
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。
- 赠款提案和科学写作协助
- 机械工程
人工智能提示 研究论文方法评论
- 增材制造设计(DfAM), 机械工业, 方法, 流程改进, 质量保证, 质量控制, 研究与开发, 统计分析, 验证
对机械工程研究论文中的方法论部分进行审查并提出改进建议,重点关注所使用方法的清晰度、完整性、合理性和适当性。这一提示有助于提高研究的严谨性和可重复性。输出为标记符格式的评论。
输出:
- Markdown
- 不需要实时互联网
- 字段:{当前方法_小节_正文}{研究目标_正文}.{研究目标文本}{关键设备或所用软件列表_csv}。
Act as a Peer Reviewer for a Mechanical Engineering journal.
Your TASK is to critique the provided `{current_methodology_section_text}` from a research paper
keeping in mind the stated `{research_objectives_text}` and the `{key_equipment_or_software_used_list_csv}` (CSV: 'Item_Name
Model_Specification
Manufacturer').
The critique should focus on improving clarity
completeness
justification
and appropriateness of the described methodology.
**CRITIQUE AND RECOMMENDATIONS (MUST be Markdown format):**
**Critique of Methodology Section for Research Objectives: '`{research_objectives_text}`'**
**1. Overall Clarity and Structure:**
* **Assessment**: [Evaluate the overall readability
logical flow
and organization of the `{current_methodology_section_text}`. Is it easy to follow? Are steps presented in a logical sequence?]
* **Recommendations**: [Suggest improvements to structure
e.g.
'Consider using subheadings for distinct phases of the methodology like Experimental Setup
Data Collection
and Data Analysis.' or 'Clarify the transition between step X and step Y.']
**2. Completeness of Description:**
* **Assessment**: [Are all necessary details provided for another researcher to replicate the study? Consider aspects like:]
* Sample preparation (if applicable).
* Detailed parameters for `{key_equipment_or_software_used_list_csv}`.
* Environmental conditions.
* Duration
frequency
or number of measurements/simulations.
* Specific protocols or standards followed (and if they are cited).
* **Recommendations**: [Point out specific missing information
e.g.
'Specify the sampling rate used for data acquisition with [Sensor Name].' or 'Provide details on the mesh convergence study for the FEA model using [Software Name].' or 'Describe the calibration procedure for [Instrument Name].']
**3. Justification and Appropriateness of Methods:**
* **Assessment**: [Are the chosen methods
materials
and equipment appropriate for achieving the `{research_objectives_text}`? Is the choice of methods justified
either explicitly or implicitly through common practice? Are any limitations of the chosen methods acknowledged?]
* **Recommendations**: [Suggest areas where justification is weak or missing
e.g.
'Explain why [Specific Method A] was chosen over [Alternative Method B] for addressing [Specific Objective].' or 'Discuss the potential impact of using [Material Grade X] if its properties significantly differ from those assumed in the model.']
**4. Data Analysis and Statistical Treatment (if described):**
* **Assessment**: [If data analysis or statistical methods are mentioned
are they appropriate and clearly described? Are error analysis or uncertainty quantification addressed?]
* **Recommendations**: [e.g.
'Specify the statistical tests used to compare groups.' or 'Clarify how outliers were handled in the dataset.']
**5. Reproducibility:**
* **Assessment**: [Overall
does the section provide enough information to ensure that the work is reproducible?]
* **General Recommendations**: [Summarize key actions to enhance reproducibility.]
**Specific Comments/Queries (line numbers or specific phrases can be referenced if the AI were to see the original text with them):**
* [e.g.
'Regarding the statement "...optimized parameters were used..."
please specify how these parameters were optimized and what the final values were.']
* [e.g.
'The description of [Equipment X from `{key_equipment_or_software_used_list_csv}`] lacks details on its accuracy/resolution
which could be important.']
**IMPORTANT**: The critique should be constructive
specific
and aimed at helping the author improve the methodology section. Refer to the `{research_objectives_text}` to ensure alignment.
- 最适合对研究论文的方法论部分提供详细的建设性评论,帮助机械工程师提高工作的清晰度、严谨性和可重复性。
- 优化实验设计
- 机械工程
人工智能提示 实验数据验证清单创建者
- 机械工业, 质量保证, 质量控制, 质量管理, 统计分析, 测试方法, 验证, 验证
此提示要求人工智能根据用户提供的实验描述和数据类型,生成一份详细的核对表,用于验证机械工程实验数据的质量和完整性。
输出:
- Markdown
- 不需要实时互联网
- 字段:{实验描述} {数据类型}
Create a comprehensive checklist for validating the quality and integrity of experimental data in mechanical engineering. The experiment description is: {experiment_description}. The type of data collected is: {data_type}. The checklist should cover data collection methods, calibration, error sources, data consistency, and documentation practices. Format the checklist in markdown with numbered items and subpoints. Highlight critical validation steps.
- 最适合最适合确保高质量、可靠的实验数据收集和分析
- 赠款提案和科学写作协助
- 机械工程
人工智能提示 导言的文献综述结构
- 快速成型制造, 持续改进, 增材制造设计(DfAM), 创新, 机械工业, 质量管理, 可持续发展实践
通过从所提供的摘要中识别关键主题并建议合理的流程来确定机械工程主题的研究缺口,从而帮助构建研究论文引言部分的文献综述结构。输出为标记大纲和叙述指南。
输出:
- Markdown
- 不需要实时互联网
- 字段:{研究课题标题}{关键摘要或论文文本列表}{研究课题或论文文本}.{关键摘要列表或论文正文}。{主要研究空白或问题}(main_research_gap_or_question)
Act as a Research Methodology Advisor specializing in scientific writing for Mechanical Engineering.
Your TASK is to help structure the literature review part of an introduction section for a research paper on '`{research_topic_title}`'.
You will be given a `{list_of_key_abstracts_or_papers_text}` (a block of text containing several abstracts or summaries of key papers) and the `{main_research_gap_or_question}` the author intends to address.
Your goal is to propose a logical flow and thematic organization for the literature review that effectively leads to the stated research gap/question.
**PROPOSED LITERATURE REVIEW STRUCTURE (MUST be Markdown format):**
**Research Topic**: `{research_topic_title}`
**Stated Research Gap/Question**: `{main_research_gap_or_question}`
**I. Broad Context and Motivation (1-2 paragraphs)**
* **Guidance**: Start by establishing the general importance and relevance of the broader field related to `{research_topic_title}`.
* **Content to draw from `{list_of_key_abstracts_or_papers_text}`**: Identify abstracts that provide this wider context or highlight the significance of the area.
* **Example Phrasing**: "The field of [Broader Field of `{research_topic_title}`] has garnered significant attention due to its implications for..."
**II. Key Themes/Sub-areas from Existing Literature (organized thematically
3-5 paragraphs typically)**
* **Guidance**: Analyze the `{list_of_key_abstracts_or_papers_text}` to identify recurring themes
established findings
common methodologies
or different approaches related to `{research_topic_title}`. Group papers by these themes.
* **For each Theme/Sub-area X**:
* **A. Introduce Theme X**: Briefly state what this theme covers.
* **B. Summarize Key Contributions**: Discuss what important studies (from the provided list) have found regarding Theme X. Mention specific authors or papers if they are seminal (e.g.
"Smith et al. (Year) demonstrated...
while Jones (Year) focused on...").
* **C. Highlight Consistencies or Contradictions**: Note if findings are generally in agreement or if there are conflicting results or debates within this theme.
* **Example Themes (AI to derive from abstracts)**: Based on typical mechanical engineering topics
themes could be "Material Development for [Application]"
"Advancements in [Specific Manufacturing Process]"
"Computational Modeling of [Phenomenon]"
"Experimental Validation of [Theory/Model]"
"Limitations of Current [Technology/Approach]".
**III. Identification of a Specific Gap or Unresolved Issues (1-2 paragraphs)**
* **Guidance**: Transition from the summary of existing work to pinpointing specific limitations
unanswered questions
or underexplored areas that emerge from the reviewed literature. This section directly sets the stage for the `{main_research_gap_or_question}`.
* **Content to draw from `{list_of_key_abstracts_or_papers_text}`**: Look for phrases in abstracts like "further research is needed..."
"limitations of this study include..."
or areas where fewer studies exist.
* **Example Phasing**: "Despite these advancements
several aspects remain underexplored..." or "A critical review of the literature reveals a gap in understanding..."
**IV. Statement of Current Work and How It Addresses the Gap (1 paragraph)**
* **Guidance**: Clearly state the `{main_research_gap_or_question}` that YOUR proposed paper will address.
* Briefly outline how your paper aims to fill this gap or answer this question
linking it to the shortcomings identified in section III.
* **Example Phasing**: "Therefore
the present study aims to address this gap by investigating [your specific objective related to `{main_research_gap_or_question}`] through [your brief method]..."
**Logical Flow Summary**:
* `General Importance -> Specific Area Review (Thematic) -> Limitations/Gaps in Specific Area -> How Current Paper Fills a Specific Gap.`
**IMPORTANT**: The AI should analyze the provided `{list_of_key_abstracts_or_papers_text}` to suggest plausible themes. The structure should provide a compelling narrative that justifies the need for the research addressing the `{main_research_gap_or_question}`.
- 最适合帮助机械工程师在研究论文引言中组织文献综述,按主题组织现有摘要中的信息,并顺理成章地引出研究缺口。
- 预测建模
- 机械工程
人工智能提示 材料性能预测模型生成器
- 机器学习, 材料, 机械工业, 机械性能, 预测性维护算法, 质量控制, 质量管理, 统计分析
该提示引导人工智能根据用户提供的 CSV 格式历史测试数据,建立机械材料属性预测模型。它包括模型选择、训练和验证步骤。
输出:
- Python
- 不需要实时互联网
- 字段:{csv_material_data} {target_property} 目标属性
Using the following CSV data of mechanical material test results: {csv_material_data}, build a predictive model to estimate the target property: {target_property}. Follow these steps: 1) Preprocess the data (handle missing values, normalize features) 2) Select suitable modeling techniques (e.g., regression, machine learning) 3) Train the model and validate it with cross-validation 4) Output performance metrics (R², RMSE) 5) Provide the final model code snippet in Python. Respond only with the Python code and brief comments.
- 最适合最适合创建数据驱动模型来预测材料行为
- 预测建模
- 机械工程
人工智能提示 系统性能预测工具
- 环境影响评估, 环境技术, 机器学习, 预测性维护算法, 质量管理, 统计分析, 统计过程控制 (SPC), 系统设计
此提示要求人工智能根据以 JSON 格式提供的历史运行数据和环境因素,预测机械系统的未来性能。人工智能将输出带有置信区间的时间序列预测。
输出:
- JSON
- 不需要实时互联网
- 字段:{历史数据_json}{环境因素_json}.{environmental_factors_json} 环境因素_json
Given the historical operational data: {historical_data_json} and environmental factors data: {environmental_factors_json}, forecast the mechanical system's performance over the next 12 months. Use appropriate time series forecasting methods and provide confidence intervals for predictions. Structure the output as a JSON object with keys: 'month', 'predicted_performance', 'confidence_interval_lower', and 'confidence_interval_upper'. Include brief comments on model choice and assumptions.
- 最适合最适合预测机械系统在不同条件下的行为
- 预测建模
- 机械工程
人工智能提示 故障概率估计模型
- 故障分析, 故障模式和影响分析(FMEA), 维护, 机械工业, 预测性维护算法, 风险分析, 风险管理, 统计分析
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:
- Python
- 不需要实时互联网
- 字段:{csv_failure_data} {组件特性}
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.
- 最适合最适合预测组件可靠性和制定维护计划
- 预测建模
- 机械工程
人工智能提示 材料的生物力学响应预测
- 生物材料, 增材制造设计(DfAM), 有限元法(FEM), 材料科学, 机械工业, 机械性能, 预测性维护算法, 结构工程
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:
- LaTeX
- 不需要实时互联网
- 字段:{材料属性}{load_conditions} 加载条件
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.
- 最适合最适合模拟生物力学载荷下的材料力学行为
- 根源分析
- 机械工程
人工智能提示 故障根源假设生成器
- 持续改进, 故障分析, 故障模式和影响分析(FMEA), 精益制造, 解决问题的技巧, 流程改进, 质量管理, 根源分析, 六西格玛
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:
- 文本
- 不需要实时互联网
- 字段:{故障描述} {观察到的症状}
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.
- 最适合最适合初步调查和缩小故障原因范围
- 根源分析
- 机械工程
人工智能提示 故障树分析生成器
- 故障分析, 故障模式和影响分析(FMEA), 故障树分析 (FTA), 机械工业, 流程改进, 质量控制, 质量管理, 风险分析, 风险管理
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:
- Markdown
- 不需要实时互联网
- 字段:{故障事件}{系统组件}
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.
- 最适合最适合可视化机械系统中的故障传播和依赖关系
- 根源分析
- 机械工程
人工智能提示 故障模式优先级矩阵
- 持续改进, 纠正行动, 故障分析, 故障模式和影响分析(FMEA), 流程改进, 质量控制, 质量管理, 风险分析, 风险管理
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:
- CSV
- 不需要实时互联网
- 字段:{csv_failure_modes} 失败模式
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.
- 最适合最适合量化故障调查和纠正措施的优先次序
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?
人工智能会让人类工程师变得多余吗?
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