In 2021, sales of electric bicycles in Germany increased compared to the previous year, leading to a 43% share of electric bicycles in the total German bicycle market. The German Bicycle Industry Association (ZIV) expects this growth to continue and that by 2025 every second bicycle sold in Germany will have an electric motor. Therefore, due to the increasing number of sales, both the built-in resources and the possible treatments after the use phase need to be considered regarding sustainable market development. In this context, remanufacturing is one promising end-of-life strategy that enables a new life cycle of products and components, thus reducing the consumption of natural resources and minimizing waste production. The used products are restored to their original conditions by disassembling, cleaning, sorting, reworking, and reassembling. Disassembly is a decisive process step as it creates the prerequisites for all further steps in the process chain and significantly determines the economic feasibility of a remanufacturing process. Therefore, this paper aims to evaluate the ease of disassembly of electric bicycle motors and thus determine their suitability for remanufacturing. To evaluate the disassemblability, the ease of Disassembly Metric (eDiM), a quantitative assessment method for evaluating disassembly effort, was adapted to the needs of the electric bicycle motors. The method was finally applied to five electric bicycle motors from established manufacturers and assessed their ease of disassembly. The results show that the electric bicycle motors can be disassembled without significant damage and classified as suitable for remanufacturing.

Remanufacturing is one key element of a circular economy by closing the loop on the product level and thus maintaining or restoring the product design and the associated product properties. The remanufacturing process chain involves disassembly of used products, cleaning of parts, inspection and sorting of parts, reconditioning or replenishment by new parts, and product reassembly into as-new products. If new parts are required, additive manufacturing is a promising alternative to conventional manufacturing or the purchase of spare parts. Additive manufacturing is characterized by the layered or element-based construction of parts and does not require product-specific tools, enabling a cost-efficient production of individual pieces or small series. The use of specific design rules in product and process development to meet the requirements of the intended process enables and simplifies additive manufacturing or remanufacturing. Despite the design rules for additive manufacturing and remanufacturing, there are no design rules for implementing additive manufacturing technology in the remanufacturing process. In this paper, existing design rules on Design for Additive Manufacturing and Design for Remanufacturing will first be identified and compared, and possible synergies and conflicts of objectives will be analyzed. Based on this, a guideline for a Design for Additive Remanufacturing is developed to facilitate and promote the implementation of additive manufacturing in remanufacturing. The developed design rules enable the evaluation of a part aimed to be produced by additive manufacturing within the remanufacturing process and give advice on how to optimize the design of the part. This paper aims to derive general design rules for a “Design for Additive Remanufacturing” that specifically address the additive remanufacturing process.

Remanufacturing plays a key role in the circular economy by reducing material consumption and extending product life cycles. However, a major challenge in remanufacturing is accurately forecasting the availability of cores, particularly regarding their quantity, timing, and condition. Although machine learning (ML) offers promising approaches for addressing this challenge, there is limited clarity on which influencing factors are most critical and which ML approaches are best suited to remanufacturing-specific forecasting tasks. This study addresses this gap through a mixed-method approach combining expert interviews with two systematic literature reviews. The interviews with professionals from remanufacturing companies identified key influencing factors affecting product returns, which were structured into an adapted Ishikawa diagram. In parallel, the literature reviews analyzed 125 peer-reviewed publications on ML-based forecasting in related domains—specifically, spare parts logistics and manufacturing quality prediction. The review categorized data sources into real-world, simulated, and benchmark datasets and examined commonly applied ML models, including traditional methods and deep learning architectures. The findings highlight transferable methodologies and critical gaps, particularly a lack of remanufacturing-specific datasets and integrated models. This study contributes a structured overview of ML forecasting in remanufacturing and outlines future research directions for enhancing predictive accuracy and practical applicability.

Remanufacturing, a key element of the circular economy, enables products and parts to have new life cycles through a systematic process. Initially, used products (cores) are visually inspected and categorized according to their manufacturer and variant before being disassembled and cleaned. Subsequently, parts are manually classified as directly reusable, reusable after reconditioning, or recyclable. As demand for remanufactured parts increases, automated classification becomes crucial. However, current Deep Learning (DL) methods, constrained by the scarcity of unique parts, often suffer from insufficient datasets, leading to overfitting. This research explores the effectiveness of Data-Efficient Generative Adversarial Network (DE-GAN) optimization approaches like FastGAN, APA, and InsGen in enhancing dataset diversity. These methods were evaluated against the State-of-the-Art (SOTA) Deep Convolutional Generative Adversarial Network (DCGAN) using metrics such as the Inception Score (IS), Fréchet Inception Distance (FID), and the classification accuracy of ResNet18 models trained with partially synthetic data. FastGAN achieved the lowest FID values among all models and led to a statistically significant improvement in ResNet18 classification accuracy. At a [1:1] real-to-synthetic ratio, the mean accuracy increased from 72% ± 4% (real-data-only) to 87% ± 3% (p < 0.001), and reached 94% ± 3% after hyperparameter optimization. In contrast, synthetic data generated by the SOTA DCGAN did not yield statistically significant improvements.

Remanufacturing gewinnt im Kontext von Ressourcenknappheit und Nachhaltigkeit weiter an Bedeutung. Forschung und Kompetenzentwicklung am realen Objekt sind dabei zentral, um die spezifischen Herausforderungen zu bewältigen. Im RemanLab wird das etablierte Konzept der Lernfabrik auf das Remanufacturing übertragen und die Prozesskette praxisnah vermittelt. Dies ermöglicht Wissenstransfer, Produktbewertungen und die Entwicklung effizienter Prozessketten, insbesondere mittels digitaler Technologien.