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In addition to research papers, the Design Society is developing several valuable resources for those interested in the study of design. These include a repository of PhD theses, a library of case studies and transcripts of design activities, and an archive of our newsletters. Please note that these resources are accessible exclusively to Design Society members.

Automatic assessment of rust level on screws using convolutional neural networks

Mandolini, Marco (1); Manuguerra, Luca (1); Dimanche, Sylvain (2); Formentini, Giovanni (3)

Type:
Papers
DS 145: Proceedings of the DESIGN2026 19th International Design Conference
Year:
2026
Editor:
Štorga, M.; Škec, S.; Martinec, T.; Marjanović, D.; Pavković, N.
Author:
Mandolini, Marco (1); Manuguerra, Luca (1); Dimanche, Sylvain (2); Formentini, Giovanni (3)
Series:
DESIGN
Institution:
1: Università Politecnica delle Marche, Italy; 2: Polytech Marseille, France; 3: Circular Momentum, Denmark
Page(s):
2443-2452
DOI number:
10.1017/pds.2026.10602
ISSN:
2732-527X (Online)
Abstract:
This paper presents a deep learning-based approach to automatically classify the rust level of screws using ResNet-18 and MobileNetV3 convolutional neural networks. A controlled salt-spray chamber was used to simulate corrosion on metal screws over 0h, 48h, 96h, and 168h of exposure. Images were processed with a circle-detection algorithm to extract individual screws, followed by data augmentation and training. The final models achieved a classification accuracy greater than 94% on the validation set.
Keywords:
artificial intelligence (AI), sustainable design, design for disassembly, deep learning
DESIGN2026 - The 19th International Design Conference
Conferences

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