Submissions

Topic: Tribology: Friction, Wear, and Lubrication

Studying friction, wear, and lubrication is very important in practice because many mechanical, electrical, and biological systems function on the basis of appropriate friction and wear values. In recent decades, tribology has attracted more attention. Many tribology manifestations are beneficial and actually make modern life possible. However, many other effects of tribology cause serious damage and require careful design to overcome damages caused by excessive friction or wear. Overall, friction uses, or wastes, a significant amount of human energy, while a large amount of production capacity is devoted to replacing the objects that are useless due to wear.

This theme collects papers relating to friction, wear, and lubrication. The subjects covered include solid contact, external friction coefficients, and preliminary displacement, the fundamental principles of friction physics, wear rate, and computation of tribological joints for wear, lubrication, mechanical measurement in tribology, and tribological problems in mechanical engineering.

Topic: Fracture Mechanics and Fatigue Analysis

Fracture mechanics and fatigue analysis are essential to understanding the structural performance of real materials. Fracture mechanics is the study of complex stress fields around cracks to determine whether existing cracks are spreading or reversing. Fatigue analysis is the study of fracture behavior under repeated cyclic loads. 

This topic collection is aimed to emphasize the application of fracture mechanics to prevent fractures and fatigue failures in structures. The topics include stress analysis for crack members, resistance forces, fatigue crack initiation, and readiness for service.

Topic: Recent Advances in Mobile Robotics Navigation

Autonomous navigation is the intersection of many different basic research areas in robotics. The smooth movement of artificial agents, without collision, attracts large amounts of attention and research and has led to hundreds of approaches over decades. Moving the robot from one position to another requires a variety of perception techniques, state estimation techniques (e.g., positioning, mapping, global representation), path planning, motion planning, and control techniques.

A fully autonomous mobile robot has the ability to:
• Gain information about the environment.
• Work for an extended period without human intervention.
• Move either all or part of itself throughout its operating environment without human assistance.
• Avoid situations that are harmful to people, property, or itself, unless those are part of its design specifications. An autonomous mobile robot may also learn or gain new capabilities like adjusting strategies for accomplishing its task(s) or adapting to changing surroundings.

This topic aims to present current and innovative research which contributes to the improvement of the navigation abilities of mobile robots.

Topic: Autonomous Robots in Industry 4.0

Industry 4.0, also known as the Fourth Industrial Revolution, is transforming how we do business. One of the key technologies driving this revolution is autonomous robots. These robots have the ability to operate independently and make decisions based on their environment, without the need for human intervention. This topic explores the opportunities and challenges of using autonomous robots in Industry 4.0.

Topics of Interest include, but are not limited to:

• Applications of autonomous robots in manufacturing, logistics, and other industries
• The Impact of autonomous robots on productivity, safety, and quality in the workplace
• The role of artificial intelligence and machine learning in enabling autonomous robots
• Ethical considerations in the use of autonomous robots, including issues related to job displacement and privacy
• Strategies for integrating autonomous robots into existing production systems
• The evolution of autonomous robot technology and its potential future applications

We welcome contributions from researchers, practitioners, and policymakers in the field of autonomous robots and Industry 4.0.

Topic: Mechanical Fault Diagnosis Based on Transfer Learning

Mechanical fault diagnosis is an important method to accurately identify the health condition of mechanical equipment and ensure its safe operation. Fault diagnosis based on transfer learning usually refers to the diagnostic method verified in simulation or laboratory that can be generalized to actual operating equipment. The implementation methods of transfer learning include feature-based transfer, model-based transfer, etc., through enhancement, fine-tuning and other technical techniques.

This topic introduces the principle of transfer learning, summarizes the research and application of transfer learning in the field of fault diagnosis, discusses the shortcomings of transfer learning in the field of fault diagnosis, and discusses the future research direction of transfer learning in the field of fault diagnosis.

Topic: Recent Innovations in Mechanical Vibrations

Mechanical vibrations encompass the study of oscillatory motions of mechanical systems and structures. This topic collection aims to highlight recent innovations and advancements in the field, covering new methodologies, analytical tools, and applications that enhance our understanding and management of mechanical vibrations

It includes, but is not limited to, the following key areas:

Advanced Theoretical Models and Computational Techniques

• New theoretical frameworks for understanding complex, non-linear vibration behaviors
• Multiscale Modeling
• High-Fidelity Computational Fluid Dynamics (CFD)

Precision Measurement and Data Analysis

• Smart Sensing Technologies
• High-Resolution Signal Processing
• Artificial Intelligence and Machine Learning

Innovative Control and Mitigation Strategies

• Smart Materials and Adaptive Structures
• Hybrid Active-Passive Control Systems
• Condition-Based Maintenance and Health Monitoring

Selected Papers from Conferences

This section will publish selected papers from conference.