Load assumption process for durability design using new data sources and data analytics. (April 2021)

• Record Type: Journal Article
• Authors: Burger, Michael; Dreßler, Klaus; Speckert, Michael
• Is Part Of: International journal of fatigue. Volume 145(2021)
• Publication Date: 2021-04
• Subjects: Data analytics -- Statistical methods -- Machine learning -- System theory -- Optimal control -- Loads and durability; 49-00 -- 62-00 -- 62-07 -- 93-03; Materials -- Fatigue -- Periodicals; Materials -- Fatigue; Periodicals; 620.1122
• DOI: 10.1016/j.ijfatigue.2020.106116
• Languages: English
• ISSNs: 0142-1123

Abstract

Abstract: Durability engineering for vehicles is about relating the real operational loading and fatigue environment to the actual capability and strength capacity of the product and its parts. The statistical modelling of the usage loading distribution has always been a big challenge; the latter is necessary to derive statistically validated design targets. In the past, dedicated durability measurement campaigns were the only available data source to obtain durability loads. Nowadays, this has changed due to the increasing availability of new complementary data sources. We show how such data like field monitoring data and geographic data, along with the usage of new data analytics and simulation methods can be used to derive customer and usage related durability loads and targets.