Tribologia - Finnish Journal of Tribology

Foreword No 1-2 (2024)

Vuokko Marjamaa — Mon, 08 Jul 2024 00:00:00 +0300

Dear Readers,

This year has reached the midterm and it is time to introduce you our latest issue from the exciting world of tribology. This time we introduce you research papers that are dealing for instance with high temperature tribology.

During June, the NORDTRIB symposium was held in Denmark gathering tribology related minds and topics under the same roof. Focus was this time on Industrial Tribology. We are expecting to have some conference papers to be published at the end of the year. If you were attending to the Nordtrib 2024, you are most welcome to send your extended abstracts for our issue to be published later this year. Of course, you are also welcome to send the full-length research papers as well. Contact our editorial team for more details.

The NORDTRIB symposium is held biennially in one of the Nordic countries: Finland, Sweden, Norway, and Denmark. It was first hosted in Tampere, Finland in 1984. Over the years the Symposium has evolved into a highly regarded venue for the international tribology community. NORDTRIB is intended as a forum where researchers and developers from universities, research institutes and industry can present their latest contributions and discuss field-related issues.

NORDTRIB 2026 will be organized by Tampere University jointly with the Finnish Society for Tribology at the Tampere University city center campus. The conference will focus on both fundamental and applied aspects of friction, wear, and lubrication covering all scales (from nano to macro). Scientific contributions in all aspects of tribology are welcome. Scientific work covering global sustainability problems, environmentally acceptable solutions and the future of energy usage, and storage are specially encouraged. More information can be found online.

Enjoy reading our latest issue,

Vuokko Marjamaa
Editor-in-Chief

New Framework for studying High Temperature Tribology (HTT) Using a Coupling Between Experimental Design and Machine Learning

Mohamed Kchaou — Mon, 15 Jul 2024 00:00:00 +0300

High temperature tribology (HTT) is considered to start at a minimum temperature of 300°C, where organic base oils and polymers begin to decompose, up to a temperature of 1200°C. In this area of application, a tribological test is typically performed under dry or solid friction, unless a solid lubricant is used, as most lubricants oxidize or decompose when exposed to these extreme temperatures. This is the case of hot forming tribology. Therefore, specific tribometers have been developed to study the tribological behavior, wear modes, friction-wear mechanisms, and other tribological aspects in the case of contact workpiece-forming tools. However, as the interdisciplinary character of tribology represents great opportunities, but also a huge challenge to well study the tribological behaviour of materials and systems, particularly at hot temperature, results are well impacted by the proposed hypothesis, the limited number of parameters to study, the dynamic behaviors of solicitations, etc. A new approach to overcoming its limitations remains a necessity in modern research. After giving general definitions of the notions of tribological system and tribometer, an assessment of the different configurations of these test benches is established. The presentation of the tribometers is organized according to the configuration adopted. The study is based on the identification of the “originality” of the benches and the limitations of the approaches used in the study of hot temperature sliding contact. The framework of the coupling between experimentation and machine learning is presented. Different scenarios are discussed in order to develop new approaches/methods of collaboration between the design of experiment, numerical development, and ML algorithms.

Mechanical and Tribological properties of the Lead-Free Composite-A review

Darshan Kulkarni, Dr. A.B. Kakade — Mon, 15 Jul 2024 00:00:00 +0300

In recent years, there has been a growing demand for materials that are both eco-friendly and cost-effective while maintaining high performance levels. Design engineers and researchers are increasingly turning to advanced materials as substitutes for traditional metals and alloys. Journal bearings, integral components of machinery and engines, play a crucial role in their operation, significantly impacting efficiency, operational costs, system longevity, and reliability. The pursuit of eco-friendly and high-performance materials has spurred notable progress in tribological research, particularly concerning journal bearings. This study offers a comprehensive examination of lead-free composite materials for journal bearings, delving into various types such as polymers, ceramics, and metallic alloys. It meticulously evaluates their mechanical and tribological properties, as well as their microstructural characteristics. The insights gleaned from this review are invaluable for engineers and researchers seeking to enhance the design and performance of journal bearings while aligning with environmental regulations and sustainability objectives.

Fault Diagnosis and Prediction of Remaining Useful Life (RUL) of Rolling Element Bearing : A review state of art

Shyam Mogal, R. V. Bhandare, V. M. Phalle, P. B. Kushare — Mon, 15 Jul 2024 00:00:00 +0300

Fault diagnosis of rolling element bearings is a critical aspect of machine maintenance and reliability. Bearings are extensively used in various industrial applications, and their failure can lead to costly downtime and equipment damage. Rotating machinery under continuous overload conditions can indeed significantly degrade bearing life and lead to various other issues. To identify issues in rolling element bearings (REB), several techniques and methods are employed. Diagnosing faults in ball bearings while simultaneously estimating the Remaining Useful Life (RUL) of the bearing is a crucial aspect of predictive maintenance. This can be achieved through a combination of signal processing techniques, machine learning methods, and RUL prediction models. The estimation of a bearing Remaining Useful Life (RUL) is of significant importance in predictive maintenance strategies to avoid unexpected failures, reduce downtime, and optimize maintenance costs. This literature review aims to explore the methodologies, techniques, and advancements in predicting the remaining useful life of bearings.