Nestability: A deep learning oracle for nesting scrap prediction in manufacturing industry

• Published in: Resources, conservation and recycling Vol. 205; p. 107540
• Main Authors: Abdou, Kirolos, Schaaf, Nina, Struckmeier, Frederick, Braun, Jannik, Bhat Keelanje Srinivas, Pavan, Ottnad, Jens, Huber, Marco F.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2024
• Subjects: Artificial intelligence; Autoencoder; Nesting-based manufacturing; Neural networks; Scrap prediction; Sustainable production; Scrap prediction; Sustainable production; Nesting-based manufacturing; Neural networks; Autoencoder; Artificial intelligence
• ISSN: 0921-3449; 1879-0658
• DOI: 10.1016/j.resconrec.2024.107540

In the quest for sustainable manufacturing, a critical yet often overlooked aspect is predicting the scrap in nesting-based manufacturing. This aspect is pivotal for its eco-friendly implications and potential to enhance cost-efficiency and competitiveness in the manufacturing industry. This paper introduces a method to predict scrap generation in nesting-based manufacturing solely based on part geometries without prior knowledge of the resulting arrangement. Our method employs three deep learning modules: one for encoding geometries, another to condense these into an information vector reflecting interdependence for scrap prediction, and a third for the actual prediction using the prior modules. Evaluated on a private real-world sheet metal dataset, the technique achieves a Mean Absolute Percentage Error (MAPE) of 24.8%, showcasing its precision in scrap prediction. Such predictions can bolster sustainable manufacturing by curtailing material waste, improving production efficiency, and enhancing eco-sustainability. The paper underscores scrap prediction's role in advancing sustainable manufacturing in nesting-based industries.