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Best AI Prompts for Mechanical Engineering

AI Prompts Mechanical Engineering
Ai mechanical engineering
Ai-driven tools are revolutionizing mechanical engineering by enhancing design optimization, simulation speed, predictive maintenance, and material selection through advanced data analysis and pattern recognition.

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 bellow 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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AI Prompt to 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.

Output: 

				
					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.
							

AI Prompt to Material Property Prediction Model Strategy

Outlines a strategy for developing a predictive model for a specific material property based on compositional and processing parameters using a described dataset. This prompt helps mechanical engineers initiate data-driven material design or selection. The output is a markdown formatted strategy document.

Output: 

				
					Act as a Materials Informatics Specialist.
Your TASK is to outline a strategy for developing a predictive model for `{target_material_property_to_predict}` (e.g.
 'Tensile Strength in MPa'
 'Hardness in HRC'
 'Fatigue Life in cycles').
The model will be based on input features described in `{available_input_features_csv_description}` (CSV string detailing feature names
 types (numeric/categorical)
 and example ranges
 e.g.
 'FeatureName
DataType
ExampleRange
CarbonContent_Numeric_0.1-1.0%
HeatTreatmentTemp_Numeric_800-1200C
AlloyingElementX_Categorical_Present/Absent').
Consider the `{dataset_size_and_characteristics_text}` (e.g.
 'Approximately 500 data points
 some missing values
 potential outliers noted in preliminary analysis
 data from various literature sources').

**PREDICTIVE MODEL DEVELOPMENT STRATEGY (MUST be Markdown format):**

**1. Project Goal:**
    *   To develop a predictive model for `{target_material_property_to_predict}` using the features outlined in `{available_input_features_csv_description}`.
    *   Potential use cases: Material screening
 alloy design optimization
 property estimation where experimental data is scarce.

**2. Data Preprocessing and Exploration (Key Steps):**
    *   **2.1. Data Loading and Initial Inspection**: 
        *   Load the dataset.
        *   Verify feature names
 data types against `{available_input_features_csv_description}`.
        *   Initial check for gross errors or inconsistencies.
    *   **2.2. Handling Missing Values**: Based on `{dataset_size_and_characteristics_text}` if it mentions missing data.
        *   Strategy: (e.g.
 Imputation using mean/median/mode
 K-Nearest Neighbors imputation
 or model-based imputation if complex. Justify choice).
        *   Consider adding an indicator column for imputed values.
    *   **2.3. Outlier Detection and Treatment**: Based on `{dataset_size_and_characteristics_text}` if it mentions outliers.
        *   Strategy: (e.g.
 Z-score
 IQR method
 Isolation Forest). Decide whether to cap
 transform
 or remove outliers
 and justify.
    *   **2.4. Feature Engineering (if applicable)**:
        *   Transformations: (e.g.
 Logarithmic
 square root transformations for skewed data; Polynomial features if non-linear relationships are suspected).
        *   Categorical Encoding: For features identified as categorical in `{available_input_features_csv_description}` (e.g.
 One-Hot Encoding
 Label Encoding).
        *   Interaction Terms: Consider creating interaction terms between key features if domain knowledge suggests their importance.
    *   **2.5. Feature Scaling/Normalization**: 
        *   Strategy: (e.g.
 StandardScaler
 MinMaxScaler) especially important for distance-based algorithms or neural networks.
    *   **2.6. Exploratory Data Analysis (EDA)**:
        *   Visualize distributions of individual features and the `{target_material_property_to_predict}`.
        *   Plot relationships between input features and the target property (scatter plots
 correlation matrix).
        *   Identify potential correlations or patterns.

**3. Model Selection and Training:**
    *   **3.1. Splitting the Dataset**:
        *   Training set (e.g.
 70-80%)
 Validation set (e.g.
 10-15%)
 Test set (e.g.
 10-15%). Stratified splitting if the target variable is highly imbalanced or for classification tasks (though this is regression).
    *   **3.2. Candidate Model Algorithms (Suggest 2-3 to explore)**:
        *   **Baseline Model**: Simple Linear Regression or a decision tree regressor.
        *   **More Complex Models**:
            *   Ensemble methods (Random Forest Regressor
 Gradient Boosting Regressor like XGBoost
 LightGBM) - often perform well on tabular data.
            *   Support Vector Regression (SVR).
            *   Neural Networks (Multilayer Perceptron - MLP) - consider if dataset is large enough and complex non-linearities are expected.
        *   Justify choices based on `{dataset_size_and_characteristics_text}` and nature of features.
    *   **3.3. Hyperparameter Tuning**:
        *   Strategy: (e.g.
 GridSearchCV
 RandomizedSearchCV
 Bayesian Optimization) using the validation set.
    *   **3.4. Training**: Train candidate models on the training set using optimized hyperparameters.

**4. Model Evaluation:**
    *   **4.1. Performance Metrics (for regression)**:
        *   Primary metrics: Root Mean Squared Error (RMSE)
 Mean Absolute Error (MAE).
        *   Secondary metrics: R-squared (Coefficient of Determination)
 Mean Absolute Percentage Error (MAPE).
    *   **4.2. Evaluation on Test Set**: Assess final model performance on the unseen test set to estimate generalization ability.
    *   **4.3. Residual Analysis**: Plot residuals to check for patterns
 homoscedasticity
 and normality.
    *   **4.4. Feature Importance Analysis**: (e.g.
 from tree-based models) to understand which features are most influential.

**5. Iteration and Refinement:**
    *   Based on evaluation
 iterate on feature engineering
 model selection
 or hyperparameter tuning.
    *   Consider ensemble stacking if individual models show complementary strengths.

**6. Deployment Considerations (Briefly
 if applicable):**
    *   How will the model be used? API
 embedded system
 standalone tool?
    *   Monitoring model performance over time if new data comes in.

**IMPORTANT**: This strategy should be comprehensive yet adaptable. The specific choices for methods will depend on the detailed exploration of the data. Emphasize the iterative nature of model development.
							

AI Prompt to RUL Model Key Input Parameter Identification

Identifies and lists key input parameters and sensor data types most relevant for developing a Remaining Useful Life (RUL) predictive model for a specific type of rotating mechanical equipment. This prompt aids in selecting appropriate data for prognostics. The output is a CSV formatted list.

Output: 

				
					Act as a Prognostics and Health Management (PHM) Specialist.
Your TASK is to identify key input parameters and sensor data types that would be MOST RELEVANT for developing a Remaining Useful Life (RUL) predictive model for `{equipment_type_and_function}`.
Consider the `{known_failure_modes_list_csv}` (CSV: 'FailureMode_ID
Description
AffectedComponents') and the `{available_sensor_data_streams_description_text}` (e.g.
 'Vibration (accelerometers on bearings
 10kHz sampling)
 Temperature (thermocouples on casing
 motor winding
 1Hz)
 Oil pressure (transducer
 10Hz)
 Rotational speed (encoder
 1kHz)
 Acoustic emission (sensor on housing
 1MHz range)
 Load current (motor control unit
 50Hz)').

**KEY INPUT PARAMETERS FOR RUL MODEL (MUST be CSV format):**

**CSV Header**: `Parameter_Rank
Parameter_Name_Or_Feature
Sensor_Or_Data_Source
Relevance_To_Failure_Modes
Potential_Feature_Engineering_Notes`

**Analysis Logic to Generate Rows:**

1.  **Understand Equipment and Failure Modes**:
    *   Analyze `{equipment_type_and_function}` (e.g.
 'Centrifugal Pump for corrosive fluids'
 'High-speed gearbox for wind turbine'
 'Aircraft engine bearing assembly').
    *   Review each failure mode in `{known_failure_modes_list_csv}`. For each mode
 think about what physical parameters would change as degradation progresses towards that failure.
2.  **Map Sensor Data to Degradation Indicators**:
    *   For each sensor stream in `{available_sensor_data_streams_description_text}`
 assess its potential to capture indicators of the identified failure modes.
3.  **Prioritize Parameters**: Rank parameters based on their likely sensitivity to degradation and relevance to the most critical or common failure modes.
4.  **Suggest Feature Engineering**: For raw sensor data
 suggest derived features that are often more informative for RUL models.

**Example Parameters to Consider (AI to generate specific to inputs):**
    *   **Vibration-based features**:
        *   RMS
 Kurtosis
 Crest Factor
 Skewness (overall or in specific frequency bands).
        *   Spectral power in bands around bearing defect frequencies
 gear mesh frequencies
 unbalance frequencies.
        *   Cepstral analysis features for identifying harmonics/sidebands.
    *   **Temperature-based features**:
        *   Absolute temperature levels.
        *   Temperature trends/rate of change.
        *   Temperature differentials between components.
    *   **Process/Operational Parameters**:
        *   Load levels
 torque
 current drawn (can indicate increased friction or effort).
        *   Pressure drops
 flow rates (for pumps
 hydraulic systems).
        *   Speed variations.
    *   **Oil/Lubricant Related (if applicable & sensors exist)**:
        *   Particle count
 viscosity
 water content (if online sensors
 otherwise lab data).
    *   **Acoustic Emission features**:
        *   Hit counts
 energy levels
 peak amplitudes (good for crack detection
 early wear).
    *   **Usage/Cycle Counters**:
        *   Number of starts/stops
 operating hours
 load cycles.

**Output CSV Content Example (Conceptual - AI generates actuals):**
`1
Vibration RMS (Bearing Housing Z-axis)
Accelerometer
Bearing wear
Spalling
Calculate rolling window RMS
Trend analysis`
`2
Oil Temperature Trend
Thermocouple (Oil Sump)
Lubricant degradation
Overheating
Slope of temperature over time
Threshold alerts`
`3
Spectral Peak at 2x RPM
Accelerometer (Motor Shaft)
Misalignment
Unbalance
FFT analysis
Monitor amplitude growth`
`4
Motor Current Mean
Motor Control Unit
Increased friction
Winding fault
Filter out transients
Compare to baseline`
`5
Acoustic Emission Hit Rate
AE Sensor
Crack initiation
Severe wear
Denoise signal
Detect sudden increases`

**IMPORTANT**: The list should be prioritized
 with the most promising parameters ranked higher. The 'Relevance_To_Failure_Modes' column should specifically link the parameter to failure modes from `{known_failure_modes_list_csv}` if possible. 'Potential_Feature_Engineering_Notes' gives hints for data processing.
							

AI Prompt to Performance Degradation Trend Extrapolation

Analyzes time-series performance data of a mechanical system to identify degradation trends and extrapolate them to predict when a predefined failure threshold might be reached. This prompt helps in prognostic efforts by suggesting a suitable mathematical model for the trend and estimating time to failure. The output is a JSON object containing the model type prediction and confidence.

Output: 

				
					Act as a Reliability Analyst specializing in trend analysis and prognostics.
Your TASK is to analyze a time-series dataset of a `{performance_metric_name_and_units}` (e.g.
 'Vibration_Amplitude_mm_s'
 'Efficiency_Percent'
 'Crack_Length_mm') for a mechanical system.
The data is provided as `{time_series_data_csv}` (CSV string with two columns: 'Timestamp_or_Cycle' and 'Metric_Value').
Your goal is to:
1.  Identify a suitable mathematical model for the degradation trend.
2.  Extrapolate this trend to predict when the metric will reach the `{failure_threshold_value}`.
3.  Provide an estimate of this prediction.

**ANALYSIS AND PREDICTION STEPS:**

1.  **Data Loading and Preparation:**
    *   Parse `{time_series_data_csv}` into time/cycle (X) and metric value (Y). Ensure time is monotonically increasing.
    *   Visualize the data to observe the trend (e.g.
 increasing
 decreasing
 linear
 exponential).

2.  **Trend Model Selection and Fitting:**
    *   Based on the visual trend and common degradation patterns
 select potential models. Suggest AT LEAST TWO plausible models:
        *   **Linear Model**: `Y = aX + b`
        *   **Exponential Model**: `Y = a * exp(bX) + c` or `Y = a * X^b + c` (Power Law). If using log-transform for fitting
 note this.
        *   **Polynomial Model (e.g.
 quadratic)**: `Y = aX^2 + bX + c` (Use with caution
 can be poor for extrapolation if not well-justified).
    *   Fit the selected models to the data using appropriate regression techniques (e.g.
 least squares).

3.  **Model Goodness-of-Fit Assessment:**
    *   For each fitted model
 calculate a goodness-of-fit metric (e.g.
 R-squared
 RMSE).
    *   Select the BEST FITTING model that also makes SENSE from a physical degradation perspective (e.g.
 avoid overly complex models that fit noise).

4.  **Extrapolation and Time-to-Threshold Prediction:**
    *   Using the equation of the best-fitting model
 solve for the 'Timestamp_or_Cycle' (X) when the 'Metric_Value' (Y) equals the `{failure_threshold_value}`.
    *   This predicted X is the estimated time/cycles to reach the threshold.

5.  **Confidence Assessment (Qualitative or Simplified Quantitative):**
    *   Acknowledge the uncertainty in extrapolation. 
    *   Qualitatively state confidence (e.g.
 'High' if data shows a very clear
 stable trend and threshold is not too far;
 'Medium' or 'Low' if data is noisy
 trend is less clear
 or extrapolation is long).
    *   (Optional
 if simple to implement for linear regression): Calculate prediction interval for the threshold crossing point if possible
 or mention factors affecting confidence.

**OUTPUT FORMAT (JSON):**
You MUST return a single JSON object with the following structure:
```json
{
  "input_summary": {
    "metric_name": "`{performance_metric_name_and_units}`"
    "failure_threshold": `{failure_threshold_value}`
    "data_points_analyzed": [Number of data points from CSV]"
  }
  ""trend_analysis"": {
    ""best_fit_model_type"": ""[e.g.
 Linear
 Exponential
 Polynomial_Degree_2]""
    ""model_equation"": ""[Equation of the best fit model
 e.g.
 Y = 0.5*X + 10]""
    ""goodness_of_fit"": {
      ""metric"": ""[e.g.
 R-squared or RMSE]""
      ""value"": "[Calculated value]"
    }
  }
  ""prediction"": {
    ""estimated_time_or_cycles_to_threshold"": "[Calculated X value when Y reaches threshold
 numeric or 'Not Reached if trend does not intersect']""
    ""units_of_time_or_cycles"": ""[Units from 'Timestamp_or_Cycle' column
 e.g.
 Hours
 Cycles
 Days]""
    ""confidence_in_prediction"": ""[High/Medium/Low]""
    ""confidence_statement"": ""[Brief justification for confidence level
 e.g.
 Clear linear trend and data consistency support high confidence
 or Noisy data and long extrapolation reduce confidence.]""
  }
  ""warnings_or_notes"": ""[e.g.
 Extrapolation assumes current degradation pattern continues. Significant operational changes may invalidate prediction. Polynomial models can be unreliable for far extrapolation.]""
}
```

**IMPORTANT**: Ensure calculations are clear. If the trend does not lead to the threshold (e.g.
 metric improving or plateauing below threshold)
 state this appropriately in the prediction. The AI should perform the calculations or outline them clearly if it simulates them."
							

AI Prompt to Fishbone Diagram Inputs for Failure

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: 

				
					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.
							

AI Prompt to 5 Whys Protocol for Process Anomaly

Guides a user through a structured 5 Whys root cause analysis for a manufacturing process anomaly in mechanical engineering. This prompt helps in drilling down to the fundamental cause by iteratively asking why based on the initial problem and process context. The output is a text-based structured questioning pathway.

Output: 

				
					Act as a Quality Engineering Coach facilitating a "5 Whys" Root Cause Analysis.
Your TASK is to guide the user through the 5 Whys methodology to find the potential root cause of the `{initial_problem_statement_text}` within the `{process_name_and_context}`.
You will provide a structured questioning pathway. For each 'Why?'
 you will prompt the user for an answer
 and then formulate the next 'Why?' based on a hypothetical (but plausible for mechanical engineering) user response. The user would then answer your 'Why?' in a real scenario.
Since this is not interactive
 generate a plausible chain of 5 Whys and answers to illustrate the process
 and then provide a template for the user to fill.

**ILLUSTRATIVE 5 WHYS EXAMPLE (AI Generated Plausible Chain):**

**Process & Context**: `{process_name_and_context}` (e.g.
 CNC Machining of aluminum brackets)
**Initial Problem**: `{initial_problem_statement_text}` (e.g.
 High scrap rate (20%) due to oversized holes on Part #XYZ)

**1. Why is there a high scrap rate (20%) due to oversized holes on Part #XYZ?**
    *   *AI's Assumed Plausible User Answer*: The holes are being drilled too large by the CNC machine.

**2. Why are the holes being drilled too large by the CNC machine?**
    *   *AI's Assumed Plausible User Answer*: The drill bit is wobbling excessively during operation.

**3. Why is the drill bit wobbling excessively?**
    *   *AI's Assumed Plausible User Answer*: The drill bit is not securely held in the tool holder collet.

**4. Why is the drill bit not securely held in the tool holder collet?**
    *   *AI's Assumed Plausible User Answer*: The collet is worn beyond its service limit and has lost its clamping force.

**5. Why is the collet worn beyond its service limit and not replaced?**
    *   *AI's Assumed Plausible User Answer*: The preventive maintenance schedule for checking/replacing collets based on usage hours was not being consistently followed due to a lack of clear responsibility assigned for this specific task.
    *   **Potential Root Cause Reached**: Lack of clear responsibility and adherence to preventive maintenance for tooling components.

**TEMPLATE FOR USER TO CONDUCT THEIR OWN 5 WHYS:**
*(Please use this template to conduct your own analysis. For each 'Why?'
 record your team's answer
 then formulate the next 'Why?' based on that answer.)*

**Process & Context**: `{process_name_and_context}`
**Initial Problem Statement**: `{initial_problem_statement_text}`

**1. Why? (Why is/did [`{initial_problem_statement_text}`] occur?)**
    *   **Answer 1**: _________________________________________________________ 

**2. Why? (Why did [Answer 1] occur?)**
    *   **Answer 2**: _________________________________________________________ 

**3. Why? (Why did [Answer 2] occur?)**
    *   **Answer 3**: _________________________________________________________ 

**4. Why? (Why did [Answer 3] occur?)**
    *   **Answer 4**: _________________________________________________________ 

**5. Why? (Why did [Answer 4] occur?)**
    *   **Answer 5**: _________________________________________________________ 
    *   **(Continue if needed - '5' is a guideline
 not a strict limit. Stop when you reach an actionable root cause
 often related to a process
 system
 or policy.)**

**Potential Root Cause(s) Identified**: _________________________________________________________ 

**Recommended Corrective Actions to Address Root Cause(s)**: _________________________________ 

**IMPORTANT**: The key is to avoid jumping to conclusions and to base each 'Why?' on the factual answer to the previous question. The goal is to find systemic causes
 not just to assign blame.
							

AI Prompt to Fault Tree Analysis Top Event Setup

Helps initiate a Fault Tree Analysis (FTA) by defining the top undesired event and suggesting immediate contributing sub-system failures or basic events for a described mechanical system. This prompt provides a starting point for a detailed quantitative or qualitative risk assessment. The output is a markdown formatted tree structure outline.

Output: 

				
					Act as a System Safety Engineer specializing in Fault Tree Analysis (FTA).
Your TASK is to help set up the initial levels of a Fault Tree for the `{system_description_text}`.
The TOP EVENT (the main undesired failure) is: `{undesired_top_event_failure_description}`.
Consider the `{key_subsystems_or_components_list_csv}` (CSV: 'Subsystem_Or_Component_Name
Brief_Function') as potential contributors.
You should propose immediate contributing events (intermediate events or basic events) and the logical gates (AND
 OR) that connect them to the Top Event or to each other at the first couple of levels.

**FAULT TREE ANALYSIS - INITIAL STRUCTURE (MUST be Markdown format):**

**System Under Analysis**: `{system_description_text}`
**Top Undesired Event**: `{undesired_top_event_failure_description}`

**Level 0: Top Event**
```mermaid
graph TD
    TE(`{undesired_top_event_failure_description}`")
```

**Level 1: Immediate Contributing Events / Sub-System Failures**
    *   **Guidance**: Think about the major ways the Top Event could occur. These could be failures of major subsystems listed in `{key_subsystems_or_components_list_csv}` or general failure categories. Determine if these immediate causes need to ALL occur (AND gate) or if ANY ONE of them occurring is sufficient (OR gate) to cause the Top Event.

    **Proposed Structure (Example - AI to generate based on inputs):**
    *   *If the Top Event can be caused by failure of Subsystem A OR Subsystem B OR an External Event:*
    ```mermaid
    graph TD
        TE("`{undesired_top_event_failure_description}`") -->|OR Gate G1| IE1("Failure of [Subsystem A Name from CSV]")
        TE -->|OR Gate G1| IE2("Failure of [Subsystem B Name from CSV]")
        TE -->|OR Gate G1| IE3("Relevant External Event Causing Failure
 e.g.
 Power Loss")
    ```
    *   *If the Top Event occurs only if Component X AND Component Y fail simultaneously:*
    ```mermaid
    graph TD
        TE("`{undesired_top_event_failure_description}`") -->|AND Gate G2| BE1("Failure of [Component X Name from CSV]")
        TE -->|AND Gate G2| BE2("Failure of [Component Y Name from CSV]")
    ```

**Level 2: Further Breakdown of Level 1 Intermediate Events (Illustrative for one branch)**
    *   **Guidance**: Take ONE of the Intermediate Events (IE) from Level 1 and break it down further. Identify how that specific subsystem or intermediate event could fail.
    *   **Example (Continuing from OR Gate G1
 focusing on IE1 'Failure of Subsystem A'):**
        *   *If 'Failure of Subsystem A' can be caused by 'Component A1 Failure' OR 'Component A2 Failure':*
        ```mermaid
        graph TD
            TE("`{undesired_top_event_failure_description}`") -->|OR Gate G1| IE1("Failure of [Subsystem A Name]")
            TE -->|OR Gate G1| IE2("Failure of [Subsystem B Name]")
            TE -->|OR Gate G1| IE3("External Event")
            IE1 -->|OR Gate G1A| BE_A1("Failure of [Component A1 of Subsystem A]")
            IE1 -->|OR Gate G1A| BE_A2("Failure of [Component A2 of Subsystem A]")
        ```
        *   The events BE_A1
 BE_A2 would be ""Basic Events"" if they represent the limit of resolution (e.g.
 a specific part failing
 human error
 software glitch) for this initial setup
 or they could be further developed Intermediate Events.

**Key Considerations for Further Development by User:**
    *   **Basic Events**: These are typically failures of individual components
 human errors
 or external events that require no further decomposition. Their probabilities of occurrence are often estimated from historical data
 handbook data
 or expert judgment.
    *   **Gate Logic**: Carefully determine if contributing events need an AND gate (all must occur) or an OR gate (any one can cause the higher-level event).
    *   **Mutual Exclusivity**: Assume events are independent unless otherwise specified.
    *   **Data Requirements**: For a quantitative FTA
 failure probabilities for all basic events are needed.
    *   **Common Cause Failures**: Consider if a single event could cause multiple basic events to fail simultaneously (this adds complexity beyond this initial setup but is important for full FTA).

**AI's Proposed Initial Breakdown (specific to your inputs):**
    *(The AI should now provide a concrete proposed Mermaid diagram snippet for Level 0 and Level 1
 and one branch of Level 2
 based on the user's specific `{system_description_text}`
 `{undesired_top_event_failure_description}`
 and `{key_subsystems_or_components_list_csv}`. It should make reasonable assumptions about how these subsystems might contribute to the top event
 stating the gate logic clearly.)*
    ```mermaid
    graph TD
        TE("`{undesired_top_event_failure_description}`")
        // AI will populate the connections and Level 1 / Level 2 events here
        // Example: If key_subsystems_or_components_list_csv includes 'Hydraulic_Pump
Provides_Pressure' and 'Control_Valve
Directs_Flow'
        // and top event is 'System_Fails_to_Actuate'
        // TE -->|OR Gate G_Main| Pump_Failure("Hydraulic Pump Fails")
        // TE -->|OR Gate G_Main| Valve_Failure("Control Valve Fails")
        // TE -->|OR Gate G_Main| Electrical_Failure("Control System Electrical Failure")
        // Pump_Failure -->|OR Gate G_Pump| Motor_Fails("Pump Motor Fails (Basic Event)")
        // Pump_Failure -->|OR Gate G_Pump| Pump_Internal_Leak("Pump Internal Leakage (Basic Event)")
    ```

**IMPORTANT**: This prompt generates a STARTING POINT for an FTA. A complete FTA is a detailed and iterative process. The Mermaid syntax is provided to suggest a visual structure; the user would use FTA software or draw this out. The AI's main role here is to structure the initial decomposition logically."
							

AI Prompt to Comparative RCA for Repetitive Failures

Analyzes textual descriptions from multiple incident reports of a repetitive failure in a mechanical system. This prompt aims to identify common patterns potential shared root causes and any differentiating factors across incidents helping to solve persistent issues. The output is a markdown formatted comparative analysis.

Output: 

				
					Act as a Senior Reliability Engineer conducting a Root Cause Analysis (RCA) on REPETITIVE failures.
Your TASK is to analyze the information provided in `{multiple_failure_incident_reports_text}` concerning recurring instances of '`{common_failure_description}`' affecting the `{system_or_component_name}`.
The goal is to identify common patterns
 potential shared root causes
 and any significant differentiating factors or unique conditions across the incidents.
The `{multiple_failure_incident_reports_text}` is a single block of text where each incident report is clearly demarcated (e.g.
 by '---INCIDENT REPORT X START---' and '---INCIDENT REPORT X END---'
 or user ensures separation). Each report may contain details like date
 operator
 specific symptoms
 environmental conditions
 immediate actions taken
 and initial findings.

**COMPARATIVE ROOT CAUSE ANALYSIS REPORT (MUST be Markdown format):**

**1. Overview of Repetitive Failure:**
    *   **System/Component**: `{system_or_component_name}`
    *   **Common Failure Mode**: `{common_failure_description}`
    *   **Number of Incident Reports Analyzed**: [AI to count based on demarcations in `{multiple_failure_incident_reports_text}`]

**2. Data Extraction and Tabulation (Conceptual - AI to perform this internally):**
    *   For each incident report
 extract key information such as:
        *   Incident ID/Date
        *   Specific symptoms observed (beyond the `{common_failure_description}`)
        *   Operating conditions at time of failure (load
 speed
 temperature
 etc.)
        *   Environmental conditions
        *   Maintenance history just prior
        *   Operator actions or comments
        *   Any parts replaced or immediate fixes tried.
    *   *(AI should internally process this information to find patterns. A table won't be in the final output unless it's a summary table
 but the AI's logic should be based on this kind of structured comparison.)*

**3. Identification of Common Patterns and Themes Across Incidents:**
    *   **Symptomology**: Are there consistent preceding symptoms or secondary effects noted across multiple reports before or during the `{common_failure_description}`?
    *   **Operating Conditions**: Do failures tend to occur under specific loads
 speeds
 temperatures
 or during particular phases of operation (startup
 shutdown
 steady-state)?
    *   **Environmental Factors**: Is there a correlation with specific environmental conditions (e.g.
 high humidity
 dusty environment
 specific time of day/year)?
    *   **Maintenance Activities**: Do failures cluster after certain maintenance activities
 or if maintenance is overdue?
    *   **Component Batch/Supplier (if mentioned in reports)**: Is there any indication of issues related to specific batches or suppliers of the `{system_or_component_name}` or its sub-parts?
    *   **Human Factors**: Any patterns related to operator experience
 shift changes
 or specific procedures being followed/not followed?

**4. Identification of Differentiating Factors and Unique Conditions:**
    *   Are there any incidents that stand out as different in terms
 of symptoms
 conditions
 or severity?
    *   What unique factors were present in these outlier incidents?
    *   Could these differences point to multiple root causes or aggravating factors for the `{common_failure_description}`?

**5. Hypothesis Generation for Potential Shared Root Cause(s):**
    Based on the common patterns
 propose 2-3 primary hypotheses for the underlying root cause(s) of the repetitive '`{common_failure_description}`'. For each hypothesis:
    *   **Hypothesis Statement**: (e.g.
 'Material fatigue due to cyclic loading under X condition'
 'Inadequate lubrication leading to premature wear'
 'Sensor malfunction providing incorrect feedback to control system').
    *   **Supporting Evidence from Reports**: Briefly list the common patterns from section 3 that support this hypothesis.

**6. Recommended Next Steps for Investigation / Verification:**
    *   What specific data collection
 tests
 or analyses should be performed to confirm or refute the proposed hypotheses? Examples:
        *   `Detailed metallurgical analysis of failed components from multiple incidents.`
        *   `Targeted inspection of [specific sub-component] across all similar units.`
        *   `Review of design specifications vs. actual operating conditions.`
        *   `Interviews with operators and maintenance staff involved in the incidents.`
        *   `Monitoring specific parameters (e.g.
 vibration
 temperature) that might be precursors.`

**7. Interim Containment or Mitigation Actions (if obvious from analysis):**
    *   Are there any immediate actions that could be taken to potentially reduce the frequency or severity of the failures while the full RCA is ongoing
 based on the patterns identified?

**IMPORTANT**: The analysis should focus on synthesizing information from MULTIPLE reports to find trends that might not be obvious from a single incident. The AI should clearly articulate the logic connecting observed patterns to potential root causes.
							

AI Prompt to DOE Plan Critique for Factorial Experiment

Critiques a proposed Design of Experiments (DOE) plan for a factorial experiment suggesting improvements for factor selection level appropriateness confounding and statistical power. This prompt aids mechanical engineers in optimizing their experimental designs for robustness and efficiency. The output is a markdown formatted critique.

Output: 

				
					Act as a Statistical Consultant specializing in Design of Experiments (DOE) for engineering applications.
Your TASK is to critique the proposed DOE plan for a factorial experiment
based on the following inputs:
    *   `{experimental_objective_text}`: Clear statement of what the experiment aims to achieve (e.g.
 'To determine the main effects and two-factor interactions of cutting speed
 feed rate
 and depth of cut on surface roughness and tool wear in milling 6061 Aluminum.').
    *   `{factors_and_levels_json}`: A JSON string defining factors and their levels (e.g.
 `{"CuttingSpeed_m_min": [100
 150
 200]
 "FeedRate_mm_rev": [0.1
 0.2]
 "DepthOfCut_mm": [0.5
 1.0]}`). The actual JSON will be standard.
    *   `{proposed_experimental_runs_table_csv}`: A CSV string of the proposed experimental runs
 showing combinations of factor levels (e.g.
 'Run
CuttingSpeed
FeedRate
DepthOfCut
...'). If it's a standard design (e.g.
 full factorial
 fractional factorial)
 this might be implied or the user might just state the design type.
    *   `{response_variables_list_csv}`: CSV string listing the output variables to be measured (e.g.
 'SurfaceRoughness_Ra_microns
ToolWear_VB_mm').

**CRITIQUE OF DOE PLAN (MUST be Markdown format):**

**1. Alignment with Objective:**
    *   **Assessment**: Does the selection of factors
 levels
 and responses in `{factors_and_levels_json}` and `{response_variables_list_csv}` directly support achieving the `{experimental_objective_text}`?
    *   **Recommendations**: [e.g.
 'The objective mentions interactions
 ensure the design specified in `{proposed_experimental_runs_table_csv}` allows estimation of these (e.g.
 full factorial or appropriate fractional factorial).' or 'Consider if [Additional Factor] might be relevant to the objective.']

**2. Factor Selection and Levels:**
    *   **Assessment**: Are the factors in `{factors_and_levels_json}` truly independent and controllable? Are the chosen levels appropriate (e.g.
 spanning a reasonable range
 not too close
 not too far apart to cause process instability)? Are there enough levels to detect non-linearity if expected (more than 2 for a factor)?
    *   **Recommendations**: [e.g.
 'For Factor X
 the levels [L1
 L2] are very close; consider widening the range if feasible to better observe its effect.' or 'If quadratic effects are suspected for Factor Y
 three levels would be necessary.']

**3. Experimental Design Choice (based on `{proposed_experimental_runs_table_csv}` or implied design):**
    *   **Assessment**: 
        *   **Type of Design**: (e.g.
 Full factorial
 Fractional factorial
 other). Is it clearly stated or inferable?
        *   **Resolution (for fractional factorials)**: If fractional
 what is its resolution and what interactions are confounded? Is this acceptable given the `{experimental_objective_text}`?
        *   **Number of Runs**: Is it practical? Is it sufficient for the effects being estimated?
        *   **Randomization**: Is randomization of run order planned? (CRITICAL - should be mentioned as essential).
        *   **Replication**: Are replications planned
 especially at center points (if any) or for key runs
 to estimate pure error?
    *   **Recommendations**: [e.g.
 'The proposed fractional factorial design (if identified) confounds main effects with two-factor interactions involving Factor Z. If Factor Z interactions are critical
 a higher resolution design or full factorial is needed.' or 'Strongly recommend randomizing the run order to mitigate effects of lurking variables.' or 'Include 3-5 replications at the center point (if applicable) to check for curvature and get a robust estimate of error.']

**4. Response Variables:**
    *   **Assessment**: Are the `{response_variables_list_csv}` well-defined
 measurable
 and relevant to the objective? Is the measurement system capable (repeatable and reproducible)? (Latter is an assumption
 but good to mention).
    *   **Recommendations**: [e.g.
 'Ensure a consistent measurement protocol for [Response Y].']

**5. Statistical Power and Analysis:**
    *   **Assessment**: While a full power analysis is complex
 comment qualitatively if the design seems underpowered for detecting effects of practical importance
 especially if there are few runs or high expected variability.
    *   **Recommendations**: [e.g.
 'With only N runs
 detecting small but significant interactions might be challenging. Consider if effect sizes are expected to be large.' or 'Plan for ANOVA and regression analysis. Check model assumptions (normality
 constant variance of residuals) post-experiment.']

**Summary of Key Recommendations:**
    *   [List the top 3-4 most critical suggestions for improving the DOE plan.]

**IMPORTANT**: The critique should be constructive and provide actionable advice. If the `{proposed_experimental_runs_table_csv}` is not detailed
 critique based on the likely design implied by factors/levels and objective.
							

AI Prompt to Control Group Suggestion for Material Test

Suggests appropriate control groups and baseline measurements for an experimental study on a new material or surface treatment in a mechanical application ensuring valid comparisons and reliable conclusions. This prompt helps engineers design more robust material testing protocols. The output is a text-based recommendation.

Output: 

				
					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.
							
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    Topics covered: test prompts, validation, user input, data collection, feedback mechanism, interactive testing, survey design, usability testing, software evaluation, experimental design, performance assessment, questionnaire, ISO 9241, ISO 25010, ISO 20282, ISO 13407, and ISO 26362..

    1. Wynter

      Are we assuming AI can always generate the best prompts in mechanical engineering? How are these generated btw?

    2. Giselle

      Is AI going to make human engineers redundant?

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