بيت » أفضل موجهات الذكاء الاصطناعي للهندسة الميكانيكية

أفضل موجهات الذكاء الاصطناعي للهندسة الميكانيكية

الذكاء الاصطناعي يدفع الهندسة الميكانيكية
الهندسة الميكانيكية Ai
تعمل الأدوات التي تعتمد على الذكاء الاصطناعي على إحداث ثورة في الهندسة الميكانيكية من خلال تعزيز تحسين التصميم وسرعة المحاكاة والصيانة التنبؤية واختيار المواد من خلال تحليل البيانات المتقدم والتعرف على الأنماط.

تعمل أدوات الذكاء الاصطناعي عبر الإنترنت على إحداث تحول سريع في الهندسة الميكانيكية من خلال زيادة القدرات البشرية في التصميم والتحليل, التصنيعوالصيانة. يمكن لأنظمة الذكاء الاصطناعي هذه معالجة كميات هائلة من البيانات، وتحديد الأنماط المعقدة، وتوليد حلول جديدة أسرع بكثير من الطرق التقليدية. على سبيل المثال، يمكن أن يساعدك الذكاء الاصطناعي في تحسين التصاميم من حيث الأداء وقابلية التصنيع، وتسريع عمليات المحاكاة المعقدة، والتنبؤ بخصائص المواد، وأتمتة مجموعة واسعة من المهام التحليلية.

The prompts provided below will for example help on generative design, accelerate simulations (FEA/CFD), help on predictive maintenance where AI analyzes sensor data from machinery to forecast potential failures, enabling proactive servicing and minimizing downtime, help on  material selection and much more.

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موجه الذكاء الاصطناعي إلى Optimizing Wear Testing Protocol Variables

Analyzes a wear testing protocol for a mechanical component suggesting ways to reduce the number of variables or improve control over parameters to isolate specific effects and enhance test repeatability and reliability. This prompt aids in refining experimental setups for tribological studies. The output is a markdown formatted list of recommendations.

المخرجات: 

				
					Act as a Tribology Specialist with expertise in wear testing methodologies.
Your TASK is to analyze the provided `{wear_test_protocol_description_text}` for testing a component made of `{component_material_and_counterface_material}` (specify both
 e.g.
 'Component: Bearing Steel
 Counterface: Stainless Steel 304'). The aim is to suggest improvements for reducing uncontrolled variables
 isolating the effects of `{key_variables_being_investigated_list_csv}` (CSV: 'Variable_Name
Range_or_Levels')
 and enhancing overall test repeatability and reliability.

**RECOMMENDATIONS FOR WEAR TESTING PROTOCOL OPTIMIZATION (MUST be Markdown format):**

**1. Review of Current Protocol and Objectives:**
    *   **Understanding the Protocol**: Briefly summarize the core elements of the `{wear_test_protocol_description_text}` (e.g.
 'Pin-on-disk test
 10N load
 0.5 m/s sliding speed
 1000m distance
 ambient temperature
 dry contact').
    *   **Investigated Variables**: Clarify the specific variables from `{key_variables_being_investigated_list_csv}` that the protocol aims to study (e.g.
 'Effect of Load (5N
 10N
 15N)'
 'Effect of Lubricant Type (Oil A
 Oil B
 Dry)').
    *   **Materials**: `{component_material_and_counterface_material}`.

**2. Identification of Potential Uncontrolled or Confounding Variables:**
    Based on the protocol description
 identify factors that might not be explicitly controlled or could interfere with isolating the effects of the `{key_variables_being_investigated_list_csv}`. Examples:
    *   **Environmental Factors**:
        *   Temperature fluctuations (ambient vs. localized heating due to friction).
        *   Humidity variations.
        *   Contamination (dust
 debris from previous tests).
    *   **Specimen Preparation Inconsistencies**:
        *   Surface finish variations (initial roughness of component and counterface).
        *   Cleaning procedures before test.
        *   Specimen alignment and clamping.
    *   **Test Rig / Operational Factors**:
        *   Actual load application (static vs. dynamic components
 precise load control).
        *   Speed fluctuations.
        *   Vibration from the test rig or surroundings.
        *   Wear debris accumulation or removal during the test.
    *   **Measurement Inconsistencies (for wear quantification)**:
        *   Method of wear measurement (mass loss
 profilometry
 wear scar dimensions) and its precision/repeatability.
        *   Timing of measurements.

**3. Recommendations for Improving Control and Isolation of Variables:**
    *   **For each identified potential issue
 suggest specific improvements:**
        *   **Environmental Control**: 
            *   `e.g.
 Consider conducting tests in a temperature and humidity controlled chamber if feasible
 or at least monitor and record ambient conditions.`
            *   `e.g.
 Implement strict cleaning protocols for the test chamber and specimens between runs.`
        *   **Specimen Preparation Standardization**:
            *   `e.g.
 Define and adhere to a specific surface preparation procedure (e.g.
 grinding
 polishing to a consistent Ra value). Verify roughness before each test.`
            *   `e.g.
 Use standardized cleaning solvents and drying methods.`
            *   `e.g.
 Develop a fixture or procedure for consistent alignment.`
        *   **Test Rig Calibration and Monitoring**:
            *   `e.g.
 Regularly calibrate load cells
 speed sensors
 and environmental sensors.`
            *   `e.g.
 Monitor key parameters like load
 speed
 and friction coefficient IN-SITU if possible.`
        *   **Wear Debris Management**:
            *   `e.g.
 Decide on a strategy: either allow debris to accumulate naturally (if studying three-body wear is intended) or implement a method for controlled removal (e.g.
 periodic cleaning
 inert gas flow) if two-body abrasion is the focus. Document the choice.`
        *   **Standardized Wear Measurement**:
            *   `e.g.
 Clearly define the wear measurement technique
 including specific locations for profilometry scans or number of mass measurements. Calibrate measurement instruments.`
    *   **Isolating `{key_variables_being_investigated_list_csv}`**: 
        *   `e.g.
 When investigating 'Load'
 ensure ALL other parameters (speed
 environment
 lubricant if any
 material batch
 surface prep) are kept as constant as possible across different load levels.`
        *   `e.g.
 Use a full factorial or well-designed fractional factorial approach if multiple variables from the list are changed simultaneously to understand interactions.`

**4. Enhancing Repeatability and Reliability:**
    *   **Replicates**: `Perform multiple test runs (e.g.
 3-5 replicates) for each unique test condition to assess variability and calculate confidence intervals.`
    *   **Randomization**: `Randomize the order of test runs for different conditions to minimize systematic errors related to time or drift.`
    *   **Reference Runs**: `Periodically run a test with a standard reference material pair under fixed conditions to check for drift in the test rig performance.`

**IMPORTANT**: The goal is to make the experimental results more attributable to the `{key_variables_being_investigated_list_csv}` by minimizing other sources of variation.
							

موجه الذكاء الاصطناعي إلى مولد فرضية السبب الجذري للفشل

توجّه هذه المطالبة الذكاء الاصطناعي لتوليد فرضيات السبب الجذري المعقول لحدث عطل ميكانيكي بناءً على وصف تفصيلي للفشل والأعراض الملاحظة التي يقدمها المستخدم.

المخرجات: 

				
					Analyze the following mechanical failure description: {failure_description}, along with observed symptoms: {observed_symptoms}. Generate a list of 5 plausible root cause hypotheses ranked by likelihood. For each hypothesis, provide supporting rationale and suggest diagnostic tests or inspections to confirm or rule out the cause. Format the output as a numbered list with clear headings.
							

موجه الذكاء الاصطناعي إلى منشئ تحليل شجرة الأعطال

تطلب هذه المطالبة من الذكاء الاصطناعي إنشاء مخطط تحليل شجرة أعطال بتنسيق نصي لحدث فشل نظام ميكانيكي معين. يقدم المستخدم وصف حدث العطل والمكونات المعنية.

المخرجات: 

				
					Construct a fault tree analysis for the mechanical failure event described as: {failure_event}. Consider the following system components: {system_components}. Present the fault tree in markdown using indentation and bullet points to represent logical AND/OR gates and failure paths. Include explanations of each branch and possible root causes. Use uppercase for failure events and lowercase for components.
							

موجه الذكاء الاصطناعي إلى مصفوفة تحديد أولويات نمط الفشل

تطلب هذه المطالبة من الذكاء الاصطناعي إنشاء مصفوفة تحديد أولويات وضع الفشل استنادًا إلى مدخلات ملف CSV لأنماط الفشل وخطورتها وحدوثها وتقييمات الكشف. يساعد في تحديد أولويات الأسباب الجذرية للأعطال الميكانيكية.

المخرجات: 

				
					Using the following CSV data of failure modes with columns: Failure_Mode, Severity, Occurrence, Detection: {csv_failure_modes}, calculate Risk Priority Numbers (RPN) for each mode. Sort the failure modes by decreasing RPN and generate a prioritization matrix. Output a CSV with columns: Failure_Mode, Severity, Occurrence, Detection, RPN, Priority_Rank. Provide a brief summary explaining the top 3 prioritized failure modes and recommendations for mitigation.
							

موجه الذكاء الاصطناعي إلى منشئ تقرير تحليل الأسباب الجذرية

توجه هذه المطالبة الذكاء الاصطناعي إلى إنشاء تقرير مفصل لتحليل السبب الجذري لحادث عطل ميكانيكي استنادًا إلى ملخص الحادث المقدم ونتائج الاختبار ونتائج الفحص. يقوم بتجميع المعلومات في مستند منظم.

المخرجات: 

				
					Generate a comprehensive root cause analysis report for the mechanical failure incident described below. Incident Summary: {incident_summary}. Test Results: {test_results}. Inspection Findings: {inspection_findings}. Structure the report with sections: Executive Summary, Problem Description, Analysis Methodology, Root Cause Identification, Recommendations for Prevention, and Conclusion. Use markdown formatting with headings and bullet points where appropriate. Emphasize clarity, technical accuracy, and actionable insights.
							

موجه الذكاء الاصطناعي إلى Ethical Framework for Autonomous Machinery

Generates a framework for ethical considerations in designing autonomous mechanical systems focusing on safety accountability and decision-making in unforeseen scenarios. This prompt helps engineers proactively address ethical challenges during the design phase of complex machinery. The output is a structured markdown document.

المخرجات: 

				
					Act as an Ethics Advisor specializing in AI and Autonomous Systems in Mechanical Engineering.
Your TASK is to generate a structured ethical framework for the development and deployment of an `{autonomous_system_type}` operating in `{operational_environment_description}`.
The framework should address key ethical principles and provide guidance for handling scenarios listed in `{key_decision_making_scenarios_csv}` (a CSV string like 'Scenario_ID
Description
Potential_Conflict
e.g. S1
Obstacle_Avoidance
Prioritize_occupant_safety_vs_pedestrian_safety').

**FRAMEWORK STRUCTURE (MUST be Markdown format):**

**1. Introduction**
    *   Purpose of the Ethical Framework for `{autonomous_system_type}`.
    *   Scope of application considering `{operational_environment_description}`.

**2. Core Ethical Principles** (Define and explain relevance for `{autonomous_system_type}`)
    *   **Safety & Non-Maleficence**: Minimizing harm.
    *   **Accountability & Responsibility**: Who is responsible in case of failure?
    *   **Transparency & Explainability**: How are decisions made by the system understandable?
    *   **Fairness & Non-Discrimination**: Avoiding bias in decision-making.
    *   **Privacy**: Data collection and usage.
    *   **Human Oversight**: Levels of human control and intervention.

**3. Guidelines for Decision-Making in Critical Scenarios**
    *   For EACH scenario provided in `{key_decision_making_scenarios_csv}`:
        *   **Scenario Analysis**: Briefly describe the ethical dilemma posed.
        *   **Primary Ethical Principle(s) at Stake**: Identify which of the above principles are most relevant.
        *   **Recommended Approach/Hierarchy**: Suggest a decision-making logic or prioritization. Clearly state any trade-offs.
        *   **Justification**: Explain the reasoning behind the recommended approach based on ethical principles.

**4. Design and Development Recommendations**
    *   Specific design considerations for `{autonomous_system_type}` to embed ethical behavior (e.g.
 fail-safe mechanisms
 auditable logs
 bias testing).

**5. Operational and Deployment Considerations**
    *   Monitoring ethical performance post-deployment.
    *   Procedures for addressing ethical breaches or unforeseen negative consequences.

**IMPORTANT**: The framework should be actionable and provide clear guidance for engineers. The discussion of scenarios from `{key_decision_making_scenarios_csv}` is CRUCIAL.
							

موجه الذكاء الاصطناعي إلى Lifecycle Environmental Impact Assessment Outline

Outlines key stages and considerations for conducting a lifecycle environmental impact assessment (LCA) for a new mechanical product. This prompt helps engineers structure their LCA efforts by identifying data needs impact categories and mitigation opportunities. The result is a markdown document detailing the LCA plan.

المخرجات: 

				
					Act as an Environmental Engineering Consultant specializing in Lifecycle Assessments (LCA) for mechanical products.
Your TASK is to generate a structured OUTLINE for conducting a Lifecycle Environmental Impact Assessment for `{product_name_and_function}`.
Consider the product's composition from `{bill_of_materials_csv}` (CSV string: 'Material
Quantity
Source_Region_if_known')
 its `{manufacturing_processes_overview_text}`
 and its `{expected_use_phase_and_disposal_text}`.
You MAY use live internet to identify common impact assessment tools
 databases (e.g.
 Ecoinvent
 GaBi)
 and relevant ISO standards (e.g.
 ISO 14040/14044).

**LCA OUTLINE STRUCTURE (MUST be Markdown format):**

**1. Goal and Scope Definition**
    *   **1.1. Purpose of the LCA**: (e.g.
 Identify environmental hotspots
 Compare with alternative designs
 Eco-labeling).
    *   **1.2. Product System Description**: Define `{product_name_and_function}`.
    *   **1.3. Functional Unit**: Quantified performance of the product system (e.g.
 'Provide X amount of torque for Y hours'
 'Manufacture Z parts').
    *   **1.4. System Boundaries**: Detail what stages are INCLUDED and EXCLUDED (Cradle-to-Grave
 Cradle-to-Gate
 Gate-to-Gate). Justify exclusions.
        *   Raw Material Acquisition (based on `{bill_of_materials_csv}`).
        *   Manufacturing & Assembly (based on `{manufacturing_processes_overview_text}`).
        *   Distribution/Transportation.
        *   Use Phase (based on `{expected_use_phase_and_disposal_text}`).
        *   End-of-Life (Disposal/Recycling
 based on `{expected_use_phase_and_disposal_text}`).
    *   **1.5. Allocation Procedures** (if dealing with multi-output processes or recycled content).
    *   **1.6. Impact Categories Selection**: (e.g.
 Global Warming Potential (GWP
 kg CO2 eq)
 Acidification Potential
 Eutrophication Potential
 Ozone Depletion Potential
 Smog Formation
 Resource Depletion
 Water Footprint). Select relevant categories for this product type.
    *   **1.7. LCA Methodology & Software/Databases**: (e.g.
 CML
 ReCiPe
 TRACI. Mention common software like SimaPro
 GaBi
 openLCA
 and databases like Ecoinvent).

**2. Life Cycle Inventory Analysis (LCI)**
    *   **2.1. Data Collection Plan**: For each life cycle stage:
        *   Identify required input data (energy
 materials
 water
 transport) and output data (emissions
 waste).
        *   Data sources (primary vs. secondary
 from `{bill_of_materials_csv}`
 literature
 databases).
    *   **2.2. Data Quality Requirements** (e.g.
 precision
 completeness
 representativeness).

**3. Life Cycle Impact Assessment (LCIA)**
    *   **3.1. Classification**: Assigning LCI results to selected impact categories.
    *   **3.2. Characterization**: Calculating category indicator results (e.g.
 converting greenhouse gas emissions into CO2 equivalents).
    *   **3.3. Normalization (Optional)**: Expressing impact indicator results relative to a reference value.
    *   **3.4. Weighting (Optional
 and to be used with caution)**: Assigning weights to different impact categories.

**4. Life Cycle Interpretation**
    *   **4.1. Identification of Significant Issues**: Hotspot analysis.
    *   **4.2. Evaluation**: Completeness
 sensitivity
 and consistency checks.
    *   **4.3. Conclusions
 Limitations
 and Recommendations for Mitigation** (e.g.
 material substitution
 process optimization
 design for disassembly).

**IMPORTANT**: This outline should guide an engineer in planning a comprehensive LCA. Emphasize the iterative nature of LCA and the importance of data quality.
							

موجه الذكاء الاصطناعي إلى Societal Impact Analysis of Automation

Analyzes the potential societal impacts such as employment shifts skill demand changes and accessibility issues arising from implementing a specific automation technology in a mechanical engineering sector. This prompt helps engineers consider broader societal consequences. The output is a text-based report.

المخرجات: 

				
					Act as a Socio-Technical Analyst specializing in the impacts of automation in engineering fields.
Your TASK is to provide an analysis of the potential societal impacts of implementing `{automation_technology_description}` within the `{industry_sector_of_application}` specifically considering the `{geographical_region_context}`.
You SHOULD use live internet access to gather data on employment trends
 skill demands
 and relevant socio-economic studies for the specified region and sector.

**SOCIETAL IMPACT ANALYSIS REPORT (Plain Text Format):**

**1. Introduction**
    *   Overview of the `{automation_technology_description}` and its intended application in the `{industry_sector_of_application}`.
    *   Brief note on the socio-economic context of `{geographical_region_context}` relevant to automation.

**2. Potential Impacts on Employment**
    *   **Job Displacement**: Analyze potential for job losses in roles directly affected by the automation. Provide any available statistics or projections for the `{industry_sector_of_application}` in `{geographical_region_context}`.
    *   **Job Creation**: Analyze potential for new jobs created (e.g.
 maintenance of automated systems
 programming
 data analysis
 new roles enabled by the technology).
    *   **Job Transformation**: How existing roles might change
 requiring new skills or responsibilities.

**3. Shifts in Skill Demand**
    *   **Upskilling/Reskilling Needs**: Identify skills that will become more critical (e.g.
 digital literacy
 robotics programming
 data interpretation
 complex problem-solving) and skills that may become obsolete.
    *   **Impact on Training and Education**: Discuss potential needs for changes in vocational training and engineering curricula in `{geographical_region_context}`.

**4. Economic Impacts**
    *   **Productivity Gains**: Potential for increased efficiency
 output
 and competitiveness in the `{industry_sector_of_application}`.
    *   **Investment Requirements**: Capital costs associated with implementing `{automation_technology_description}`.
    *   **Distribution of Economic Benefits**: Discuss who is likely to benefit most (e.g.
 capital owners
 highly skilled labor
 consumers). Consider potential for increased inequality.

**5. Accessibility and Equity**
    *   **Impact on Small vs. Large Businesses**: Can businesses of all sizes in `{geographical_region_context}` adopt this technology
 or does it favor larger enterprises?
    *   **Impact on Different Demographics**: Are there specific groups (e.g.
 older workers
 specific genders
 minority groups) that might be disproportionately affected
 positively or negatively?
    *   **Digital Divide**: Does the technology exacerbate or mitigate the digital divide within the region?

**6. Broader Societal and Ethical Considerations**
    *   **Worker Well-being**: Impact on job quality
 stress levels
 and workplace safety.
    *   **Social Acceptance and Resistance**: Potential for resistance to adoption from workers or the public.
    *   **Long-term Regional Development**: How might widespread adoption of this technology influence the economic trajectory of `{geographical_region_context}`?

**7. Policy Recommendations / Mitigation Strategies (Brief Suggestions)**
    *   Proactive measures that could be taken by policymakers
 industry
 or educational institutions in `{geographical_region_context}` to maximize benefits and mitigate negative impacts (e.g.
 retraining programs
 social safety nets
 investment in education).

**8. Conclusion**
    *   Summary of key potential societal impacts and a call for responsible implementation.

**Disclaimer**: This analysis is based on publicly available information and general trends. Specific impacts can vary based on the details of implementation.
							

موجه الذكاء الاصطناعي إلى Dual-Use Technology Ethical Assessment

Conducts a preliminary ethical assessment for a mechanical engineering technology that may have dual-use applications highlighting potential risks ethical dilemmas and proposing safeguards. This prompt aims to foster responsible innovation by considering unintended consequences. The output is a structured markdown report.

المخرجات: 

				
					Act as an Ethics Reviewer specializing in dual-use technologies in engineering.
Your TASK is to conduct a preliminary ethical assessment of the `{technology_description_and_capabilities}`
 considering its `{intended_civilian_application}` and the `{potential_misuse_concerns_list_csv}` (CSV string: 'Concern_ID
Description_of_Misuse
Potential_Harm_Level_High_Medium_Low').

**ETHICAL ASSESSMENT REPORT (MUST be Markdown format):**

**1. Technology Overview**
    *   Description of `{technology_description_and_capabilities}`.
    *   Stated `{intended_civilian_application}` and its potential benefits.

**2. Identification of Dual-Use Potential**
    *   Analysis of how the core capabilities of the technology could be diverted for harmful or unintended military/security purposes
 drawing from `{potential_misuse_concerns_list_csv}`.
    *   For each concern in `{potential_misuse_concerns_list_csv}`
 elaborate on the pathway from civilian application to potential misuse.

**3. Ethical Dilemmas and Concerns**
    *   **Responsibility of Innovators/Engineers**: Discuss the ethical obligations of those developing such technologies.
    *   **Risk of Unintended Escalation**: How could the technology contribute to instability or an arms race if misused?
    *   **Accessibility and Proliferation**: How easily could the technology or knowledge to replicate it spread to actors with malicious intent?
    *   **Difficulty in Control/Verification**: Once developed
 how hard is it to monitor or control its use or prevent its misuse?
    *   **Impact on Human Rights**: Potential for the technology to be used in ways that violate human rights (e.g.
 surveillance
 autonomous weapons if applicable).

**4. Assessment of Potential Harms (based on `{potential_misuse_concerns_list_csv}`)**
    *   Summarize the potential severity and nature of harms associated with the identified misuses.

**5. Proposed Safeguards and Mitigation Strategies**
    *   **Technical Safeguards**: Are there ways to design the technology to make misuse more difficult (e.g.
 built-in limitations
 usage restrictions
 tamper-proofing
 tracking mechanisms)?
    *   **Policy and Regulatory Safeguards**: Suggestions for governance frameworks
 export controls
 international treaties
 or ethical oversight bodies that could mitigate risks.
    *   **Transparency and Open Dialogue**: Importance of public discussion and engagement with policymakers and ethicists throughout the technology's lifecycle.
    *   **End-User Vetting and Agreements**: Potential for controlling distribution to responsible parties.

**6. Conclusion and Recommendation**
    *   Summarize the key ethical risks associated with the dual-use potential of `{technology_description_and_capabilities}`.
    *   Provide a concluding thought on whether development should proceed
 and if so
 under what ethical conditions or with what mandatory safeguards.
    *   Suggest if a more comprehensive ethical review by a dedicated committee is warranted.

**IMPORTANT**: This is a PRELIMINARY assessment. The aim is to raise awareness and stimulate deeper ethical reflection
 not to provide a definitive judgment. Focus on balancing innovation with responsibility.
							

موجه الذكاء الاصطناعي إلى Experimental Plan Critique and Improvement Suggestions

This prompt asks the AI to analyze a provided experimental design in mechanical engineering, identifying weaknesses and proposing detailed improvements to enhance validity, reliability, and efficiency. The user inputs the experimental plan description and key variables.

المخرجات: 

				
					Critically analyze the following mechanical engineering experimental plan: {experimental_plan}. Consider the key variables: {key_variables}. Identify potential flaws or limitations in design, controls, sample size, measurement methods, and data collection. Suggest specific improvements or alternative approaches to increase validity, reliability, and efficiency. Present your analysis in a numbered list with clear rationale for each suggestion.
							
جدول المحتويات
    أضف رأسًا لبدء إنشاء جدول المحتويات

    تحدي التصميم أو المشروع؟
    مهندس ميكانيكي، مدير مشروع أو بحث وتطوير
    تطوير المنتج الفعال

    متاح لتحدي جديد في غضون مهلة قصيرة في فرنسا وسويسرا.
    تواصل معي على LinkedIn
    المنتجات البلاستيكية والمعدنية، التصميم حسب التكلفة، بيئة العمل، الحجم المتوسط إلى الكبير، الصناعات المنظمة، CE وFDA، التصميم بمساعدة الحاسوب، Solidworks، الحزام الأسود Lean Sigma، شهادة ISO 13485 الطبية من الفئة الثانية والثالثة

    نحن نبحث عن راعي جديد

     

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    المواضيع المغطاة: مطالبات الاختبار، والتحقق من الصحة، وإدخال المستخدم، وجمع البيانات، وآلية التغذية الراجعة، والاختبار التفاعلي، وتصميم الاستبيان، واختبار قابلية الاستخدام، وتقييم البرمجيات، والتصميم التجريبي، وتقييم الأداء، والاستبيان، وISO 9241، وISO 25010، وISO 20282، وISO 13407، وISO 26362.

    1. وينتر

      هل نفترض أن الذكاء الاصطناعي قادر دائمًا على توليد أفضل المطالبات في الهندسة الميكانيكية؟ كيف يتم توليدها بالمناسبة؟

    2. جيزيل

      هل سيجعل الذكاء الاصطناعي المهندسين البشريين زائدين عن الحاجة؟

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