A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials

verfasst von

Karina Gevers, Alexander Tornede, Marcel Wever, Volker Schöppner, Eyke Hüllermeier

Abstract

Heated tool butt welding is a method often used for joining thermoplastics, especially when the components are made out of different materials. The quality of the connection between the components crucially depends on a suitable choice of the parameters of the welding process, such as heating time, temperature, and the precise way how the parts are then welded. Moreover, when different materials are to be joined, the parameter values need to be tailored to the specifics of the respective material. To this end, in this paper, three approaches to tailor the parameter values to optimize the quality of the connection are compared: a heuristic by Potente, statistical experimental design, and Bayesian optimization. With the suitability for practice in mind, a series of experiments are carried out with these approaches, and their capabilities of proposing well-performing parameter values are investigated. As a result, Bayesian optimization is found to yield peak performance, but the costs for optimization are substantial. In contrast, the Potente heuristic does not require any experimentation and recommends parameter values with competitive quality.

Externe Organisation(en)

Universität Paderborn
Ludwig-Maximilians-Universität München (LMU)

Typ

Artikel

Journal

Welding in the world

Band

66

Seiten

2157-2170

Anzahl der Seiten

14

ISSN

0043-2288

Publikationsdatum

10.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Werkstoffmechanik, Maschinenbau, Metalle und Legierungen

Elektronische Version(en)

https://doi.org/10.1007/s40194-022-01339-9 (Zugang: Offen)