A Digital Twin Framework of a Material Handling Operator in Industry 4.0 Environments
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The manufacturing and construction industries around the globe have poor occupational health and safety records. Slip & fall, manual material handling (MMH) moves, and forklift accidents are the top three causes for warehouse injuries. Statistics from the U.S. Department of Labor, Bureau of Labor Statistics show that in manufacturing industries, musculoskeletal disorders accounted for 34% of the "Days Away from Work" cases in 2017. Sprains, strains, and tears accounted for the leading type of injury in the manufacturing industry. This research presents a digital twin (DT) approach to assess fatigue in human operators in the material handling industry. DT is an advanced simulation tool that is an exact representation of a physical object. For data collection and analysis, a simulation-based framework is presented. The proposed methodology consists of three modules: Data Collection, Operator Analysis & Feedback, and Digital Twin Development. An optical motion capture system helps develop the DT, which captures simulated material handling activities similar to those in an actual environment. For a pilot study, participants were selected from the university population to perform a series of 'lifting' MMH activities. The participants' physical attributes, body kinematics, and their rating of perceived exertion were measured throughout the experiment. Fatigue was measured as a factor in the subjects' joint angles and analyzed via a dynamic time warping algorithm. To identify the accumulation of biomechanical fatigue, we use an exponentially weighted moving average control chart.
This research aims to conceptualize a DT of an operator and propose a tool that enables the understanding and analysis of the factors that influence human variability and error while performing MMH tasks. The proposed methodology was able to detect biomechanical fatigue in subjects performing MMH tasks and justify the need for a true DT of an operator for fatigue evaluation in the Industry 4.0 era.