Proceedings Of The 10th International Conference On Fracture Fatigue And Wear

This book gathers a selection of peer-reviewed papers presented at the 10th International Conference on Fracture Fatigue and Wear (FFW 2022), held in the city of Ghent, Belgium, on August 2–3, 2022. The contributions, prepared by international scientists and engineers, cover the latest advances in and innovative applications of fracture mechanics, fatigue of materials, tribology, and wear of materials. In addition, they discuss industrial applications and cover theoretical and analytical methods, numerical simulations, and experimental techniques. The book is intended for academics, including graduate students and researchers, as well as industrial practitioners working in the areas of fracture fatigue and wear.

These proceedings gather a selection of peer-reviewed papers presented at the 7th International Conference on Fracture Fatigue and Wear (FFW 2018), held at Ghent University, Belgium on 9–10 July 2018. The contributions, prepared by international scientists and engineers, cover the latest advances in and innovative applications of fracture mechanics, fatigue of materials, tribology and wear of materials. The book is intended for academics, including graduate students and researchers, as well as industrial practitioners working in the areas of fracture fatigue and wear.

This proceedings gather a selection of peer-reviewed papers presented at the 9th International Conference on Fracture Fatigue and Wear (FFW 2021), held in the city of Ghent, Belgium on 2-3 August 2021. The contributions, prepared by international scientists and engineers, cover the latest advances in and innovative applications of fracture mechanics, fatigue of materials, tribology, and wear of materials. In addition, they discuss industrial applications and cover theoretical and analytical methods, numerical simulations and experimental techniques. The book is intended for academics, including graduate students and researchers, as well as industrial practitioners working in the areas of fracture fatigue and wear.

This proceedings gather a selection of peer-reviewed papers presented at the 8th International Conference on Fracture Fatigue and Wear (FFW 2020), held as a virtual conference on 26-27 August 2020. The contributions, prepared by international scientists and engineers, cover the latest advances in and innovative applications of fracture mechanics, fatigue of materials, tribology, and wear of materials. In addition, they discuss industrial applications and cover theoretical and analytical methods, numerical simulations and experimental techniques. The book is intended for academics, including graduate students and researchers, as well as industrial practitioners working in the areas of fracture fatigue and wear.

This proceedings gather a selection of peer-reviewed papers presented at the 8th International Conference on Fracture Fatigue and Wear (FFW 2020), held as a virtual conference on 26–27 August 2020. The contributions, prepared by international scientists and engineers, cover the latest advances in and innovative applications of fracture mechanics, fatigue of materials, tribology, and wear of materials. In addition, they discuss industrial applications and cover theoretical and analytical methods, numerical simulations and experimental techniques. The book is intended for academics, including graduate students and researchers, as well as industrial practitioners working in the areas of fracture fatigue and wear.

Fatigue failure of structures used in transportation, industry, medical equipment, and electronic components needs to build a link between cutting-edge experimental characterization and probabilistically grounded numerical and artificially intelligent tools. The physics involved in this process chain is computationally prohibitive to comprehend using traditional computation methods. Using machine learning and Bayesian statistics, a defect-correlated estimate of fatigue strength was developed. Fatigue, which is a random variable, is studied in a Bayesian-based machine learning algorithm. The stress-life model was used based on the compatibility condition of life and load distributions. The defect-correlated assessment of fatigue strength was established using the proposed machine learning and Bayesian statistics algorithms. It enabled the mapping of structural and process-induced fatigue characteristics into a geometry-independent load density chart across a wide range of fatigue regimes.