أدوات قرار للذكاء الاصطناعي المادي

مكتبة الموارد

ملاحظات ميدانية وأطر تقييم وقوالب تنفيذية لتحويل تجارب الذكاء الاصطناعي الصناعي إلى منتجات قابلة للنشر والتشغيل والوثوق.

Start with your decision

Choose the question in front of you. Each path starts with three useful references; expand it for the full reading list.

تحليل مستند إلى المصادر

قانون الذكاء الاصطناعي الأوروبي للذكاء الاصطناعي المادي

يعتمد التاريخ المنطبق على كيفية تصنيف النظام. اقرأ التحليل الحالي، ثم حدّد الاستخدام المقصود وحدود النظام والالتزامات قبل اعتبار أي تاريخ موعدًا نهائيًا.

اقرأ التحليلاستكشف دعم القرار
جميع الموارد
جميع الفئات

جميع الموارد

22

  • الأدلة

    تقني

    Ollama and Strix Halo: Local AI Setup and Evaluation

    A documentation-based guide to local setup, AMD acceleration, runtime choices and the evidence needed before an industrial rollout. No measured hardware results are claimed.

  • القوالب

    الاستراتيجية

    Worked Product System Evidence Pack

    Inspect a fictional cobot decision, evidence gaps and linked Product System exports. Modeled inputs and accountable roles remain distinct from recorded human approval.

  • القوالب

    الاستراتيجية

    Worked Industrial AI Acceptance Pack

    Fictional worked example of an Industrial AI acceptance pack: unplanned-stop triage on one packaging line, with measures, costs and the decision.

  • القوالب

    الاستراتيجية

    Worked Commercial Product Decision

    Fictional worked example of a commercial product decision: segment, price logic and channel for an AMR fleet, with its stop conditions.

  • القوالب

    الاستراتيجية

    Physical AI Lifecycle Economics Stress Test

    Compare a fictional baseline with five declared stresses using the existing simulator. Inspect capital, support, recovery and payback assumptions; these are modeled scenarios, not forecasts.

  • قوائم المراجعة

    الاستراتيجية

    Intralogistics Procurement Evidence Checklist

    Sixteen evidence requests before a fleet commitment, with accountable roles, acceptance questions and hold conditions. Every request starts unassessed; no supplier score or approval is implied.

  • الأدلة

    الاستراتيجية

    Physical AI Product Management 2027: Research Protocol

    The proposed study question, exploratory design, sample targets, consent boundaries and pre-launch gates. Recruitment has not started and no findings exist.

  • الأدلة

    تقني

    Deploying Mistral On-Prem for Manufacturing — Sovereign, Air-Gapped AI

    How manufacturers deploy Mistral AI on-premise and in air-gapped environments. Covers the Sovereign Model Ladder, Mistral Forge/Studio/Compute stack, use cases across aerospace, automotive, semiconductors, and energy, and EU AI Act compliance.

  • الأدلة

    تقني

    Mistral vs OpenAI vs Anthropic for Industrial & Sovereign AI

    Honest side-by-side comparison of Mistral, OpenAI GPT, and Anthropic Claude for industrial and sovereign AI workloads. Covers data residency, on-prem deployment, fine-tunability, cost at scale, capability ceiling, vendor lock-in, and EU AI Act fit — plus when frontier models genuinely win.

  • الأدلة

    تقني

    Predictive Maintenance AI for Industrial Equipment: Condition Monitoring, RUL & ROI

    How to build predictive maintenance for production equipment: the data foundation (vibration, thermal, and motor-current signatures over OPC-UA and time-series), modeling approaches (anomaly detection, remaining-useful-life, survival models), edge vs cloud inference, CMMS/SCADA integration, and how to quantify ROI (downtime avoided, MTBF). Framed against ISO 13374 condition monitoring and IEC 62443 OT security. Ties to the live CSV-maintenance demo on this site.

  • الأدلة

    المعايير

    AI Visual Quality Inspection for Manufacturing: Computer-Vision Defect Detection

    How to deploy computer vision quality inspection on the production line: surface defects, assembly/completeness, and weld inspection — sensor and lighting setup, dataset and annotation strategy, and edge deployment. Includes the honesty boundary: a vision model surfaces candidate indications mapped to a vocabulary like ISO 5817 weld imperfections — it does not assign a certified grade (that needs metrology and your WPS). Ties to the live plant-audit and defect demos on this site.

  • الأدلة

    تقني

    Sim-to-Real for Industrial Robotics: Evidence Before Release

    A product-decision guide to simulation assumptions, transfer tests, target hardware, policy authority, independent checks, recovery and field evidence. Compares simulator and policy choices without treating simulation performance as release proof.

  • الأدلة

    السلامة

    Edge AI Under Functional Safety: Deploying AI in ISO 26262 & IEC 62443 Systems

    Deploying AI in safety-critical embedded systems: ISO 26262 ASIL levels, SOTIF (ISO 21448), IEC 61508 SIL, IEC 62443 OT cybersecurity, runtime assurance monitors, operational design domains, and the edge inference toolchain (ONNX/TensorRT).

  • الأدلة

    تقني

    Digital Twin ROI Playbook: From OPC-UA to a Production Twin

    A practical ROI framework for industrial digital-twin programmes: the data foundation (PLC → OPC-UA → time-series → twin), the five-rung maturity ladder, where AI enters (anomaly detection, predictive maintenance, process optimization), how to quantify ROI, and build-vs-buy guidance for manufacturing leaders.

  • الأدلة

    المعايير

    AI for Aerospace & Defence-Adjacent Manufacturing: A Civil-First, Dual-Use Primer

    How MRO operators, avionics suppliers, and UAV makers deploy AI for predictive maintenance, automated NDT, and documentation copilots — with a clear-eyed view of DO-178C / DO-254 / ARP4754A certification, EASA's learning-assurance roadmap, and the sovereign on-prem case. Civil-first; no defence contracts or clearances.

  • الأدلة

    المعايير

    Auralink: A Physical AI Research Reference Architecture

    A Hyperion-authored Physical AI research reference for EV-charging infrastructure, evaluated in simulation and bounded environments—not a live deployment or client-outcome claim.

  • الأدلة

    الاستراتيجية

    Physical AI Product Teardowns

    Three shipped physical-AI product families read for the decisions behind them — EV charging reliability, Android Automotive inside a homologated vehicle, and video platforms at 100M+ scale. Public observation, not client case studies.

  • الأدلة

    الاستراتيجية

    The Prototype-to-Product Decision Gate

    Five decisions that determine whether a Physical AI prototype becomes a product — degraded-mode ownership, variant count, field survival, the scale delta, and who signs the compliance case. Each gate carries the test that answers it and the failure it prevents.

  • الأدلة

    الاستراتيجية

    Physical AI Product Management: The Complete Guide

    The operating reference for AI products that perceive, decide and act: strategy, discovery, productization, evidence, operating envelopes, safety, platforms, launch, field operations, metrics and scale.

  • الأدلة

    الاستراتيجية

    Physical AI Product Manager: Role Charter & Hiring Scorecard

    A practical role definition for Physical AI product leaders: decision accountabilities, specialist boundaries, seniority ladder, hiring scorecard, interview loop, first-90-days plan and copyable role brief.

  • القوالب

    السلامة

    مجاني مع البريد الإلكتروني

    Safety-Case Evidence Template (ISO 26262 / DO-178C / IEC 61508)

    A practitioner template for structuring an AI safety case: the Claims–Arguments–Evidence skeleton, a HARA input sheet, ASIL/DAL/SIL decomposition placeholders, and a V&V traceability matrix — mapped to ISO 26262, DO-178C and IEC 61508. It is a template and checklist; a notified/certification body assigns the actual rating, we engineer the evidence.

  • القوالب

    المعايير

    مجاني مع البريد الإلكتروني

    Edge AI Vendor Evaluation Matrix

    A vendor-neutral scoring matrix for evaluating edge-AI inference hardware and vendors: TOPS/W, P99 latency, ONNX/TensorRT-class format support, on-prem/sovereign deployment, toolchain maturity, functional-safety support, power/thermal envelope, and long-term support — with a weighted 1–5 rubric. Criteria and a scoring method, not a ranked vendor list.

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