Sensor Placement Design Strategy and Quality Estimation in Modern Car Body Production Using Stochastic Finite Element Methods
| Autor: | R. Struck, Pavel Hora, H. Mautz, Niko Manopulo, F.M. Neuhauser |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry media_common.quotation_subject Process (computing) Automotive industry Design strategy Industrial and Manufacturing Engineering Finite element method Reliability engineering Variable (computer science) Artificial Intelligence Quality (business) Observability business Selection algorithm media_common |
| Zdroj: | Procedia Manufacturing. 27:104-111 |
| ISSN: | 2351-9789 |
| DOI: | 10.1016/j.promfg.2018.12.051 |
| Popis: | For automotive body panel’s producers, any failures of tooling or formed components pose significant risk in the forms of costly repairs and production delays. Accordingly, methods of reducing the risk of tooling damage and part failure through predictive measures are of great value. Such predictive methods are particularly beneficial in optimizing forming operations when process parameters such as friction and material behavior are uncertain or variable. AUDI has invented the Intelligent Tool which uses sensor measurements to observe the quality of the part and adjusts actuators to eliminate differences between desired quality and actual quality. A key question in planning such intelligent tool systems is the appropriate number and positions of sensors to maximize information content. A stochastic finite element method-based approach has been developed in the scope of this work to critically assess the observability of split and wrinkling type failures, based on laser sensor draw-in measurements. Furthermore, a design strategy for the number of sensors and the respective positions around the blank is proposed which enables an accurate yet cost-effective acquisition of process data. The methodology is applied to the tailgate of an AUDI Model A4. Stochastic finite element simulations are computed in AutoForm. The quality of the part under varying process parameters is evaluated and critical zones are identified. A principal component analysis is utilized to reveal that correlations exist between the quality criteria; therefore, only principal quality criteria need to be observed in order to estimate global part quality. Regression models are trained to connect quality criteria to flange draw-in measurements and a subset selection algorithm is used to find the optimal sensor layout which delivers the highest information content. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|