Online AI tools are rapidly transforming mechanical engineering by augmenting human capabilities in design, analysis, manufacturing, and maintenance. These AI systems can process vast amounts of data, identify complex patterns, and generate novel solutions much faster than traditional methods. For instance, AI can assist you in optimizing designs for performance and manufacturability, accelerate complex simulations, predict material properties, and automate a wide range of analytical tasks.
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.
- This page is specific for one domain. If necessary, you can have full search capabilities accros all domains and all criteria in our > AI Prompts Directory <, dedicated to product design and innovation.
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- Root Cause Analysis
- Mechanical engineering
AI Prompt to Fishbone Diagram Inputs for Failure
- Continuous Improvement, Design for Manufacturing (DfM), Failure analysis, Lean Manufacturing, Mechanical Engineering, Process Improvement, Quality Management, Root Cause Analysis, Six Sigma
Helps structure a Fishbone (Ishikawa) diagram for a mechanical component failure by suggesting potential contributing factor categories (e.g. Man Machine Material Method Environment Measurement) and specific questions to ask for each category based on the failure description. This prompt facilitates a systematic root cause analysis. The output is a markdown formatted outline.
Output:
- Markdown
- does not require live Internet
- Fields: {component_that_failed} {failure_mode_description} {operating_conditions_at_failure_text}
Act as a Root Cause Analysis (RCA) Facilitator.
Your TASK is to help structure a Fishbone (Ishikawa) Diagram to investigate the root cause of a failure involving `{component_that_failed}`.
The described failure is: `{failure_mode_description}`.
The failure occurred under these conditions: `{operating_conditions_at_failure_text}`.
You should propose key questions for standard Fishbone categories tailored to this mechanical failure context.
**FISHBONE DIAGRAM STRUCTURE INPUTS (MUST be Markdown format):**
**Problem Statement (Head of the Fish):** Failure of `{component_that_failed}`: `{failure_mode_description}`
**Main Bones (Categories) and Potential Contributing Factor Questions:**
**1. Machine (Equipment / Technology)**
* Was the `{component_that_failed}` the correct type/model/specification for the application?
* Was the equipment where `{component_that_failed}` is installed operating correctly before/during the failure? (e.g.
speed
load
pressure
temperature within design limits described in `{operating_conditions_at_failure_text}`?)
* Had there been any recent maintenance
repair
or modification to the machine or `{component_that_failed}`? Were procedures followed?
* Was auxiliary equipment (e.g.
cooling
lubrication
power supply) functioning correctly?
* Is there a history of similar failures with this machine or other similar machines?
* Could any tooling
fixtures
or associated parts have contributed to the failure of `{component_that_failed}`?
**2. Method (Process / Procedure)**
* Were correct operating procedures being followed when the failure occurred
considering `{operating_conditions_at_failure_text}`?
* Were installation or assembly procedures for `{component_that_failed}` followed correctly?
* Were maintenance procedures adequate and followed correctly for `{component_that_failed}` and related systems?
* Were there any recent changes in operating procedures
set-points
or work instructions?
* Was the system being operated outside of its design intent or capacity?
* Could any testing or quality control procedures related to `{component_that_failed}` have missed a defect?
**3. Material (Includes Raw Materials
Consumables
`{component_that_failed}` itself)**
* Was the `{component_that_failed}` made from the specified material? Was material certification available/correct?
* Could there have been a defect in the material of `{component_that_failed}` (e.g.
inclusions
porosity
incorrect heat treatment
flaws)?
* If consumables are involved (e.g.
lubricants
hydraulic fluids
coolants)
were they the correct type
clean
and at the correct level/condition?
* Has the `{component_that_failed}` been exposed to any corrosive or degrading substances not accounted for in its design?
* Could there have been issues with material handling or storage of `{component_that_failed}` before installation?
**4. Manpower (People / Personnel)**
* Was the operator/maintenance personnel adequately trained and qualified for the task they were performing related to `{component_that_failed}` or its system?
* Was there sufficient experience or supervision?
* Could human error (e.g.
misjudgment
incorrect assembly
misreading instructions
fatigue) have contributed?
* Were personnel following safety procedures? Were they rushed or under stress?
* Was there clear communication regarding operational or maintenance status?
**5. Measurement (Inspection / Instrumentation)**
* Were measuring instruments or sensors used to monitor `{operating_conditions_at_failure_text}` (e.g.
temperature
pressure
vibration
current) calibrated and functioning correctly?
* Were any warning signs or abnormal readings from instruments ignored or misinterpreted prior to the failure of `{component_that_failed}`?
* Were quality control checks or inspections of `{component_that_failed}` (pre-installation or during service) performed correctly and were the criteria appropriate?
* Could there be inaccuracies in the data used to assess the condition of `{component_that_failed}`?
**6. Environment (Operating Conditions / Surroundings)**
* Were the environmental conditions (temperature
humidity
cleanliness
vibration from external sources) as described in `{operating_conditions_at_failure_text}` within design limits for `{component_that_failed}`?
* Could any unusual environmental factors (e.g.
sudden impact
flooding
power surge
foreign object ingress) have contributed?
* Was the `{component_that_failed}` properly protected from the operating environment?
* Could long-term environmental exposure (e.g.
corrosion
UV degradation) have weakened `{component_that_failed}`?
**Instructions for User**: Use these questions as starting points to brainstorm specific potential causes under each category for the failure of `{component_that_failed}`. Further drill down with 'Why?' for each identified cause.
- Best for: Assisting mechanical engineers in systematically investigating component failures by providing tailored questions for each category of a Fishbone (Ishikawa) diagram.
- Ethical Consideration and Impact Analysis
- Mechanical engineering
AI Prompt to Ethical Framework for Autonomous Machinery
- Advanced Driver-Assistance Systems (ADAS), Artificial Intelligence (AI), Autonomous Vehicle, Design Thinking, Human-Centered Design, Risk Management, Robotics, Safety
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.
Output:
- Markdown
- does not require live Internet
- Fields: {autonomous_system_type} {operational_environment_description} {key_decision_making_scenarios_csv}
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.
- Best for: Proactively developing ethical guidelines for autonomous mechanical systems helping engineers navigate complex moral decision-making in design and operation.
- Ethical Consideration and Impact Analysis
- Mechanical engineering
AI Prompt to Lifecycle Environmental Impact Assessment Outline
- Circular Economy, Eco-Friendly Manufacturing, Environmental Impact, Environmental Impact Assessment, Life Cycle, Life Cycle Assessment (LCA), Sustainability Practices, Sustainable Development, Sustainable Product Design
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.
Output:
- Markdown
- does require live Internet
- Fields: {product_name_and_function} {bill_of_materials_csv} {manufacturing_processes_overview_text} {expected_use_phase_and_disposal_text}
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.
- Best for: Structuring the lifecycle environmental impact assessment of mechanical products enabling engineers to systematically evaluate and mitigate environmental footprints.
- Ethical Consideration and Impact Analysis
- Mechanical engineering
AI Prompt to Societal Impact Analysis of Automation
- Change Management, Industrial Automation, Mechanical Engineering, Sustainability Practices
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.
Output:
- Text
- does require live Internet
- Fields: {automation_technology_description} {industry_sector_of_application} {geographical_region_context}
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.
- Best for: Analyzing potential societal consequences of automation in mechanical engineering such as employment shifts and skill demand helping to inform responsible technology adoption.
- Ethical Consideration and Impact Analysis
- Mechanical engineering
AI Prompt to Dual-Use Technology Ethical Assessment
- Additive Manufacturing, Design for Additive Manufacturing (DfAM), Environmental Impact, Mechanical Engineering, Risk Management, Sustainability Practices
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.
Output:
- Markdown
- does not require live Internet
- Fields: {technology_description_and_capabilities} {intended_civilian_application} {potential_misuse_concerns_list_csv}
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.
- Best for: Assessing dual-use mechanical technologies for ethical risks and misuse potential guiding engineers in responsible innovation and proposing safeguards.
- Experimental Design Optimization
- Mechanical engineering
AI Prompt to Experimental Plan Critique and Improvement Suggestions
- Design Analysis, Design Evaluation, Design Optimization, Process Improvement, Quality Assurance, Quality Control, Statistical Analysis, Validation
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.
Output:
- Text
- does not require live Internet
- Fields: {experimental_plan} {key_variables}
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.
- Best for: Best for optimizing experimental setups to yield more reliable and valid results
- Grant Proposal and Scientific Writing Assistance
- Mechanical engineering
AI Prompt to Grant Proposal Significance Section Draft
- Additive Manufacturing, Design for Additive Manufacturing (DfAM), Innovation, Mechanical Engineering, Product Development, Prototyping, Research and Development, Sustainability Practices, Value Proposition
Drafts the Significance and Innovation sections for a mechanical engineering grant proposal highlighting the project’s novelty research gap addressed and potential impact. This prompt helps engineers articulate the core value of their proposed work. The output is a markdown formatted text.
Output:
- Markdown
- does not require live Internet
- Fields: {project_title} {research_problem_statement} {proposed_solution_summary} {key_innovative_aspects_list_csv}
Act as a Scientific Writing Assistant specializing in engineering grant proposals.
Your TASK is to draft the 'Significance' and 'Innovation' sections (or a combined 'Significance and Innovation' section) for a grant proposal titled '`{project_title}`'.
The draft should clearly articulate the importance of the `{research_problem_statement}`
the novelty of the `{proposed_solution_summary}`
and the potential impact of the research
drawing upon the `{key_innovative_aspects_list_csv}` (CSV string: 'Aspect_ID
Description_of_Innovation').
**DRAFT SECTIONS (MUST be Markdown format):**
**`{project_title}`**
**Significance**
1. **Critical Need/Problem Statement**:
* Elaborate on the `{research_problem_statement}`. Clearly define the existing challenge
knowledge gap
or unmet need in the field of mechanical engineering that this project addresses.
* Explain the current limitations or drawbacks of existing approaches or technologies.
* Quantify the problem if possible (e.g.
'Current methods result in X% energy loss'
'Failures due to Y cost the industry $Z annually').
2. **Impact if Successful**:
* Describe the potential impact of successfully completing this project. How will the `{proposed_solution_summary}` advance scientific knowledge
technological capability
or address societal needs?
* Who will benefit from this research (e.g.
specific industries
researchers
society at large)?
* Discuss broader impacts
such as contributions to education
diversity
or economic development
if applicable.
3. **Relevance to Funder's Mission (Generic - user to tailor if funder is known)**:
* Briefly connect the project's goals to typical missions of funding agencies focused on scientific and technological advancement (e.g.
advancing fundamental knowledge
fostering innovation
enhancing national competitiveness
solving critical societal problems).
**Innovation**
1. **Novelty of Approach/Concept**:
* Clearly explain what is fundamentally new and innovative about the `{proposed_solution_summary}` and the project's overall approach.
* Refer to specific points from `{key_innovative_aspects_list_csv}`. For each innovative aspect:
* Describe the innovation in detail.
* Explain how it departs from or improves upon current paradigms
theories
methods
or technologies.
2. **Advancement Beyond Current State-of-the-Art**:
* Contrast the proposed work with existing methods
highlighting the advancements it offers.
* Why is this approach likely to be more effective
efficient
or transformative than current alternatives?
3. **Potential for Paradigm Shift (if applicable)**:
* If the project has the potential to significantly change the way research is conducted or problems are solved in this field
articulate this potential.
**Overall Summary of Significance and Innovation**:
* A brief concluding paragraph that powerfully reiterates why this project is important
innovative
and worthy of funding.
**IMPORTANT**: The tone should be persuasive
confident
and scholarly. Ensure clear connections between the problem
the proposed innovative solution
and the expected impact. Avoid jargon where possible or explain it.
- Best for: Assisting mechanical engineers in drafting compelling Significance and Innovation sections for grant proposals ensuring clear articulation of research value and novelty.
- Experimental Design Optimization
- Mechanical engineering
AI Prompt to Optimal Experimental Design Generator
- Design for Additive Manufacturing (DfAM), Design for Manufacturing (DfM), Mechanical Engineering, Process Optimization, Quality Assurance, Quality Control, Research and Development, Statistical Analysis
This prompt instructs the AI to design an optimal experiment to investigate specified mechanical engineering parameters. The user provides the research question, variables to test, and constraints. The AI returns a full experimental plan with control groups, sample sizes, and measurement strategies.
Output:
- Markdown
- does not require live Internet
- Fields: {research_question} {variables} {constraints}
Design an optimal experimental plan for the mechanical engineering research question: {research_question}. The variables to be tested are: {variables}. Consider the following constraints: {constraints}. Provide a detailed plan including experimental setup, control groups, number of samples, measurement techniques, data collection methods, and statistical analysis approach. Format your response using markdown with sections and bullet points. Emphasize efficiency and validity in your design.
- Best for: Best for generating comprehensive, statistically sound mechanical engineering experiments
- Grant Proposal and Scientific Writing Assistance
- Mechanical engineering
AI Prompt to Technical Report Abstract Generator
- Continuous Improvement, Design for Additive Manufacturing (DfAM), Mechanical Engineering, Methodology, Process Improvement, Project Management, Quality Management, Research and Development, Sustainability Practices
Generates a concise and informative abstract for a technical report based on key sections of the report. This prompt helps engineers quickly summarize their work for wider dissemination. The output is a plain text abstract.
Output:
- Text
- does not require live Internet
- Fields: {report_title} {project_objectives_summary} {methodology_used_summary} {key_results_and_conclusions_summary}
Act as a Technical Editor specializing in engineering reports.
Your TASK is to generate a concise and informative abstract for a technical report titled '`{report_title}`'.
The abstract should be based on the following summaries provided by the user:
* `{project_objectives_summary}`: A brief statement of the project's goals.
* `{methodology_used_summary}`: A concise description of the methods
tools
or approaches employed.
* `{key_results_and_conclusions_summary}`: A summary of the most important findings and the main conclusions drawn from the project.
**ABSTRACT GENERATION GUIDELINES:**
The abstract MUST be a single paragraph
typically between 150-250 words (though this is a guideline
quality over strict length).
It should be structured to include the following elements seamlessly:
1. **Background/Purpose (derived from `{project_objectives_summary}` and `{report_title}`):**
* Start with a sentence or two that introduces the context or purpose of the work described in '`{report_title}`' and its main objectives from `{project_objectives_summary}`.
2. **Methodology (derived from `{methodology_used_summary}`):**
* Briefly describe the key methods
experimental procedures
simulation techniques
or analytical approaches used
as outlined in `{methodology_used_summary}`. Avoid excessive detail; focus on what was done.
3. **Key Results (derived from `{key_results_and_conclusions_summary}`):**
* Highlight the most significant findings or outcomes of the project. Quantify results where possible and impactful (e.g.
'a 25% improvement in efficiency was observed'
'the material exhibited a tensile strength of X MPa').
4. **Main Conclusions (derived from `{key_results_and_conclusions_summary}`):**
* State the primary conclusions drawn from the results. What is the overall significance of the findings?
5. **Keywords (Optional but Recommended - AI to suggest 3-5 based on content):**
* If appropriate
the AI can suggest a few keywords at the end of the abstract text
prefixed with 'Keywords:'. This is a secondary task.
**Output Format:**
Plain text
suitable for direct inclusion in a technical report.
**Example Abstract Structure (Conceptual):**
`This report
'`{report_title}`'
details an investigation aimed at [paraphrase/combine from `{project_objectives_summary}`]. The study employed [summarize from `{methodology_used_summary}`
e.g.
'a combination of finite element analysis and experimental validation' or 'a novel design algorithm']. Key findings indicate [summarize key quantitative or qualitative results from `{key_results_and_conclusions_summary}`]. It was concluded that [summarize main conclusions from `{key_results_and_conclusions_summary}`
highlighting significance].`
`Keywords: [Suggested Keyword1
Suggested Keyword2
Suggested Keyword3]`
**IMPORTANT**: The abstract MUST be self-contained and understandable without reference to the full report. It should be accurate
concise
and highlight the most compelling aspects of the work. Avoid using jargon that isn't commonly understood or defined within the abstract's context.
- Best for: Generating concise and well-structured abstracts for technical reports helping mechanical engineers efficiently summarize project objectives methods results and conclusions.
- Experimental Design Optimization
- Mechanical engineering
AI Prompt to Statistical Power Analysis for Experiments
- Design for Six Sigma (DfSS), Process Improvement, Process Optimization, Quality Assurance, Quality Control, Statistical Analysis, Statistical Process Control (SPC), Statistical Tests, Validation
This prompt requests the AI to perform a statistical power analysis for a mechanical engineering experiment based on input parameters such as effect size, sample size, and significance level. It helps determine if the experiment is sufficiently powered.
Output:
- Text
- does not require live Internet
- Fields: {effect_size} {sample_size} {significance_level}
Perform a statistical power analysis for a mechanical engineering experiment with the following parameters: Effect Size: {effect_size}, Sample Size: {sample_size}, Significance Level (alpha): {significance_level}. Calculate the statistical power and interpret whether the current design is adequate. If underpowered, suggest adjustments to sample size or effect size. Present calculations step-by-step and summarize the conclusion clearly.
- Best for: Best for validating experimental designs through power calculations
Are we assuming AI can always generate the best prompts in mechanical engineering? How are these generated btw?
Is AI going to make human engineers redundant?
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