This preprint is based on an accepted paper for the Annals of Scientific Society for Assembly, Handling, and Industrial Robotics 2023, and may undergo further revisions before the final publication. This preprint presents a groundbreaking research study that focuses on the application of incremental learning processes to enhance high-throughput product inspection and minimize false labeling in industrial environments. The paper tackles a critical challenge faced by industries today, which is the accurate identification and labeling of products during the inspection process. Traditional methods often fall short in maintaining high accuracy rates due to evolving production lines and ever-changing product variations. However, this preprint introduces an innovative approach that leverages incremental learning techniques to adapt and improve the inspection process continuously. By employing incremental learning, the proposed system can autonomously learn from new data and make adjustments to its inspection algorithms in real-time. This adaptive capability allows the system to keep up with changing product characteristics, ensuring accurate and reliable labeling throughout high-throughput operations. The result is a significant reduction in false labeling, leading to enhanced quality control and improved efficiency in industrial environments. We invite researchers, industry professionals, and enthusiasts in the field of assembly, handling, and industrial robotics to explore this preprint. The findings presented here have the potential to revolutionize the way product inspection is conducted in industrial settings, addressing a critical aspect of quality control. We encourage readers to engage in discussions, share feedback, and contribute to the advancement of this exciting research area. Abstract: In the circular economy, remanufacturing success relies heavily on the accurate identification and classification of used products. Processes, which rely on worker experience, lack objective validation, leading to the potential for mislabeling and inaccurate damage assessment. This, in turn, results in additional manual evaluations and unproductive costs, which run counter to the principles of sustainability. To address these issues, machine learning and artificial intelligence have been applied with promising results. However, producing reliable large amounts of labeled data remains a challenge, as workers are susceptible to human error. This paper addresses process design in production. It proposes a new design to ensure that only valid labels enter the prediction models, reducing the potential for false labels in the dataset. Through this, the aim is to improve the accuracy and reliability of remanufacturing, ultimately reducing costs and mitigating the carbon footprint in the manufacturing, repair, and maintenance industries.