural networks, regression models, and ANFIS. In the welding process, the required process parameters may affect the welding quality, and achieving accurate prediction of the welding quality as a multi-objective welding optimization problem provides the basis for these parameters To predict and optimize the welding quality, it is necessary to obtain the relationship between input quantities (such as the welding speed, rotation speed, cutting depth, and tool type), state quantities (such as welding vibration, sounds, and images), and output quantities (such as the tensile strength, yield strength, hardness, and residual stress).
3. Wear analysis by ferrographs: Ferro-spectrometry is a new type of wear test method that uses a magnetic force to separate the metal particles in oil and arrange them on a substrate according to the size of the particles. Using this method, researchers can obtain the particle concentration in the oil and the micro-mechanical properties of the wear particles. This technology is thus of great significance for wear testing and analysis.

Limitations

Tribology is a systematic, multidisciplinary coupling that is time-dependent, and the relationship between two physical quantities cannot reveal the operating law of the entire system. For this reason, the concept and architecture of tribo-informatics have been proposed, and the application of tribo-informatics methods in practical problems should also consider the input, output, and state quantities of the tribological system. It is difficult to determine the most suitable informatics method for a specific tribological problem. At present, the general process of most tribo-informatics research is to use a selected informatics method for monitoring or prediction, and the obtained results are compared and verified with tribology experiments

Future development

There is no proper framework for the tribo-informatics hence it is necessary to establish a complete concept and framework of tribo-informatics. Then, a comprehensive fusion study should be carried out based on this framework. In the future, a complete tribo-informatics approach should be able to link the input, state, and output of a tribology system based on the tribology information model to obtain more accurate monitoring, prediction, and optimization results. Standardizing the tests is important. Standards for tribological testing need to be established, which will allow various tribological test data to be reused. Advanced tribological data collection technology should be developed to handle the data obtained from tribology experiments.

Reference

[1] Yin, N., Xing, Z., He, K. and Zhang, Z., 2022. Tribo-informatics approaches in tribology research: A review. Friction, pp.1-22.