EURO 2025 Leeds
Abstract Submission

23. Optimal Grouping and Placement of Composite Parts in an Autoclave

Invited abstract in session WC-38: Optimization in Online Environments, stream Data Science meets Optimization.

Wednesday, 12:30-14:00
Room: Michael Sadler LG19

Authors (first author is the speaker)

1. Tugce Yucel
Industrial Engineering, Turkish Aerospace Instution
2. Diclehan Tezcaner Öztürk
Industrial Engineering, Hacettepe University
3. Murat Caner Testik
Industrial Engineering, Hacettepe University

Abstract

Composite materials used in the aviation industry undergo a curing process in heat and pressure ovens called autoclaves. In a curing cycle, all parts that are loaded in the autoclave are heated until they reach their curing temperature, cured at that temperature for a while, and then cooled down to room temperature. The heating phase ends when all parts reach the curing temperature, which is mainly affected by how the parts are located in the autoclave. In this study, we address the heating phase of this cycle, and aim to keep its duration at minimum by placing the parts optimally in the autoclave. For the autoclave and parts considered in this study, there are no established relations between the location of the parts in the autoclave and their heating durations. In our approach, we first estimate the heating durations of parts with respect to their placements in the autoclave and the properties of the batch (like the weight, length of parts) with an Artificial Neural Network (ANN) Model. We then develop a mixed integer nonlinear programming model that groups the parts into batches, and finds the best layout for each batch in the autoclave using the estimations of the ANN Model. We test our approach on real data.

Keywords

Status: accepted

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