Abstract
AbstractHeated 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.
Funder
Deutsche Forschungsgemeinschaft
Universität Paderborn
Publisher
Springer Science and Business Media LLC
Subject
Metals and Alloys,Mechanical Engineering,Mechanics of Materials
Reference25 articles.
1. Stokes VK (1989) Joining methods for plastics and plastic composites: an overview. Polymer Engineering & Science 29(19):1310–1324. https://doi.org/10.1002/pen.760291903
2. Potente H, Tappe P (1984) Heated tool-butt welding of polyethylene-pipes—welding parameters and testing technique. Materials & Design 5(6):273–280
3. Gunst RF, Mason RL (2009) Fractional factorial design. WIREs Computational Statistics 1 (2):234–244. https://doi.org/10.1002/wics.27
4. Bischl B, Binder M, Lang M, Pielok T, Richter J, Coors S, Thomas J, Ullmann T, Becker M, Boulesteix A, Deng D, Lindauer M (2021) Hyperparameter optimization: foundations, algorithms, best practices and open challenges. arXiv:2107.05847
5. Feurer M, Hutter F (2019) Hyperparameter optimization. In: Hutter F, Kotthoff L, Vanschoren J (eds) Automated machine learning - methods, systems, challenges. the springer series on challenges in machine learning. Springer, pp 3–33. https://doi.org/10.1007/978-3-030-05318-5∖_1