The recent increase in computational capability has led to an unprecedented increase in the range of new applications where machine learning can be used in real time. Notwithstanding the range of use cases where automation is now feasible, humans are likely to retain a critical role in the operation and certification of manufacturing systems for the foreseeable future. This paper presents a use case review of how human operators affect the performance of cyber–physical systems within a ’smart’ or ’cognitive’ setting. Such applications are classified using Industry 4.0 (I4.0) or 5.0 (I5.0) terminology. The authors argue that, as there is often no general agreement as to when a specific use case moves from being an I4.0 to an I5.0 example, the use of a hybrid Industry X.0 notation at the intersection between I4.0 and I5.0 is warranted. Through a structured review of the literature, the focus is on how secure human-mediated autonomous production can be performed most effectively to augment and optimise machine operation.

Sampling and analysis occur along the milk processing train: from collection at farm level, to intake at the diary plant, the processing steps, and the end products. Milk has a short shelf life; however, products such as milk powders have allowed a global industry to be developed. Quality control tests are vital to support activities for hygiene and food standards to meet regulatory and customer demands. Multiples of chemical and microbiological contamination tests are undertaken. Hazard analysis testing strategies are necessary, but some tests may be redundant; it is therefore vital to identify product optimization quality control strategies. The time taken to undergo testing and turnaround time are rarely measured. The dairy industry is a traditional industry with a low margin commodity. Industry 4.0 vision for dairy manufacturing is to introduce the aspects of operational excellence and implementation of information and communications technologies. The dairy industries’ reply to Industry 4.0 is represented predominantly by proactive maintenance and optimization of production and logistical chains, such as robotic milking machines and processing and packaging line automation reinforced by sensors for rapid chemical and microbial analysis with improved and real-time data management. This chapter reviews the processing trains with suggestions for improved optimization.

Deep learning based visual cognition has greatly improved the accuracy of defect detection, reducing processing times and increasing product throughput across a variety of manufacturing use cases. There is however a continuing need for rigorous procedures to dynamically update model-based detection methods that use sequential streaming during the training phase. This paper reviews how new process, training or validation information is rigorously incorporated in real time when detection exceptions arise during inspection. In particular, consideration is given to how new tasks, classes or decision pathways are added to existing models or datasets in a controlled fashion. An analysis of studies from the incremental learning literature is presented, where the emphasis is on the mitigation of process complexity challenges such as, catastrophic forgetting. Further, practical implementation issues that are known to affect the complexity of deep learning model architecture, including memory allocation for incoming sequential data or incremental learning accuracy, is considered. The paper highlights case study results and methods that have been used to successfully mitigate such real-time manufacturing challenges.

This work demonstrates the use of Deep Learning-based Computer Vision for Remanufacturing end-of-life consumer electronics products, considering smartphones as the use-case. We implemented automated detection of screen defects such as scratches and cracks. In turn, this could lead to increased reuse of smartphones in a secondary market alongside new ones to meet consumer demand. The refurbishment of smartphone devices is a growing industry heavily dependent on manual labor, making decisions subjective, especially in grading the severity of damage. A critical analysis of defect detection and smartphone grading from a remanufacturing perspective is conducted. This analysis is significant in a remanufacturing sector that deals with dynamically varying input of cores (used products for remanufacturing), characteristics, timing, and number of returns. The solution we propose here is novel in its own right, requiring data analysis and collection, data curing, defect parameterization, and dataset building to enable model-based training and detection experiments. We collected and annotated a dataset to detect and grade the various defects based on their severity. A range of deep learning models was trained on the dataset to obtain baseline results for the state-of-the-art deep learning detection models, including YOLOv7, YOLOv8, YOLO11 variants, and Mask R-CNN. Our experiments also showed improved precision values when the pre-trained models were pre-fine-tuned using a road crack segmentation dataset before training on our phone defect dataset. The inference time for the YOLOv8x model is 8ms. This reduced inference time with a high precision of 70.4% indicates that a consistent, fast, and accurate grading is achieved here, ensuring a high throughput rate in the remanufacturing process and ensuring sustainability.