Making impressions in patients with microstomia is often rather problematic due to their restricted mouth opening. Herein, this report describes a novel digital workflow for making impressions with computer‐aided design and computer‐aided manufacturing (CAD/CAM) custom sectional trays for a 58‐year‐old female patient with scleroderma and microstomia. CAD/CAM custom sectional trays were made based on digital dentition models from another case with similar arch scale. After the sectional impressions were obtained, the sectional casts were scanned and digitally aligned to form the final dentition models. The removable partial dentures were designed on the final digital models and printed using a 3D printer. This procedure was executed with a successful prosthetic outcome that included good fit and acceptable esthetics. The patient also reported a high level of satisfaction.
The cloud manufacturing platform needs to allocate the endlessly emerging tasks to the resources scattered in different places for processing. However, this real-time scheduling problem in the cloud environment is more complicated than that in a traditional workshop because constraints, such as type matching, task precedence, resource occupation, and logistics duration, need to be met, and the internal manufacturing plan of providers must also be considered. Since the platform aggregates massive manufacturing resources to serve large-scale manufacturing tasks, the space of feasible solutions is huge, resulting in many conventional search algorithms no longer being applicable. In this paper, we considered resource allocation as the key procedure for real-time scheduling, and an ANN (Artificial Neural Network) based model is established to predict the task completion status for resource allocation among candidates. The trained ANN model has high prediction accuracy, and the ANN-based scheduling approach performs better than the preferred method in terms of the optimization objectives, including total cost, service satisfaction, and make-span. In addition, the proposed approach has potential in the application for smart manufacturing or Industry 4.0 because of its high response performance and good scalability.
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