###### Abstract  
Exponentially growing technologies such as intelligent robots in the context of Industry 4.0 are radically changing traditional manufacturing to intelligent manufacturing with increased productivity and flexibility. Workspaces are being transformed into fully shared spaces for performing tasks during human-robot collaboration (HRC), increasing the possibility of accidents as compared to the fully restricted and partially shared workspaces. The next technological epoch of Industry 5.0 has a heavy focus on human well-being, with humans and robots operating in synergy. However, the reluctance to adopt heavy-payload-capacity robots due to safety concerns is a major hurdle. Therefore, the importance of analyzing the level of injury after impact can never be neglected for the safety of workers and for designing a collaborative environment. In this study, quasi-static and dynamic analyses of accidental scenarios during HRC are performed for medium- and low-payload-capacity robots according to the conditions given in ISO TS 15066 to assess the threshold level of injury and pain, and is subsequently extended for high speeds and heavy payloads for collaborative robots. For this purpose, accidental scenarios are simulated in ANSYS using a 3D finite element model of an adult human index finger and hand, composed of cortical bone and soft tissue. Stresses and strains in the bone and tissue, and contact forces and energy transfer during impact are studied, and contact speed limit values are estimated. It is observed that heavy-payload-capacity robots must be restricted to 80% of the speed limit of low-payload-capacity robots. Biomechanical modeling of accident scenarios offers insights and, therefore, gives confidence in the adoption of heavy-payload robots in factories of the future. The analysis allows for prediction and assessment of different hypothetical accidental scenarios in HRC involving high speeds and heavy-payload-capacity robots. Industry 4.0; human-robot collaboration; occupational safety; static and dynamic analysis Article  
A Computational Assessment for Heavy-Payload-Capacity Robots  
[PERSON]  
that completely stop the machine in case of any workspace violation. This causes resetting and stoppage actions to be triggered and hinders productive time [3]. With the advent of Industry 4.0, there has been a paradigm shift from automated manufacturing to intelligent manufacturing concepts [4]. In Industry 4.0, industrial robots have shifted from isolation into a collaborative work environment, where humans and robots share workspaces fully without any barriers, termed human-robot collaboration (HRC) [5,6]. A collaborative work environment is a core part of the manufacturing setups involved in Industry 4.0 where both humans and robots are pooling up their specific capabilities. Hand guiding, speed and separation monitoring, and power and force limiting are the level of HRC with partially separated, completely restricted, and fully shared workspaces, respectively. Direct deployment of intelligent robots in fully shared workspaces in HRC increases the possibility of accidents. There is extensive literature on using advances in Artificial Intelligence for safe Human-Robot Interaction to avoid collision, using computer vision [7], an array of sensors and techniques such as Reinforcement Learning [8,9], Digital Twins, and Deep Learning [10]. However, in case of any type of robot failure or cyber-attack intended to cause harm [11], the large amount of impact forces created during accidents can create minor, moderate, and major injury to the human workers. There is also the consideration that converting present industrial robots into interactive ones for safe HRC further increases the risk of accidents [12,13]. Therefore, in the factory of the future, safety will be an intrinsic part of the Collaborative Robotic Cyber Physical Systems.