TY - JOUR

T1 - A Framework for Anomaly Classification and Segmentation in Remanufacturing using Autoencoders and Simulated Data

AU - Kaiser, Jan Philipp

AU - Enslin, Carl Leandro

AU - Barczak, Erik Tabuchi

AU - Stamer, Florian

AU - Heizmann, Michael

AU - Lanza, Gisela

N1 - Conference code: 17

PY - 2024

Y1 - 2024

N2 - In remanufacturing, reusable end-of-life products must be separated from those for which remanufacturing is not economical. In this process, the focus is initially on the detection and evaluation of surface defects of the used product. Due to the use phase of the product, these show a great variety with strongly differing degrees of severity. Deep-learning-based autoencoders offer the advantage of defect-independent detection of anomalies by detecting deviations from a defect-free product condition. In the present work, anomaly detection based on synthetically generated image data is investigated. In addition, a framework for classification and segmentation is developed and presented. The results show that the approach presented in the paper is effective in detecting anomalies with potential for improvement through adjustments such as perceptual loss or image processing methods. However, challenges remain in accurately detecting anomalies in the presence of missing parts, large anomalies, and image data with high variance.

AB - In remanufacturing, reusable end-of-life products must be separated from those for which remanufacturing is not economical. In this process, the focus is initially on the detection and evaluation of surface defects of the used product. Due to the use phase of the product, these show a great variety with strongly differing degrees of severity. Deep-learning-based autoencoders offer the advantage of defect-independent detection of anomalies by detecting deviations from a defect-free product condition. In the present work, anomaly detection based on synthetically generated image data is investigated. In addition, a framework for classification and segmentation is developed and presented. The results show that the approach presented in the paper is effective in detecting anomalies with potential for improvement through adjustments such as perceptual loss or image processing methods. However, challenges remain in accurately detecting anomalies in the presence of missing parts, large anomalies, and image data with high variance.

KW - Anomaly Detection

KW - Convolutional Autoencoder

KW - Remanufacturing

KW - Unsupervised Learning

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85208581634&partnerID=8YFLogxK

U2 - 10.1016/j.procir.2024.08.383

DO - 10.1016/j.procir.2024.08.383

M3 - Conference article in journal

AN - SCOPUS:85208581634

VL - 126

SP - 1005

EP - 1010

JO - Procedia CIRP

JF - Procedia CIRP

SN - 2212-8271

T2 - 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME 2023

Y2 - 12 July 2023 through 14 July 2023

ER -