## 4 Literature Review
### Quality Control  
Quality control in remanufacturing involves assessing and validating the condition of returned components or remanufactured products to ensure they meet specified standards before reprocessing or market release. Remanufacturing models in the literature often assume quality to be homogeneous; however, it has wide variations due to customer usage, usage length, and special product characteristics [133]. These variations make inspection processes in remanufacturing labor-intensive and time-consuming. Advanced computational methods, such as object detection and defect identification, can aid in evaluating the condition, reusability, and quality grade of returned cores, addressing the high uncertainty and subjective bias associated with manual assessments [134]. In this section, we review and categorize the literature on the use of data-driven and ML methods for automating remanufacturing quality control. These technologies help overcome issues related to individual subjectivity, time constraints, and high labor costs by excelling in learning complex geometries and patterns [135]. [PERSON] et al. highlighted the challenges of high uncertainty in the inspection process related to cores and addressed these by utilizing RL models to capture cores, and an unsupervised learning model for anomaly detection [32], as demonstrated in Figure 10. The figure demonstrates the model capability of handling uncertainty in remanufacturing through its adaptable architecture. As shown in the figure, the model starts with processing, comparing the core's expected and inspected conditions. Deviations are flagged for review. Sensors capture data during the perception stage, which the quality controller analyzes for defects. Finally, during decision-making, the system decides if the core is reusable or should be rejected, automating the entire inspection process. Few-shot learning  
Figure 9: Flowchart of adaptive (re)scheduling strategy (taken from [101]). techniques were employed to categorize anomalies and to precisely assess the core's quality grade [32]. [PERSON] et al. presented a novel inspection process with a deep convolution neural network for mild steel plates to detect eight fault conditions and their combinations [105]. The high accuracy of the model on a small dataset demonstrated its robustness and efficiency, making it an ideal solution for smart inspection strategy in remanufacturing. [PERSON] et al. presented an automated sorting system utilizing a smart conveyor with multiple cameras, reflective sensors, and a PC running Python applications. It leverages inception transfer learning for image classification and the YOLO model for object detection, ensuring robust and high-accuracy identification and sorting of remanufacturing parts through the combination of classifiers [106]. [PERSON] et al. used a Gaussian process regression model to predict the performance of ultrasonically welded joints and unanticipated process variation based on the process inputs and feedback (integrated sensor data) [107]. The proposed method, capable of detecting process variations and anomalies, has proven effective in both manufacturing and remanufacturing environments. It enhances quality control by identifying anomalies throughout operations and enabling informed decisions regarding the reusability and remanufacturability of cores and parts. In addition to implementing an effective inspection strategy for cores and remanufactured products, it is crucial to monitor various processes within remanufacturing operations. Statistical process control is a valuable tool for achieving process stability and reducing variability [136]. It facilitates continuous improvement and enhances quality assurance throughout the operations [137], which can be significantly augmented by ML models. ML excels in pattern recognition, allowing control charts to detect complex patterns, automate root cause analysis, and examine relationships between process data [108].