Open AccessBayesian Model-based State Estimation for Mass Production Metal Forming
Author(s) -
Jos Havinga,
Pranab Kumar Mandal,
A.H. van den Boogaard
Publication year - 2019
Publication title -
iop conference series materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/651/1/012095
Subject(s) - process (computing) , bayesian inference , sheet metal , state (computer science) , computer science , measure (data warehouse) , work in process , statistical model , process modeling , probabilistic logic , statistical inference , bayesian probability , algorithm , mathematics , mechanical engineering , engineering , data mining , statistics , artificial intelligence , operations management , operating system
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.