Abstract. Bone drilling is an essential part of many orthopaedic surgery procedures, including those for internal fixation and for attaching prosthetics. Estimation and control of bone drilling forces are critical to prevent drill breakthrough, excessive heat generation, and mechanical damage to the bone. This paper presents a 3D finite element (FE) model for prediction of thrust forces experienced during bone drilling. The model incorporates the dynamic characteristics involved in the process along with the accurate geometrical considerations. The average critical thrust forces and torques obtained using FE analysis, for set of machining parameters are found to be in good agreement with the experimental results.
The last decade saw the emergence of highly autonomous, flexible, re-configurable Cyber-Physical Systems. Research in this domain has been enhanced by the use of high-fidelity simulations, including Digital Twins, which are virtual representations connected to real assets. Digital Twins have been used for process supervision, prediction, or interaction with physical assets. Interaction with Digital Twins is enhanced by Virtual Reality and Augmented Reality, and Industry 5.0-focused research is evolving with the involvement of the human aspect in Digital Twins. This paper aims to review recent research on Human-Centric Digital Twins (HCDTs) and their enabling technologies. A systematic literature review is performed using the VOSviewer keyword mapping technique. Current technologies such as motion sensors, biological sensors, computational intelligence, simulation, and visualization tools are studied for the development of HCDTs in promising application areas. Domain-specific frameworks and guidelines are formed for different HCDT applications that highlight the workflow and desired outcomes, such as the training of AI models, the optimization of ergonomics, the security policy, task allocation, etc. A guideline and comparative analysis for the effective development of HCDTs are created based on the criteria of Machine Learning requirements, sensors, interfaces, and Human Digital Twin inputs.
Human workers are envisioned to work alongside robots and other intelligent factory modules, and fulfill supervision tasks in future smart factories. Technological developments, during the last few years, in the field of smart factory automation have introduced the concept of cyber-physical systems, which further expanded to cyber-physical production systems. In this context, the role of collaborative robots is significant and depends largely on the advanced capabilities of collision detection, impedance control, and learning new tasks based on artificial intelligence. The system components, collaborative robots, and humans need to communicate for collective decision-making. This requires processing of shared information keeping in consideration the available knowledge, reasoning, and flexible systems that are resilient to the real-time dynamic changes on the industry floor as well as within the communication and computer network infrastructure. This article presents an ontology-based approach to solve industrial scenarios for safety applications in cyber-physical production systems. A case study of an industrial scenario is presented to validate the approach in which visual cues are used to detect and react to dynamic changes in real time. Multiple scenarios are tested for simultaneous detection and prioritization to enhance the learning surface of the intelligent production system with the goal to automate safety-based decisions.
Ionic polymer metal composites (IPMCs) are an emerging class of electroactive polymers (EAP), which have many potential applications as sensors and actuators. Recently, IPMCs have been intensively studied because of their huge potential in medical, mechanical, electronic, and aerospace engineering. However, before the benefits of these materials can be effectively exploited for practical use, a mathematical model must be established to enhance understanding and predictability of IPMC actuation. The coupled electrical-chemical-mechanical response of an IPMC depends on the structure of the polyelectrolyte membrane, the morphology and conductivity of the metal electrodes, the cation properties, and the level of hydration. With this in mind, the purpose of this study is to establish a finite element model for bending behavior of IPMC beams. With reference to their operation principle, it is assumed that an IPMC beam has three virtual layers. We draw an analogy between thermal strain and real strain in IPMC due to volume change. This is a coupled structure/thermal model, and the finite element method is used to solve this model. The ion concentration distribution in the IPMC boundary layer is mimicked with the temperature distribution, and the electromechanical coupling coefficient is mimicked with the thermal expansion coefficient. Theoretical and experimental results demonstrate that our suggested model is practical and effective enough in predicting the blocking force of IPMC strips for different input voltages and strip thicknesses.
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