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RISA Digital Twin–Based Environment-Adaptive Assignment Method for Human–Robot Collaboration
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Human–robot collaboration, which strives to combine the best skills of humans and robots, has shown board application prospects in meeting safe–effective–flexible requirements in various fields. The ideation of much closer ...
Digital Twin-Enabled Temporal Uncertainty Mitigation for Human–Robot Collaborative Assembly in Distributed Control System
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Abstract. Human–machine collaboration is an effective means to perform complex tasks in manufacturing. However, in distributed control systems that require temporal certainty, the temporal uncertainty in human–machine ...
Robust Scheduling Based on Deep Reinforcement Learning for Flexible Job Shop With Machine Breakdown and New Job Arrival
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Abstract. With the continuous growth of personalized product demands and the upsurge in new customer orders, the arrival of new jobs and machine breakdowns due to overloading have emerged as common production disturbances, ...
An Extensible Model for Multitask-Oriented Service Composition and Scheduling in Cloud Manufacturing
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Cloud manufacturing is an emerging novel business paradigm for the manufacturing industry. In cloud manufacturing, distributed manufacturing resources are encapsulated into services and aggregated in a cloud manufacturing ...
Partial/Parallel Disassembly Sequence Planning for Complex Products
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Disassembly is a very important step in recycling and maintenance, particularly for energy saving. However, disassembly sequence planning (DSP) is a challenging combinatorial optimization problem due to complex constraints ...
A Cooperative Co-Evolutionary Algorithm for Large-Scale Process Planning With Energy Consideration
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Process planning can be an effective way to improve the energy efficiency of production processes. Aimed at reducing both energy consumption and processing time (PT), a comprehensive approach that considers feature sequencing, ...
Learning Nonlinear Constitutive Laws Using Neural Network Models Based on Indirectly Measurable Data
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Artificial neural network (ANN) models are used to learn the nonlinear constitutive laws based on indirectly measurable data. The real input and output of the ANN model are derived from indirect data using a mechanical ...
Failure Prognosis of Complex Equipment With Multistream Deep Recurrent Neural Network
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: The failure prognosis is crucial for industrial equipment in prognostics and health management field. The vibration signal is the commonly used data for failure prognosis. The conventional prognostic approaches have ...
Data and Model Synergy-Driven Rolling Bearings Remaining Useful Life Prediction Approach Based on Deep Neural Network and Wiener Process
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Various remaining useful life (RUL) prediction methods, encompassing model-based, data-driven, and hybrid methods, have been developed and successfully applied to prognostics and health management for diverse rolling ...
Energy-Aware Material Selection for Product With Multicomponent Under Cloud Environment
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Energy consumption in manufacturing has risen to be a global concern. Material selection in the product design phase is of great significance to energy conservation and emission reduction. However, because of the limitation ...