YaBeSH Digital Library: Recent submissions
Now showing items 1121-1140 of 316660
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An Approach for Straightening Force Prediction of Continuous Casting Machine Segment Based on MAML-LSTM
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. Segment is a key component of a continuous casting machine, which frequently experiences faults. The straightening force is an important indicator for the working state of the continuous casting machine, effectively ... -
A Hybrid Physical Damage Neural Network for Wear Prediction of Self-Lubricating Bearings
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. Self-lubricating bearings are widely used in aerospace, marine, and other fields due to their excellent performance. Accurate wear prediction for self-lubricating bearings is crucial for ensuring reliability and ... -
Graph-Based Knowledge-Integrated Deep Reinforcement Learning for Dynamic Multi-Objective Reentrant Hybrid Flow-Shop Scheduling Problem With Batch Processing Machines
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. The reentrant hybrid flow-shop scheduling problem with batch processing machines (RHFSP-BPMs), characterized by reentrant routing and batch processing, is widely observed in industrial settings such as electronics ... -
Transformer-Fused Level Set UNet3+ for Monitoring of Iron Ore Pellet Size Distribution
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. Iron ore pellets are one of the main raw materials in the metallurgical industry. To ensure the utilization rate of raw materials and the efficiency of industrial processes, it is often necessary to control the ... -
A Physics-Informed Deep Encoding-Parsing Network for Cross-Domain Bearing Fault Diagnosis Under Noisy Sensor Data
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. Fault diagnosis based on bearing vibration signals is an important approach to improving the operational safety of complex equipment. However, existing zero-shot fault diagnosis methods across operating conditions ... -
A Physics-Informed Multimodal Transformer Model for Machining Energy Consumption Prediction
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. Machining energy consumption (MEC) prediction plays an important role in energy planning, management, and conservation in the manufacturing industry. Existing MEC prediction techniques are commonly classified ... -
Synergizing Global Prototypes and Local Topology: A Dual-Branch Network for Robust Few-Shot Learning
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. Few-shot fault diagnosis presents significant challenges due to sample scarcity and the difficulty in extracting discriminative features. To address these issues, this article proposes a dual-branch integrated ... -
A Knowledge-Driven Framework for Automated Datum System Design in Body-in-White Assembly
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. The design of datum systems for body-in-white (BIW) assembly is critical but reliant on expert experience, leading to inefficiency and inconsistency. This article presents a knowledge-driven framework to automate ... -
Agent-Based Resilience Analysis: A Domain-Agnostic Framework for System Degradation and Recovery Analysis
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. This article introduces a domain-agnostic simulation framework for evaluating the resilience and robustness of network-based systems subjected to degradation and partial recovery. Built upon a previously developed ... -
Nanomaterial-Enhanced Greases: A Review of Tribological and Rheological Advancement
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. Lubricating greases play a key role in reducing friction and wear across a wide range of mechanical systems. Yet, conventional formulations often fall short when exposed to high temperatures or harsh operating ... -
GPU-Based Global Optimization for Engineering Design
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. Design optimization has important applications in many engineering fields, where the goal is to find the best design within the available means. In many practical applications, finding the best design can bring ... -
Large Language Model-Augmented Semantic Digital Twins for Real-Time Fault Diagnosis and Closed-Loop Control
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. Modern manufacturing systems demand cognitive digital twins (CDTs) capable of not only mirroring physical processes but also interpreting context and reasoning about system behavior. Traditional digital twins ... -
Deep Reinforcement Learning for Helicopter Assembly Workshop Scheduling Considering the Workers' Operational Proficiency
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. As a critical stage in helicopter manufacturing, the assembly process relies on effective scheduling to ensure both efficiency and quality. Traditional, experience-based scheduling methods are often inadequate ... -
Memory-Augmented Prediction With Cross-Iteration Alignment for Anomaly Signal Detection in the Integrated Test of Aerospace Products
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. Anomaly detection in integrated test signals is crucial for ensuring the reliability and safety of aerospace products. However, this task faces significant challenges due to the scarcity of anomalous samples, the ... -
An Enhanced Deep Reinforcement Learning Approach to Motion Planning With Knowledge Transfer and Online Demonstrations
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. Deep reinforcement learning is now widely applied in motion planning problems for autonomous systems due to its model-free nature and its ability to solve complex control problems through trial and error. However, ... -
Generating Explainable and Verified Product Information Models From Industrial Documentation
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. The design and management of complex products require engineers to synthesize vast amounts of information from disparate sources like design manuals, specification sheets, and technical reports. Manually constructing ... -
A Large Language Model-Enhanced Knowledge Graph Multi-Hop Reasoning Method for Assembly Process Question–Answering
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. In the assembly process design, knowledge question–answering is a crucial scenario for promoting the sharing of knowledge resources and enhancing the process design accuracy and efficiency. Simultaneously, knowledge ... -
User Needs Analysis in Design Research by Using Large Language Models as Interviewees
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. The advent of generative artificial intelligence (AI), such as large language models (LLMs), brings a vast range of possibilities and concerns for engineering design. The speed and efficiency of generative AI ... -
Generative Artificial Intelligence for Interpretable Satisficing Solution Design in Manufacturing
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. In multi-objective manufacturing, designers face increasing difficulty in visualizing and interpreting tradeoffs as the number of goals and decision variables grows. Traditional visualization methods offer intuitive ... -
Generative Model Predictive Control in Manufacturing Processes: A Review
(The American Society of Mechanical Engineers (ASME), 2026)Abstract. Manufacturing processes are inherently dynamic and uncertain, with varying parameters and nonlinear behaviors, making robust control essential for maintaining quality and reliability. Traditional control methods ...