Prescriptive Digital Twins and Explainable AI for Human-Centric Industrial Automation

In the transition toward Industry 5.0, physical human–robot interaction (pHRI) operates as an open bio-technical system far from thermodynamic equilibrium. Traditional industrial automation relies on quasi-static anthropometry and post-hoc checklists, failing to capture continuous neuro-kinematic instability and the systemic cost of assembly defects. This study develops and computationally benchmarks a closed-loop Prescriptive Digital Twin (PDT) that unifies non-linear biomechanics, Explainable Artificial Intelligence (XAI), and cyber–physical shop-floor execution. Leveraging open-source empirical kinematics (N = 8151 observations), the edge sensory tier reconstructs the human motor manifold via Takens delay coordinate embedding. By coupling Largest Lyapunov Exponents (LyE) with fixed-grid Shannon entropy (H), the system detects orbital trajectory divergence and compensatory muscular stiffening in real time. Rather than operating as an opaque black box, a game-theoretic TreeSHAP supervisory engine decomposes failure risk within 2.12 ms, autonomously dispatching multi-tier prescriptive policies: dynamic FABRIK-A* Cartesian handover waypoint elevation, non-linear hyperbolic tangent (tanh) cobot admittance damping, and asynchronous REST-API payloads to plant MES/ERP systems for line pacing and recovery scheduling. As an in silico computational proof-of-concept benchmarked through Latin Hypercube Sampling Monte Carlo simulations (n = 1000), prescriptive waypoint elevation restores spinal posture, reducing median L5/S1 compressive loading by 48.4% and eliminating NIOSH action limit exceedances (from 18.9% open-loop down to 0.0%), while attenuating cobot velocity tremor by 63.6%. The empirical input derives from a single healthy operator (N = 1) and the Monte Carlo analysis varies mechanical rather than biological parameters, so the findings establish computational feasibility, not population-level generalizability. While physical validation on shop-floor robotic hardware and empirical process-level Life Cycle Assessment (LCA) remain prospective, treating dynamic human biomechanical stability as an endogenous control parameter outlines a candidate architecture for vulnerable-worker protection and, prospectively, resource-efficient manufacturing.