Mukd-482

| Pros | Cons | |----------|----------| | High cleaning efficiency at relatively low power consumption. | Initial cost is higher than basic ultrasonic cleaners. | | Flexible frequency & temperature options cover a broad range of materials. | Requires periodic maintenance of transducer surfaces. | | Robust safety interlocks reduce risk of overheating or dry‑run. | The 4 L tank may be limiting for very large batch jobs (though expansion kits are available). | | Easy integration into automated production lines via Modbus. | Noise level, while reduced, is still noticeable in very quiet environments. | | Compact footprint for a 250 W unit. | Learning curve for advanced programming (but the UI is intuitive). |

| Feature | Benefit | |---------|----------| | Dual‑Frequency Capability (optional) | Allows fine‑tuning for different part geometries; 80 kHz reduces cavitation intensity for fragile items. | | Integrated Heater with PID Control | Maintains a stable temperature, dramatically improving cleaning efficiency for greases and fluxes. | | Programmable Cycle Profiles | Up to 10 user‑defined programs (time, temperature, power) saved directly on the unit. | | Rapid‑Drain Valve | Reduces drying time and prevents re‑contamination. | | Modular Tank Inserts | Swap‑in acrylic or stainless‑steel baskets for easy loading of PCBs, connectors, or small machined parts. | | Noise Dampening Enclosure | Acoustic insulation reduces operating noise to <55 dB(A). |

MUKD-482 is a blueprint for a modular edge appliance that balances performance, security, and operational manageability for modern distributed AI use cases. The right combination of hardware accelerators, a minimal secure OS, standardized model formats (ONNX), and robust lifecycle tooling delivers reliable, low-latency intelligence at the edge while minimizing cloud dependency and protecting sensitive data. MUKD-482

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Feel free to edit, add details, or ask for clarification on any section. | Pros | Cons | |----------|----------| | High

| Question | Owner | Due | |----------|-------|-----| | What is the exact versioning strategy for the taxonomy (e.g., semantic version vs. timestamp)? | Taxonomy Team | End of Sprint 1 | | Do we need a “soft‑delete” of suggested tags for compliance (e.g., after article deletion)? | Legal / Compliance | Sprint 2 | | Should we expose confidence scores to the author (e.g., tooltip) or keep them hidden? | UX Lead | Sprint 4 (design review) | | What is the budget for the GPU inference nodes (if needed)? | Engineering Ops | Sprint 2 | | Do we need multilingual support for suggestions (currently English only)? | Product | Sprint 3 (scope) |

| Frequency | Task | |-----------|------| | After Every Use | Drain tank, rinse with fresh water, wipe exterior. | | Weekly | Inspect and clean the ultrasonic transducers with a soft brush. | | Monthly | Check heater element for scaling; descale if needed (use a mild citric solution). | | Quarterly | Verify temperature sensor accuracy; recalibrate if deviation > 2 °C. | | Annually | Perform a full electrical safety inspection (ground continuity, fuse condition). | MUKD-482 is a blueprint for a modular edge

The MUKD‑482 is the latest entry in the “MUKD” series of modular ultrasonic cleaning devices, targeting both industrial and laboratory environments. Designed to balance high performance with user‑friendly operation, the unit is especially popular among precision‑manufacturing facilities, PCB rework stations, and research labs that require reliable, repeatable cleaning of delicate components.

| # | Requirement | Target | |---|--------------|--------| | NFR‑1 | Performance | 95 % of suggestion requests respond ≤ 250 ms (cold model load excluded). | | NFR‑2 | Scalability | Service must handle up to 5 k concurrent author sessions (≈ 50 k req/min) with horizontal pod autoscaling. | | NFR‑3 | Availability | 99.9 % monthly uptime (excluding planned maintenance). | | NFR‑4 | Observability | Export Prometheus metrics (request_latency, error_rate, model_version). | | NFR‑5 | Data retention | Feedback events kept 180 days for model retraining, then archived. | | NFR‑6 | Compliance | GDPR‑ready – ability to delete all events linked to a specific userId on request. | | NFR‑7 | Maintainability | Model versioning stored in MLflow; CI/CD pipeline must run unit, integration, and performance tests on each push. | | NFR‑8 | Usability | Minimum 2 seconds to first suggestion after article load (including network). | | NFR‑9 | Accessibility | UI must be WCAG 2.1 AA (focusable, screen‑reader friendly, high‑contrast). |