Manual disassembly of the lithium-ion battery (LIB) modules of electric vehicles (EVs) for recycling is time-consuming, expensive, and dangerous for technicians or workers. Dangers associated with high voltage and thermal runaway make a robotic system suitable for the automated or semi-automated disassembly of EV batteries. In this paper, we explore battery disassembly using industrial robots. To understand the disassembly process, human workers were monitored, and the operations were analyzed and broken down into gripping and cutting operations. These operations were selected for automation, and path planning was performed offline. For the gripper, a linear quadratic regulator (LQR) control system was implemented. A system identification method was also implemented in the form of a batch least squares estimator to form the state space representation of the planar linkages used in the control strategy of the gripper. A high-speed rotary cut-off wheel was adapted for the robot to perform precise cutting at various points in the battery module case. The simulation results were used to program an industrial robot for experimental validation. The precision of the rotary cutter allowed for a more direct disassembly method as opposed to the standard manual method. It was shown that the robot was almost twice as fast in cutting but slower in pick and place operations. It has been shown that the best option for disassembly of a LIB pack is a human–robot collaboration, where the robot could make efficient cuts on the battery pack and the technician could quickly sort the battery components and remove connectors or fasteners with which the robot would struggle. This collaboration also reduces the danger encountered by the technician because the risk of shorting battery cells while cutting would be eliminated, but the time efficiency would be significantly improved. This paper demonstrates that a robot offers both safety and time improvements to the current manual disassembly process for EV LIBs.

Since the first successful on-orbit repair mission in 1984 to the Solar Maximum Mission (SMM) satellite, considerable progress has been made in the field of On-orbit Servicing, Assembly, and Manufacturing (OSAM) of spacecraft using either human-guided or autonomous robots. This article aims to provide a review of state-of-the-art efforts in this field and the necessary technologies to achieve the ultimate objective of autonomous spacecraft repairs while in orbit. The article covers the literature relevant to OSAM, including a brief overview of OSAM, inspection technologies, manufacturing and repair technologies, state-of-the-art robotic technologies capable of performing the required tasks, and intelligent path planning of robots. The article also highlights the research’s location, timeframe, and public versus private sector efforts, and outlines future directions in this field. This article aims to foster a community of researchers and public-private partnerships working towards the common objective of autonomous robotic inspection and repair of on-orbit spacecraft.

Ureteral peristalsis can be considered as a series of waves on the ureteral wall, which transfers the urine along the ureter toward the bladder. The stones that form in the kidney and migrate to the ureter can create a substantial health problem due to the pain caused by interaction of the ureteral walls and stones during the peristaltic motion. Three-dimensional (3D) computational fluid dynamics (CFD) simulations were carried out using the commercial code ansys fluent to solve for the peristaltic movement of the ureter, with and without stones. The effect of stone size was considered through the investigation of varying obstructions of 5%, 15%, and 35% for fixed spherical stone shape. Also, an understanding of the effect of stone shape was obtained through separate CFD calculations of the peristaltic ureter with three different types of stones, a sphere, a cube, and a star, all at a fixed obstruction percentage of 15%. Velocity vectors, mass flow rates, pressure gradients, and wall shear stresses were analyzed along one bolus of urine during peristalsis of the ureteral wall to study the various effects. It was found that the increase in obstruction increased the backflow, pressure gradients, and wall shear stresses proximal to the stone. On the other hand, with regard to the stone shape study, while the cube-shaped stones resulted in the largest backflow, the star-shaped stone showed highest pressure gradient magnitudes. Interestingly, the change in stone shape did not have a significant effect on the wall shear stress at the obstruction level studied here.

Advances in sensing technologies are increasingly transforming cutting operations by enabling data-driven condition monitoring, predictive maintenance, and process optimization. This review surveys recent developments in sensing modalities for cutting systems, including vibration sensors, acoustic emission sensors, optical and vision-based systems, eddy-current sensors, force sensors, and emerging hybrid/multi-modal sensing frameworks. Each sensing approach offers unique advantages in capturing mechanical, acoustic, geometric, or electromagnetic signatures related to tool wear, process instability, and fault development, while also showing modality-specific limitations such as noise sensitivity, environmental robustness, and integration complexity. Recent trends show a growing shift toward hybrid and multi-modal sensor fusion, where data from multiple sensors are combined using advanced data analytics and machine learning to improve diagnostic accuracy and reliability under changing cutting conditions. The review also discusses how artificial intelligence, Internet of Things connectivity, and edge computing enable scalable, real-time monitoring solutions, along with the challenges related to data needs, computational costs, and system integration. Future directions highlight the importance of robust fusion architectures, physics-informed and explainable models, digital twin integration, and cost-effective sensor deployment to accelerate adoption across various manufacturing environments. Overall, these advancements position advanced sensing and hybrid monitoring strategies as key drivers of intelligent, Industry 4.0-oriented cutting processes.

This paper describes the development of intelligent sensors as part of an integrated systems approach. The integrated systems approach treats the sensor as a complete system with its own sensing hardware (the traditional sensor), A/D converter, processing and storage capabilities, software drivers, self-assessment algorithms and communication protocols. The immediate application is the monitoring of rocket test stands, but the technology should be generally applicable to the Integrated Systems Health Monitoring (ISHM) vision. This paper outlines progress made in the development of intelligent sensors by describing the work done to date on Physical Intelligent Sensors (PIS) and Virtual Intelligent Sensors (VIS). The PIS as discussed here consists of a thermocouple used to read temperature in an analog form which is then converted into digital values. A microprocessor collects the sensor readings and runs numerous embedded event detection routines on the digital data. If any event, i.e. spike, drift, noise, is detected, it is reported, stored and sent to a remote system through an Ethernet connection. Hence the output of the PIS is data coupled with a confidence factor in the reliability of the data. The VIS discussed here is a virtual implantation of the PIS in C++. The VIS is designed to mirror the operations of the PIS; however, the VIS works on a computer at which digital data is provided as the input and is thus portable to any sensor system. This work lays the foundation for the next generation of smart devices that have embedded intelligence for distributed decision making capabilities.