Mukd-482 _top_ Jun 2026

| Feature | Benefit | |---------|----------| | (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). |

Once I have more context, I'll do my best to assist you. MUKD-482

MUKD-482

| | 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 | |---------|----------| | (optional)

| # | Requirement | Target | |---|--------------|--------| | | 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). | | | Rapid‑Drain Valve | Reduces drying time

| # | As a… | I want to… | So that… | |---|--------|-----------|----------| | | Content author | See tag suggestions while typing the article body or title. | I don’t have to think about the taxonomy and can keep my focus on writing. | | US‑2 | Content author | Accept a suggestion with a single click or keyboard shortcut (e.g., Enter ). | Tagging is fast and frictionless. | | US‑3 | Content author | Dismiss a suggestion ( Esc or ❌) and optionally provide a reason (e.g., “Irrelevant”). | The system learns from my feedback and improves future suggestions. | | US‑4 | Editor | Review a “suggestion log” that shows which AI‑suggested tags were accepted/rejected for each article. | I can audit tagging quality and override if needed. | | US‑5 | Product analyst | Export tagging‑accuracy reports (acceptance rate, precision/recall) per taxonomy branch. | I can gauge the health of the taxonomy and the AI model. | | US‑6 | System (backend) | Store author‑feedback events in the analytics pipeline for model retraining. | The AI model continuously improves without manual re‑labeling. | | US‑7 | Platform admin | Configure which taxonomies are exposed to the suggestion engine (e.g., enable/disable certain tag groups). | We can roll out gradually or limit suggestions for sensitive domains. |