Multi-Dimensional Regression Models for Predicting the Wall Thickness Distribution of Corrugated Pipes

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@Article{albrecht:2022:Polymers,

• author = "Hanny Albrecht and Wolfgang Roland and Christian Fiebig and Gerald Roman Berger-Weber",
• title = "Multi-Dimensional Regression Models for Predicting the Wall Thickness Distribution of Corrugated Pipes",
• journal = "Polymers",
• year = "2022",
• volume = "14",
• number = "17",
• pages = "Article No. 3455",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2073-4360",
• URL = "https://www.mdpi.com/2073-4360/14/17/3455",
• DOI = "doi:10.3390/polym14173455",
• abstract = "Corrugated pipes offer both higher stiffness and higher flexibility while simultaneously requiring less material than rigid pipes. Production rates of corrugated pipes have therefore increased significantly in recent years. Due to rising commodity prices, pipe manufacturers have been driven to produce corrugated pipes of high quality with reduced material input. To the best of our knowledge, corrugated pipe geometry and wall thickness distribution significantly influence pipe properties. Essential factors in optimising wall thickness distribution include adaptation of the mold block geometry and structure optimisation. To achieve these goals, a conventional approach would typically require numerous iterations over various pipe geometries, several mold block geometries, and then fabrication of pipes to be tested experimentally—an approach which is very time-consuming and costly. To address this issue, we developed multi-dimensional mathematical models that predict the wall thickness distribution in corrugated pipes as functions of the mold geometry by using symbolic regression based on genetic programming (GP). First, the blow molding problem was transformed into a dimensionless representation. Then, a screening study was performed to identify the most significant influencing parameters, which were subsequently varied within wide ranges as a basis for a comprehensive, numerically driven parametric design study. The data set obtained was used as input for data-driven modelling to derive novel regression models for predicting wall thickness distribution. Finally, model accuracy was confirmed by means of an error analysis that evaluated various statistical metrics. With our models, wall thickness distribution can now be predicted and subsequently used for structural analysis, thus enabling digital mold block design and optimising the wall thickness distribution.",
• notes = "also known as \cite{polym14173455}",

}

Genetic Programming entries for Hanny Albrecht Wolfgang Roland Christian Fiebig Gerald Roman Berger-Weber

Citations