Deep learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, object detection and localisation, natural language processing, prediction and forecasting systems. With significant applicability, deep learning could be used in new and broader areas of applications, including remanufacturing. Remanufacturing is a process of taking used products through disassembly, inspection, cleaning, reconditioning, reassembly and testing to ascertain that their condition meets new products conditions with warranty. This process is complex and requires a good understanding of the respective stages for proper analysis. Inspection is a critical process in remanufacturing, which guarantees the quality of the remanufactured products. It is currently an expensive manual operation in the remanufacturing process that depends on operator expertise, in most cases. This research investigates the application of deep learning algorithms to inspection in remanufacturing, towards automating the inspection process. This paper presents a novel vision-based inspection system based on deep convolution neural network (DCNN) for eight types of defects, namely pitting, rust, cracks and other combination faults. The materials used for this feasibility study were 100 cm × 150 cm mild steel plate material, purchased locally, and captured using a USB webcam of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies used in the design of inspection systems. This research is the first to apply deep learning techniques in remanufacturing inspection. The proposed method offers the potential to eliminate expert judgement in inspection, save cost, increase throughput and improve precision. This preliminary study demonstrates that deep learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of deep learning algorithms to remanufacturing.

While many developing countries may not afford state-of-the-art medical equipment, they may take advantage of the significant price reduction and other benefits of remanufacturing to solve their perennial healthcare problems that are aggravated by the shortage of medical equipment. As a first step towards implementing medical equipment remanufacturing in developing countries, the regulatory perspectives which plays a crucial role in the industry should be understood. However, since regulation of medical equipment is weak or inexistent in most developing countries, the regulatory perspectives with respect to remanufacturing or related activities in both the European Union (EU) and the United States of America (US) are first examined to determine their impacts. Unfortunately, there appears to be a lack of precise definition of remanufacturing for medical devices. An unambiguous definition is necessary to promote effective research, improve understanding, ensure uniformity of standards, drive quacks out of the remanufacturing market and thus, enhance customer confidence in remanufactured products. This paper proposes a definition for medical equipment remanufacture. The principal advantage of this definition is that it could be adopted in future research toward increasing access to functional medical equipment to developing countries through remanufacturing.

The availability of medical equipment contributes significantly to the stability and sustainability of health care systems. However, in some countries, especially the developing ones, medical equipment availability is a major issue that remains unsolved. Hence, this paper explores the root causes of the issue, reviews existing solution approaches and suggests remanufacturing as a sustainable option. An extensive review was first conducted to uncover key factors contributing to the poor availability of medical equipment in developing countries. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was then used to measure the prominence degrees of the key factors and characterise these factors with an aim to differentiate those that are net drivers from those that are driven. Subsequently, factors that can be addressed by remanufacturing were identified, to determine the potential contribution of remanufacturing in addressing the poor medical equipment availability issue. The result shows that remanufacturing can potentially address at least five of the key factors which account for a cumulative total prominence of 43.5%. Remanufacturing is thus, a viable strategy for improving medical equipment availability in developing countries. In addition to remanufacturing, other recommendations were also proposed to help address the issue.

Remanufacturing is a crucial component of the circular economy concept which emphasises sustainable consumption habits. This study proposes a novel automated sorting system for remanufacturing which is based on deep convolutional neural networks(CNN). To demonstrate its applicability, the proposed deep learning (DL) system was used to distinguish among dry, wet, oily and defected surfaces. The test was conducted on four locally sourced 3" x 6 " plates. Sample image data were captured using a USB webcam. The network training was done with 75% of the data while the balance data were used for testing. In this preliminary study, the DCNN classified the features with up to 99.74% accuracy on validation data and above 96% accuracy on live video feed; demonstrating that it can accurately sort components. This study is the first to propose a low-cost sorting system for remanufacturing based on the deep CNN and logic gates. The results show that the method is an accurate, reliable, cost-effective and fast technique that can potentially outperform existing sorting systems in the remanufacturing industry.