Enhancing Manufacturing Quality Prediction Models through Explainability Methods
Utilizing explainability techniques to enhance machine learning models for quality prediction in manufacturing processes.
Additive Manufacturing and Deep Learning: Current Progress and Challenges
Deep learning is revolutionizing additive manufacturing by addressing complex challenges and improving processes.
Manufacturing Service Capability Prediction with Graph Neural Networks
Data-driven solutions using Graph Neural Networks enhance accuracy in identifying manufacturing service capabilities.
DeepMachining: Online Prediction of Lathe Machine Errors with Deep Learning
DeepMachining utilizes deep learning to predict lathe machine errors accurately.
DeepMachining: Online Prediction of Lathe Machine Errors
DeepMachining utilizes deep learning for accurate online prediction of lathe machine errors.
Open Stamped Parts Dataset: Defect Detection in Auto Manufacturing
Advancing defect detection in auto manufacturing using the Open Stamped Parts Dataset.
Solving Distributed Flexible Job Shop Scheduling Problems in the Wool Textile Industry with Quantum Annealing
In solving Distributed Flexible Job Shop Scheduling Problems in the Wool Textile Industry, Quantum Annealing offers a promising approach to optimize production planning. The authors explore the applicability of Quantum Annealing to large problem instances specific to the industry.
Advancing Additive Manufacturing through Deep Learning: Current Progress and Future Challenges Revealed
The author explores the potential of deep learning in advancing additive manufacturing, highlighting its ability to address complex challenges and improve the AM process.