6.1. Conclusions
Based on these conclusions, companies and institutes should consider the possibility of investing money and time in undertaking this direction of working. The power of ML and data analysis, in general, is evident, and putting more effort into building a more extensive company database is of paramount importance.
Future studies could address the origins and causes of the different parameters to understand the implications of these results. A model built in this work can help pursue this goal. The next session explores and suggests two possible new research solutions related to this work.
6.2 Outlook
This work shows significant and good results in predicting tribological outcomes, but the performance can be improved. The first aspect of tackling is the increase of the dimension of the dataset used to train the model. The latter, in general, is a problem related to machine learning and artificial intelligence in many sectors and application areas.
The latest significant data analysis improvement did not ignore tribology, and many tribological problems have already been solved using AI approaches. Different configurations and sensing technologies and tribology's intrinsic multi-scale and statistical nature still pose a significant challenge in addressing ambiguities in the laboratory and operational data sets.
Therefore, a more fundamental study is necessary before applying new AI techniques to assure their applicability and dependability in resolving tribological problems. The importance of particular technical expertise cannot be overstated. Because tribology is a multidisciplinary field, the collaboration between physicists, biochemists, materials engineers, mechatronics, and computer scientists is more challenging. Therefore, tribologists should be encouraged to explore new methodologies and develop cross-disciplinary partnerships. A better understanding of tribology can be gained using AI and machine learning algorithms, leading us to a new, more sustainable, and energy-efficient era.
The use of AI in tribology will undoubtedly rise in the next few years because of rapid algorithmic and computational capacity advancements and data's rising accessibility and recyclability. On the other hand, one of the most considerable challenges is the inaccuracy of results caused by experimental settings and sensor systems variations. Such challenges may
6.2. Outlook
be readily overcome with sufficient study for accurate and appropriate data utilisation and cooperation with specialists from other sectors.
All the test bench experiments should be saved for a more extensive set and a more accurate model. However, this requires much time and work; therefore, it is out of the scope of this work. The possible solution to this work, to make it work better, is to create an “institute database” of tribometer measurements. The database mentioned above can include experiments with many different experimental setups. For example, parameters, such as pressure or load, significantly modify the shape of a Stribeck curve. However, in this work, such parameters could not be compared since the measurements available were only related to the same values.
Another way to be followed is to construct a bigger model that allows the extraction of the complete Stribeck curve. That is to say, having as output a considerable number of points that compose the tribological characteristic curve. The latter requires implementing a Neural Network model, particularly a Long short-term memory one. The latter needs to have some decoder inside it since the model has a much larger number of outputs concerning the number of inputs. A model of this type could also be useless from an engineering perspective. However, it is helpful if the user also needs information related to the number of peaks and, therefore, to the fretting wear phenomenon.
Numerous applications in lubrication and tribology have previously made use of artificial intelligence and machine learning techniques. However, the lubrication and tribological industry will need to create strategies for sharing the enormous quantities of information that such models require if it is to advance. Currently, most tribology tests produce only modest amounts of data, making it challenging to analyse the primary material, surface finish, and lubricant characteristics.
Soon, AI and machine learning can provide early warnings of potential issues and faults with machines so that proactive maintenance can be undertaken and customers will avoid potentially costly breakdowns. However, as stated before, the most critical aspect of achieving will be the trustworthy cooperation between researchers and specialists from many areas to increase the effectiveness of ML & AI in tribology.
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