Browser-native digital model of a front-load washing machine: anisotropic suspension physics and phyphox smartphone validation

This paper presents a browser-native digital model of a domestic front-load washing machine, implemented in WebGL/Three.js with a 100 Hz Euler-step physics engine. The model incorporates a six-parameter anisotropic suspension scheme capturing viscous and Coulomb damping mechanisms, alongside a corrected excitation force-vector plane tailored specifically to front-load drum geometry. Validation against physical vibration data acquired via a smartphone IMU (Phyphox platform) demonstrates exact peak-amplitude agreement on the reference Sway axis (0% error) and 1.3% error on the Axial axis, with Bounce-axis discrepancies attributed to non-linear Coulomb damping complexities. Classified as a Kritzinger Level 1 digital model, this zero-install simulation eliminates software overhead to facilitate immediate deployment for suspension-dynamics demonstration and fault-injection studies. The primary novelty resides in three combined contributions: a physically motivated front-load rotor excitation correction, an axis-dependent six-parameter anisotropic damping formulation, and rigorous vibration characterisation achieved solely through consumer smartphone sensing without specialist laboratory metrology. While evaluated as a single-machine proof-of-concept requiring broader statistical replication across diverse loading conditions, this validated parametric model establishes a foundational architecture for future digital twin evolution and real-time anomaly detection upon integrating live sensor telemetry.