Bayesian Model-based State Estimation for Mass Production Metal Forming

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Modern metal forming factories produce large amounts of data, such as process forces and product geometries. These data contain indirect information about fluctuations in the manufacturing process, such as changes in temperature, material properties and lubrication conditions. In this work, Bayesian inference is used to obtain a probabilistic estimate of the process state based on force measurements in mass production metal forming. The procedure requires statistical assumptions about process state variations, which are often not known as it is usually not possible to directly measure the process state in-line. It is shown that unknown statistical model parameters can be estimated simultaneously with the process state. This leads to an improvement in the accuracy of the state estimate. The procedure is studied using pseudo-data from a mass production sheet bending process, using a finite element model with ten parameters. The material, friction and process parameters are estimated based on process force measurements.
Original languageEnglish
Title of host publication38th International Deep Drawing Research Group Conference
PublisherIOP Science
Number of pages8
Publication statusPublished - 2019
Event38th International Deep Drawing Research Group Annual Conference, IDDRG 2019: Forming 4.0: Big Data - Smart Solutions - University of Twente, Enschede, Netherlands
Duration: 3 Jun 20197 Jun 2019
Conference number: 38

Publication series

NameIOP Conference Series: Materials Science and Engineering


Conference38th International Deep Drawing Research Group Annual Conference, IDDRG 2019
Abbreviated titleIDDRG 2019
Internet address


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