In aerospace, medicine, disaster response, and laboratory work, one missed part, reversed component, or premature closure can turn a manual assembly error into a safety failure. MagTrace-MX is a camera-free smart work surface with chip-free tags that verifies the physical state of high-consequence assemblies directly at the workstation. In prototype testing, it produced zero final false passes across 4,200 induced critical-error trials covering missing items, wrong items, wrong zones, reversed orientations, post-verification removals, premature seals, and unknown metal distractors.

The working prototype uses an active 8 x 8 time-division multiplexed array of planar inductive sensing cells under a cleanable 240 mm x 240 mm work surface. Dummy border rows improve edge-field symmetry, while gapped shielding reduces parasitic coupling without creating shorted-turn damping paths. Components are placed in cassettes, sleeves, or package backers containing passive, silicon-free inductor-capacitor (LC) spatial tags. These tags require no batteries or integrated circuits and are designed for roll-to-roll printed-electronics manufacturing. A standardized binary spatial layout encodes item identity and orientation, with compensating trace geometry reducing resonance shifts during tag coding.

When a component is placed, its tag couples with the near-field matrix. Operating in a 6.4 to 7.2 MHz dither band, the prototype produced calibrated continuous feature maps from normalized in-phase/quadrature response channels, equivalent real/imaginary response features, settling behavior, and cell-to-cell coupling signatures. These features separate sharp resonant LC tag signatures from broad, lossy metal clutter such as tools, foil-backed packaging, or workspace debris.

To avoid false classification from a worker's hand, a temporal hysteresis gate waits until transient contact effects stabilize. The stable-state tensor is normalized at the edge using candidate-profile and null-baseline scaling, including a numerical stability epsilon as shown in the pipeline diagram. A local quantized Tiny Machine Learning (TinyML) decoder running on embedded hardware then extracts item identity, X/Y zone location, axial orientation, lift-tolerant placement state, and anomaly probability from continuous normalized feature channels rather than raw phase angle.

The neural network does not approve the assembly. Its decoded features feed a deterministic workflow state engine that checks ID, zone, orientation, sequence, removal, and closure state. If the model is uncertain or an anomaly exceeds threshold, the engine defaults to fail/lockout. A tamper-evident resonant seal is accepted only after the stored workflow template is physically satisfied.

Across three workflow templates (emergency-kit, electronics-tray, and field-lab assembly), and as compiled in Figure A, MagTrace-MX achieved 99% or higher model-level tag identity, zone localization, and axial-orientation classification; 0.5% or lower model-level metal-distractor false accepts before deterministic workflow veto; median feedback latency below 200 ms; and less than 5% workflow-time overhead. The deterministic state engine prevented model-level uncertainty or anomaly detections from becoming final system false passes, preserving the zero-final-false-pass result across all 4,200 induced critical-error trials.

Unlike computer vision, MagTrace-MX does not depend on lighting or line of sight. Unlike RFID inventory tracking, it verifies location, orientation, sequence, removal, and closure directly at the work surface. The same architecture can extend from emergency kits to electronics trays, aerospace fastener staging, automotive wire-harness sequencing, cleanroom tool monitoring, and high-security logistics.