Reducing the Sim2Real-Gap in Extrusion Blow Molding using Random Forest Regressors

被引：0

作者：

Maack R.F. ^{[1
]}

Waubert de Puiseau C. ^{[1
]}

Sokolova A. ^{[2
]}

Atsbha H. ^{[3
]}

Tercan H. ^{[1
]}

Meisen T. ^{[1
]}

机构：

[1] Chair of Technologies and Management of Digital Transformation, University of Wuppertal, Lise-Meitner-Straße 27, Wuppertal

[2] Kautex Textron GmbH & Co. KG, Kautexstraße 52, Bonn

[3] Kautex Textron Canada, Kautex Drive 2701, Windsor

来源：

Manufacturing Letters | 2022年 / 33卷

关键词：

extrusion blow molding; machine learning; manufacturing; quality estimation; random forest; sim2real gap;

D O I：

10.1016/j.mfglet.2022.07.104

中图分类号：

学科分类号：

摘要：

In plastics manufacturing, the extrusion of thermoplastic material and subsequent blow molding into a cavity is a common procedure to create hollow objects with arbitrary geometric shapes and sizes. To ensure reliability of the production process and satisfactory quality of the final product, several control parameters need to be adjusted to modify the process behavior. The appropriate parameter allocation is typically determined during product and process development using FEM simulations. The simulations provide the ability to estimate the expected geometric shape of the final product and adjust the control parameters accordingly. However, simulations are only as good as their underlying computational model and can reflect reality only to a restricted degree. Deviations between the simulated product quality and real quality measurements of the physically produced product, the so-called sim2real gap, cannot be entirely avoided and persist even after several optimization cycles. In this paper, we address this problem and propose a data-driven approach to correct the sim2real gap. At its core, our approach is based on a random forest regression model that predicts simulation deviations based on process and product characteristics. The input data for the model is composed of control parameters from the extrusion and blow molding process as well as data from intermediate simulation results. Given the multitude of different variables, we incorporate extensive feature selection and engineering to extract the most valuable information to predict the sim2real gap. We train and evaluate our approach in a real use-case in which several different product geometries are manufactured with differently configured control parameters. The results show that our approach successfully reduces the sim2real-gap significantly. In addition, we show that with carefully designed feature engineering and parameterization, a model trained on one set of products with certain geometrical characteristics can be transferred and applied to another set of products with distinctively different geometrical characteristics. © 2022

引用

页码：843 / 849

页数：6