» 机械工程最佳人工智能提示

机械工程最佳人工智能提示

人工智能提示 机械工程
Ai 机械工程
人工智能驱动的工具通过先进的数据分析和模式识别,提高了设计优化、仿真速度、预测性维护和材料选择的能力,为机械工程带来了革命性的变化。

通过增强人类在设计、分析方面的能力,在线人工智能工具正在迅速改变机械工程、 制造业和维护。与传统方法相比,这些人工智能系统可以更快地处理海量数据、识别复杂模式并生成新的解决方案。例如,人工智能可以帮助您优化性能和可制造性设计,加速复杂的模拟,预测材料特性,并自动执行各种分析任务。

例如,下面提供的提示有助于生成设计、加速模拟(有限元分析/有限差分分析)、预测性维护(人工智能通过分析机械的传感器数据来预测潜在故障,从而实现主动服务并最大限度地减少停机时间)、材料选择等方面的帮助。

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人工智能提示 材料测试对照组建议

为机械应用中新材料或表面处理的实验研究建议适当的对照组和基线测量,以确保有效的比较和可靠的结论。该提示可帮助工程师设计更稳健的材料测试协议。输出结果是基于文本的建议。

输出: 

				
					Act as an Experimental Design Specialist in Materials Science and Engineering.
Your TASK is to recommend appropriate control groups and baseline measurements for an experimental study involving `{test_material_or_treatment_description}` under `{experimental_conditions_text}`
 where `{performance_metrics_to_be_measured_list_csv}` (CSV: 'Metric_Name
Units') are the key outputs.
The goal is to ensure that any observed changes in performance can be confidently attributed to the `{test_material_or_treatment_description}`.

**RECOMMENDATIONS FOR CONTROL GROUPS AND BASELINE MEASUREMENTS:**

**1. Understanding the Core Investigation:**
    *   The primary goal is to evaluate the effect of `{test_material_or_treatment_description}`.
    *   The `{experimental_conditions_text}` (e.g.
 'High-temperature tensile testing at 600°C'
 'Cyclic fatigue testing under 200 MPa load for 10^6 cycles'
 'Wear testing against a steel counterface with 10N load for 5 hours') define the environment.
    *   The `{performance_metrics_to_be_measured_list_csv}` (e.g.
 'Ultimate_Tensile_Strength_MPa
Elongation_Percent'
 'Fatigue_Life_Cycles'
 'Wear_Rate_mm3_Nm') are the indicators of performance.

**2. Recommended Control Group(s):**
    *   **A. Untreated/Standard Material Control:**
        *   **Description**: Samples made from the SAME BASE MATERIAL as the `{test_material_or_treatment_description}` but WITHOUT the specific new material feature or treatment being tested. If the test involves a new alloy
 the control might be the conventional alloy it aims to replace or a version of the new alloy without a critical processing step.
        *   **Justification**: This is the MOST CRITICAL control. It allows for direct comparison to determine if the `{test_material_or_treatment_description}` provides any benefit (or detriment) over the standard or untreated state.
        *   **Processing**: These control samples should
 as much as possible
 undergo all other processing steps (e.g.
 heat treatments
 machining) that the test samples experience
 EXCEPT for the specific treatment/feature being evaluated.
    *   **B. (Optional
 if applicable) Benchmark/Reference Material Control:**
        *   **Description**: Samples made from a well-characterized
 industry-standard benchmark material that is commonly used in similar applications or for which extensive performance data exists.
        *   **Justification**: This allows comparison against a known quantity and can help validate the testing procedure if the benchmark material behaves as expected. It also positions the performance of the `{test_material_or_treatment_description}` within the broader field.
    *   **C. (Optional
 if treatment involves application) Placebo/Sham Treatment Control:**
        *   **Description**: If the treatment involves a complex application process (e.g.
 a coating applied via a specific sequence of steps
 some of which might independently affect the material)
 a sham control experiences all application steps EXCEPT the active treatment ingredient/process.
        *   **Justification**: Helps to isolate the effect of the active treatment component from the effects of the application process itself.

**3. Baseline Measurements (Pre-Test Characterization):**
    *   For ALL samples (both test and control groups)
 consider performing and recording the following baseline measurements BEFORE subjecting them to the main `{experimental_conditions_text}`:
        *   **Initial Microstructure Analysis**: (e.g.
 Optical microscopy
 SEM) To document the starting state
 grain size
 presence of defects
 or treatment-induced surface changes.
        *   **Initial Hardness Testing**: A quick way to check for consistency or initial effects of a surface treatment.
        *   **Precise Dimensional Measurements**: Especially important for wear or deformation studies.
        *   **Surface Roughness**: If surface properties are critical or affected by the treatment.
        *   **Compositional Analysis (Spot Checks)**: To verify material or coating composition if it's a key variable.
    *   **Justification**: Baseline data helps confirm initial sample consistency
 can reveal pre-existing flaws
 and provides a reference point for assessing changes after testing.

**4. Experimental Considerations:**
    *   **Sample Size**: Ensure a sufficient number of samples in each group (test and control) for statistical validity.
    *   **Randomization**: If there are variations in the testing apparatus or over time
 randomize the testing order of samples from different groups.
    *   **Identical Test Conditions**: CRITICAL - All groups (test and control) MUST be subjected to the EXACT SAME `{experimental_conditions_text}` and measurement procedures for the `{performance_metrics_to_be_measured_list_csv}`.

**Summary**: By including these control groups and baseline measurements
 the experiment will be better able to isolate the true effect of the `{test_material_or_treatment_description}` and produce more reliable and defensible conclusions.
							

人工智能提示 故障概率估计模型

此提示指示人工智能开发一个预测模型,根据输入特征和 CSV 格式提供的历史故障数据估算机械部件的故障概率。其中包括模型解释和使用说明。

输出: 

				
					Using the provided CSV dataset of historical failures: {csv_failure_data} and the list of component features: {component_features}, build a predictive model estimating failure probability of mechanical components. Steps: 1) Data preprocessing 2) Feature importance analysis 3) Model training (e.g., logistic regression, random forest) 4) Model evaluation 5) Provide Python code with comments explaining usage. Return only the code and brief explanations.
							

人工智能提示 材料的生物力学响应预测

此提示要求人工智能预测指定加载条件下材料的生物力学响应。用户输入材料属性和载荷参数,人工智能就会输出详细的响应模型。

输出: 

				
					Predict the biomechanical response of a material with the following properties: {material_properties}, subjected to load conditions: {load_conditions}. Include stress-strain behavior, deformation, and failure criteria. Present the response model using LaTeX formatted equations and explanations. Highlight assumptions and boundary conditions clearly.
							

人工智能提示 故障根源假设生成器

该提示指示人工智能根据用户提供的详细故障描述和观察到的症状,为机械故障事件生成可信的根本原因假设。

输出: 

				
					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.
							

人工智能提示 故障树分析生成器

此提示要求人工智能为给定的机械系统故障事件构建文本格式的故障树分析图。用户提供故障事件描述和涉及的部件。

输出: 

				
					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.
							

人工智能提示 故障模式优先级矩阵

此提示要求人工智能根据 CSV 输入的故障模式、严重程度、发生率和检测评级,创建故障模式优先级矩阵。这有助于确定机械故障根本原因的优先级。

输出: 

				
					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.
							

人工智能提示 根源分析报告生成器

此提示指示人工智能根据所提供的事件摘要、测试结果和检查结果,为机械故障事件生成详细的根本原因分析报告。它将信息综合成结构化文件。

输出: 

				
					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.
							

人工智能提示 自主机械的伦理框架

生成一个设计自主机械系统的伦理考虑框架,重点关注意外情况下的安全责任和决策。该提示可帮助工程师在复杂机械的设计阶段主动应对伦理挑战。输出为结构化的标记符文档。

输出: 

				
					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.
							

人工智能提示 生命周期环境影响评估大纲

概述了对新机械产品进行生命周期环境影响评估(LCA)的关键阶段和注意事项。本提示通过确定数据需求影响类别和缓解机会,帮助工程师构建 LCA 工作。结果是一份详细说明 LCA 计划的标记文件。

输出: 

				
					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.
							

人工智能提示 自动化的社会影响分析

分析在机械工程领域实施特定自动化技术可能产生的社会影响,如就业转移、技能需求变化和可及性问题。这一提示有助于工程师考虑更广泛的社会后果。输出结果是一份基于文本的报告。

输出: 

				
					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.
							
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    1. 温特

      我们是否假设人工智能总能生成机械工程方面的最佳提示?这些提示是如何生成的?

    2. 吉赛尔

      人工智能会让人类工程师变得多余吗?

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